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How to Write a Market Analysis for a Business Plan

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A lot of preparation goes into starting a business before you can open your doors to the public or launch your online store. One of your first steps should be to write a business plan . A business plan will serve as your roadmap when building your business.

Within your business plan, there’s an important section you should pay careful attention to: your market analysis. Your market analysis helps you understand your target market and how you can thrive within it.

Simply put, your market analysis shows that you’ve done your research. It also contributes to your marketing strategy by defining your target customer and researching their buying habits. Overall, a market analysis will yield invaluable data if you have limited knowledge about your market, the market has fierce competition, and if you require a business loan. In this guide, we'll explore how to conduct your own market analysis.

How to conduct a market analysis: A step-by-step guide

In your market analysis, you can expect to cover the following:

Industry outlook

Target market

Market value

Competition

Barriers to entry

Let’s dive into an in-depth look into each section:

Step 1: Define your objective

Before you begin your market analysis, it’s important to define your objective for writing a market analysis. Are you writing it for internal purposes or for external purposes?

If you were doing a market analysis for internal purposes, you might be brainstorming new products to launch or adjusting your marketing tactics. An example of an external purpose might be that you need a market analysis to get approved for a business loan .

The comprehensiveness of your market analysis will depend on your objective. If you’re preparing for a new product launch, you might focus more heavily on researching the competition. A market analysis for a loan approval would require heavy data and research into market size and growth, share potential, and pricing.

Step 2: Provide an industry outlook

An industry outlook is a general direction of where your industry is heading. Lenders want to know whether you’re targeting a growing industry or declining industry. For example, if you’re looking to sell VCRs in 2020, it’s unlikely that your business will succeed.

Starting your market analysis with an industry outlook offers a preliminary view of the market and what to expect in your market analysis. When writing this section, you'll want to include:

Market size

Are you chasing big markets or are you targeting very niche markets? If you’re targeting a niche market, are there enough customers to support your business and buy your product?

Product life cycle

If you develop a product, what will its life cycle look like? Lenders want an overview of how your product will come into fruition after it’s developed and launched. In this section, you can discuss your product’s:

Research and development

Projected growth

How do you see your company performing over time? Calculating your year-over-year growth will help you and lenders see how your business has grown thus far. Calculating your projected growth shows how your business will fare in future projected market conditions.

Step 3: Determine your target market

This section of your market analysis is dedicated to your potential customer. Who is your ideal target customer? How can you cater your product to serve them specifically?

Don’t make the mistake of wanting to sell your product to everybody. Your target customer should be specific. For example, if you’re selling mittens, you wouldn’t want to market to warmer climates like Hawaii. You should target customers who live in colder regions. The more nuanced your target market is, the more information you’ll have to inform your business and marketing strategy.

With that in mind, your target market section should include the following points:

Demographics

This is where you leave nothing to mystery about your ideal customer. You want to know every aspect of your customer so you can best serve them. Dedicate time to researching the following demographics:

Income level

Create a customer persona

Creating a customer persona can help you better understand your customer. It can be easier to market to a person than data on paper. You can give this persona a name, background, and job. Mold this persona into your target customer.

What are your customer’s pain points? How do these pain points influence how they buy products? What matters most to them? Why do they choose one brand over another?

Research and supporting material

Information without data are just claims. To add credibility to your market analysis, you need to include data. Some methods for collecting data include:

Target group surveys

Focus groups

Reading reviews

Feedback surveys

You can also consult resources online. For example, the U.S. Census Bureau can help you find demographics in calculating your market share. The U.S. Department of Commerce and the U.S. Small Business Administration also offer general data that can help you research your target industry.

Step 4: Calculate market value

You can use either top-down analysis or bottom-up analysis to calculate an estimate of your market value.

A top-down analysis tends to be the easier option of the two. It requires for you to calculate the entire market and then estimate how much of a share you expect your business to get. For example, let’s assume your target market consists of 100,000 people. If you’re optimistic and manage to get 1% of that market, you can expect to make 1,000 sales.

A bottom-up analysis is more data-driven and requires more research. You calculate the individual factors of your business and then estimate how high you can scale them to arrive at a projected market share. Some factors to consider when doing a bottom-up analysis include:

Where products are sold

Who your competition is

The price per unit

How many consumers you expect to reach

The average amount a customer would buy over time

While a bottom-up analysis requires more data than a top-down analysis, you can usually arrive at a more accurate calculation.

Step 5: Get to know your competition

Before you start a business, you need to research the level of competition within your market. Are there certain companies getting the lion’s share of the market? How can you position yourself to stand out from the competition?

There are two types of competitors that you should be aware of: direct competitors and indirect competitors.

Direct competitors are other businesses who sell the same product as you. If you and the company across town both sell apples, you are direct competitors.

An indirect competitor sells a different but similar product to yours. If that company across town sells oranges instead, they are an indirect competitor. Apples and oranges are different but they still target a similar market: people who eat fruits.

Also, here are some questions you want to answer when writing this section of your market analysis:

What are your competitor’s strengths?

What are your competitor’s weaknesses?

How can you cover your competitor’s weaknesses in your own business?

How can you solve the same problems better or differently than your competitors?

How can you leverage technology to better serve your customers?

How big of a threat are your competitors if you open your business?

Step 6: Identify your barriers

Writing a market analysis can help you identify some glaring barriers to starting your business. Researching these barriers will help you avoid any costly legal or business mistakes down the line. Some entry barriers to address in your marketing analysis include:

Technology: How rapid is technology advancing and can it render your product obsolete within the next five years?

Branding: You need to establish your brand identity to stand out in a saturated market.

Cost of entry: Startup costs, like renting a space and hiring employees, are expensive. Also, specialty equipment often comes with hefty price tags. (Consider researching equipment financing to help finance these purchases.)

Location: You need to secure a prime location if you’re opening a physical store.

Competition: A market with fierce competition can be a steep uphill battle (like attempting to go toe-to-toe with Apple or Amazon).

Step 7: Know the regulations

When starting a business, it’s your responsibility to research governmental and state business regulations within your market. Some regulations to keep in mind include (but aren’t limited to):

Employment and labor laws

Advertising

Environmental regulations

If you’re a newer entrepreneur and this is your first business, this part can be daunting so you might want to consult with a business attorney. A legal professional will help you identify the legal requirements specific to your business. You can also check online legal help sites like LegalZoom or Rocket Lawyer.

Tips when writing your market analysis

We wouldn’t be surprised if you feel overwhelmed by the sheer volume of information needed in a market analysis. Keep in mind, though, this research is key to launching a successful business. You don’t want to cut corners, but here are a few tips to help you out when writing your market analysis:

Use visual aids

Nobody likes 30 pages of nothing but text. Using visual aids can break up those text blocks, making your market analysis more visually appealing. When discussing statistics and metrics, charts and graphs will help you better communicate your data.

Include a summary

If you’ve ever read an article from an academic journal, you’ll notice that writers include an abstract that offers the reader a preview.

Use this same tactic when writing your market analysis. It will prime the reader of your market highlights before they dive into the hard data.

Get to the point

It’s better to keep your market analysis concise than to stuff it with fluff and repetition. You’ll want to present your data, analyze it, and then tie it back into how your business can thrive within your target market.

Revisit your market analysis regularly

Markets are always changing and it's important that your business changes with your target market. Revisiting your market analysis ensures that your business operations align with changing market conditions. The best businesses are the ones that can adapt.

Why should you write a market analysis?

Your market analysis helps you look at factors within your market to determine if it’s a good fit for your business model. A market analysis will help you:

1. Learn how to analyze the market need

Markets are always shifting and it’s a good idea to identify current and projected market conditions. These trends will help you understand the size of your market and whether there are paying customers waiting for you. Doing a market analysis helps you confirm that your target market is a lucrative market.

2. Learn about your customers

The best way to serve your customer is to understand them. A market analysis will examine your customer’s buying habits, pain points, and desires. This information will aid you in developing a business that addresses those points.

3. Get approved for a business loan

Starting a business, especially if it’s your first one, requires startup funding. A good first step is to apply for a business loan with your bank or other financial institution.

A thorough market analysis shows that you’re professional, prepared, and worth the investment from lenders. This preparation inspires confidence within the lender that you can build a business and repay the loan.

4. Beat the competition

Your research will offer valuable insight and certain advantages that the competition might not have. For example, thoroughly understanding your customer’s pain points and desires will help you develop a superior product or service than your competitors. If your business is already up and running, an updated market analysis can upgrade your marketing strategy or help you launch a new product.

Final thoughts

There is a saying that the first step to cutting down a tree is to sharpen an axe. In other words, preparation is the key to success. In business, preparation increases the chances that your business will succeed, even in a competitive market.

The market analysis section of your business plan separates the entrepreneurs who have done their homework from those who haven’t. Now that you’ve learned how to write a market analysis, it’s time for you to sharpen your axe and grow a successful business. And keep in mind, if you need help crafting your business plan, you can always turn to business plan software or a free template to help you stay organized.

This article originally appeared on JustBusiness, a subsidiary of NerdWallet.

On a similar note...

How to write a sales forecast for a business plan

Table of Contents

What is a sales forecast?

Why do you need a sales forecast, how do you write a sales forecast, top-down or bottom-up, writing your sales forecast, calculating a sales forecast, how can countingup help manage your forecasting.

Sales forecasts are an important part of your business plan . If done correctly, they can give accurate projections of your business’ cash flow, and let you better prepare for the year ahead. They can also make it easier to find the right investors . While it’s easier for existing businesses with plenty of data, you can still calculate a sales forecast for a new business .

In this guide, we’ll explore:

• How can you manage your forecasting?

A sales forecast is a prediction of your business’ future revenue. In order to be an accurate prediction, the forecast is based on previous sales, current economic trends, and industry performance. Having a sales forecast is a useful tool, because it gives you a better idea of how to manage your business. 

Having a sales forecast is like using the past to have a peek into the future of your company. It might not be 100% accurate, but it can help you plan any future spending, or prevent any cash flow issues from occurring. 

You can also use your sales forecast to monitor your business’ progress. For instance, if your business regularly performs better than your forecast, it could be a sign that your business is continuing to grow. On the other hand, if your actual sales are frequently less than expected, this could be a sign that your business is struggling and needs adjustment. 

It’s important to remember that any projections you make aren’t guaranteed, there can be advantages and disadvantages of financial forecasting . 

Now we’ve run through why having a sales forecast can help you run your business, let’s look at how to write one. 

While there are two types of sales forecasting (top-down and bottom-up), one is a lot more accurate for small businesses than the other. A top-down forecast looks at the market as a whole and attributes a portion of the market to your business. 

A top-down approach may work for large businesses that already own a significant chunk of the market. When forecasting for a small business, it’s easy to overestimate your market share. For example, a 1% market share may not seem like a lot, but a small restaurant owning 1% of the £89.5 billion UK market is extremely unrealistic.

The alternative to top-down is bottom-up. A bottom-up sales forecast starts with existing company data (like customer or product information) and works up to revenue. Since this starts with the company, it’s easier to 

Your sales forecast is ultimately a prediction of your revenue over a set period. It considers the amount you think you’ll sell, and the cost of those sales. We’ve included how to calculate a sales forecast below.

A sales forecast consists of three separate values: revenue, cost of goods sold, and gross profit. For estimating values in the calculations below, it’s best to use any existing business data to be as accurate as possible. 

To calculate your predicted revenue:

• Make a list of your available goods and services
• Note the price of each of your goods and services
• Estimate the expected sales of each good or service
• Multiply the price by the estimated sales to get your estimated revenue
• Add them all together to get your total revenue

For example, if your food truck business sold pizzas at £10 and burgers at £5, you would multiply these values by how much you expected to sell. For calculating a weekly sales forecast, you might estimate selling 60 pizzas and 80 burgers. Your predicted revenue for that week would be £600 for pizzas and £400 for burgers — giving £1,000 total.

In order to figure out how much profit you’ll make, you also need to calculate your costs for those predicted sales. To calculate your predicted costs:

• Figure out how much each good or service will cost per unit
• Multiply each cost by the projected sales

Using the same example as above, assume a single pizza cost £3.50 to make and a burger cost £2. Using the estimated sales, the total cost for your pizzas (3.5 x 60) would be £210, and £160 for your burgers (2 x 80). Combining these two figures gives you a total cost of £370.

The last step is to work out your gross profit , and it’s a relatively simple calculation.

• Subtract the total predicted cost from your total predicted revenue

Continuing with the example above, your revenue (£1,000) minus your costs (£370), leaves you with a projected gross profit of £630 for the week. Using this estimate, you can then plan how much working capital your business should have access to. It’s important to remember that these are only estimates, and your actual values can be higher or lower than your forecast.

If you want your forecasts to be as accurate as possible, you need to refer to all of your business’ financial data. Since collecting and collating this data can be challenging, you may want to use financial management software like the Countingup app. 

When trying to calculate your sales forecasts, having an up-to-date log of your current sales can be hugely beneficial. By combining a business current account with accounting software, Countingup is the only software that provides real-time cash flow tracking. 

The Countingup app also provides business owners with access to automatically generated profit and loss statements. These can prove invaluable when trying to stay aware of all your business’ costs.

Start your three-month free trial today. Find out more here .

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The Last Guide to Sales Forecasting You’ll Ever Need: How-To Guides and Examples

By Kate Eby | January 26, 2020 (updated August 26, 2021)

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Sales forecasts are a critical part of your business planning. In this comprehensive guide, you’ll learn how to do them correctly, including explanations of different forecasting methods, step-by-step tutorials, and advice from experienced finance and sales leaders.

Included on this page, you'll find details on more than 20 sales forecasting techniques , information regarding how to forecast sales for new businesses and products , a step-by-step guide on how to forecast sales , and a free sales forecast template .

What Is Sales Forecasting?

When you produce a sales forecast , you are predicting what your sales or revenue will be in the future. An accurate sales forecast helps your firm make better decisions and is arguably the most important piece of your business plan. 

A sales forecast contrasts with a sales goal . The former is the realistic representation of what you believe will occur, while the latter is what you want to occur. Forecasts are never perfectly accurate, but you should be as objective as possible when creating a sales forecast. Goals, on the other hand, can be based on optimistic or motivational targets.

Because the sales forecast is critical to business planning, many different stakeholders in a company (beyond sales managers and representatives) rely on these estimates, including human resources planners, finance directors, and C-level executives. 

In this article, you’ll learn about different sales forecasting methods with varying levels of sophistication. The most basic method is called naive forecasting , which uses the prior period’s actual sales for the new period’s forecast and does not apply any adjustments for growth or inflation. Naive forecasts are used as comparative figures for more robust methods.

What Is Sales Planning?

A sales plan describes the goals, strategies, target customers, and likely hurdles for your sales effort. The sales plan defines your sales strategy and the method of execution you will use to achieve the numbers in your sales forecast.

Overview of Sales Forecasting Steps

Your sales forecasting model can ultimately become very sophisticated, but to grasp the basics, you should first gain a high-level understanding of what is involved. There are three primary steps to getting started:

• Decide which forecasting method or technique you will use. Also, determine the time period for your forecast. Later in this guide, we will review different methods of forecasting sales, including how to know which is best for your business.  
• Gather the data to plug into your forecast model. The data points will vary by method, but will almost always include your actual past sales and current growth rate.
• Pick a tool to support your forecasting effort. For learning purposes, you can start with pencil and paper, but soon after, you’ll want to take advantage of digital solutions. Common tools include spreadsheets, accounting software, and customer relationship management (CRM) or sales management solutions.

As you get going, remember not to be overly focused on complex formulas. Do regular reality checks to make sure your sales forecasts accord with common sense. Bounce forecasts off sales reps to get realistic feedback, and revise.

You will likely achieve greater accuracy if you build your forecasts based on unit sales wherever possible, because pricing can move independently from unit sales. Use data if you have it.

Benefits and Importance of Sales Forecasting

Sales forecasting helps your business by giving you data to make decisions concerning allocating resources, assigning staff, and managing cash flow and overhead. Using this data reduces your risk and supports your growth. 

Your sales forecast enables you to predict both short and long-term performance and customer demand for your product. In the short term, having a sales forecast makes it easy for you to spot when actual sales are not meeting estimates and gives you an opportunity to make corrections early in the period.

The forecast guides how much you spend on marketing and administration, and the projections generate your sales reps’ objectives. In this way, sales forecasts are an important benchmark for gauging the performance of your sales reps. 

Sales forecasts also lead to better management of inventory levels. With a good idea of how much product you will sell, you can stock enough to meet customer demand without missing any sales and without carrying more than you need. Excess inventory ties up capital and reduces profit margins. 

In the long term, sales forecasts can help you prepare for changes in your business. For example, you might see that within a few years, your company will require more manufacturing capacity to meet growing sales. To expand capacity, you may need to build a new factory, so now you can start planning how you will pay for it. Predictive sales forecasting is a critical part of your presentation if you are seeking equity capital from investors or commercial loans for expansion. 

In short, sales forecasting helps your business avoid surprises, so you aren’t making decisions in a crisis environment. Companies with trustworthy sales forecasts see a 10 percentage point  greater increase in annual revenues compared to counterparts without, according to research from the Aberdeen Group .

What Makes a Good Sales Forecast?

The most important quality for a sales forecast is accuracy. But, the benefits of accuracy must be weighed against the time, effort, and expense of the forecasting technique.

Useful sales forecasts are also easily understood and often include visual elements, such as charts, graphs, and tables, to make important trends visible. 

Ideally, you can quickly build a highly reliable sales forecast with simple, economical methods. The ultimate forecast method would automatically (i.e., without manual intervention) fetch the relevant data and make predictions using an algorithm finely tuned to your business. 

In reality, the forecasting process is more time consuming and subjective. Sales forecasts often depend on reps’ assessments of how likely their prospects are to close, and perceptions vary widely. (A conservative rep’s 60 percent probability may be understated, while another rep’s 60 percent may be overly optimistic.) 

Sales managers, who are usually responsible for forecasting, spend a lot of time factoring in these nuances and other market factors when calculating forecasts. 

Surprisingly, spending more time on forecasting does not always improve accuracy. According to research from CSO Insights, sales managers who spend 15 to 20 percent of their time producing their forecast had win rates for approximately 46.5 percent of deals. But, when they spend more than 20 percent of their time on forecasting, the win rate declined by more than two percentage points. 

An axiom of forecasting is that accuracy is highest during time periods that are close at hand and lowest during those that are far into the future. Short-term forecasts draw upon the following: deals that are already in the sales pipeline, the current economic environment, and actual market trends. So, the data underlying short-term forecasts is more reliable.

Forecasting for distant time periods requires bigger guesses about opportunities, demand, competitor activity, and product trends, so it makes sense that the forecast becomes less accurate the further into the future you go. (This concept applies to many companies, especially those that are young and growing; the concept becomes more relevant for all businesses at three years and beyond.) Bear this thought in mind when you look at your sales forecast in order to make long-term decisions.

Sales Forecasting Methods: Qualitative and Quantitative

Sales forecasting methods break down broadly into qualitative and quantitative techniques. Qualitative forecasts depend on opinions and subjective judgment, while quantitative methods use historical data and statistical modeling.

Qualitative Methods for Sales Forecasting

Sales forecasting often uses five qualitative methods. These are based on different ways of generating informed opinions about sales prospects. Creating and conducting these kinds of surveys is often expensive and time intensive. These five qualitative methods include the following: 

• Jury of Executive Opinion or Panel Method: In this method, an executive group meets, discusses sales predictions, and reaches a consensus. The advantage of this method is that the result represents the collective wisdom of your most informed people. The disadvantage is that the result may be skewed by dominant personalities or the group may spend less time reflecting.
• Delphi Method: Here, you question or survey each expert separately, then analyze and compile the results. The output is then returned to the experts, who can reconsider their responses in light of others’ views and answers. You may repeat this process multiple times to reach a consensus or a narrow range of forecasts. This process avoids the influence of groupthink and may generate a helpful diversity of viewpoints. Unfortunately, it can be time consuming.  
• Sales Force Composite Method: With this technique, you ask sales representatives to forecast sales for their territory or accounts. Sales managers and the head of sales then review these forecasts, along with the product owners. This method progressively refines the views of those closest to the customers and market, but may be distorted by any overly optimistic forecasts by sales reps. The composite method also does not take into account larger trends, such as the political or regulatory climate and product innovation. 
• Customer Surveys: With this approach, you survey your customers (or a representative sample of your customers) about their purchase plans. For mass-market consumer products, you may use market research techniques to get an idea about demand trends for your product.  
• Scenario Planning: Sales forecasters use this technique most often when they face a lot of uncertainty, such as when they are estimating sales for more than three years in the future or when a market or industry is in great flux. Under scenario planning, you brainstorm different circumstances and how they impact sales. For example, these scenarios might include what would happen to your sales if there were a recession or if new duties on your subcomponents increased prices dramatically. The goal of scenario planning is not to arrive at a single accepted forecast, but to give you the opportunity to counter-plan for the worst-case scenarios.

Quantitative Methods for Sales Forecasting

Quantitative sales forecasting methods use data and statistical formulas or models to project future sales. Here are some of the most popular quantitative methods:

• Time Series: This method uses historical data and assumes history will repeat itself, including seasonality or sales cycles. To arrive at future sales, you multiply historical sales by the growth rate. This method requires chronologically ordered data. Popular time-series techniques include moving average, exponential smoothing, ARIMA, and X11. 
• Causal: This method looks at the historical cause and effect between different variables and sales. Causal techniques allow you to factor in multiple influences, while time series models look only at past results. With causal methods, you usually try to take account of all the possible factors that could impact your sales, so the data may include internal sales results, consumer sentiment, macroeconomic trends, third-party surveys, and more. Some popular causal models are linear or multiple regression, econometric, and leading indicators.

Sales Forecasting Techniques with Examples

In reality, most businesses use a combination of qualitative and quantitative methods to produce sales forecasts. Let’s look at the common ways that companies put sales forecasting into action with examples.

Intuitive Method

This forecasting method draws on sales reps’ and sales managers’ opinions about how likely an opportunity is to close, so the technique is highly subjective. Estimates from reps with a lot of experience are likely to be more accurate, and the reliability of the forecast requires reps and managers to be realistic and honest.

This method can be especially helpful if you do not have historical data or if you are assessing  new prospects early in your funnel. In these cases, a rep’s gut feeling after initial contact can be a good indicator. If you are a manager, you will review reps’ estimates with an eye for any outliers and work with those reps to make any necessary adjustments. 

Here is an example of the intuitive method in action: You manage a team of four sales reps. You go to each one and inquire about the leads they are nurturing. You ask each rep which opportunities they believe they will win in the next quarter and how much those sales will be worth. John, your strongest rep, tells you $175,000. Alice, another strong performer, says $115,000. Bob, who is in his second year at your company, reports $85,000. Jennifer, a recent college graduate, projects $100,000. You calculate the total of those forecasts and arrive at an intuitive forecast of $450,000. However, you suspect Jennifer’s forecast is unrealistic, because she is inexperienced, so you ask her more questions. Based on what you learn, you decide that only half of Jennifer’s deals are likely to close, so you reduce her contribution to $50,000 and revise your total quarterly forecast to $400,000.

Scenarios Method

Scenario forecasts are qualitative and involve you projecting sales outcomes based on a variety of assumptions. This process can also be a helpful business planning exercise, because once you identify major risks or uncertainty for your company, you can develop action plans to deal with these circumstances if they arise.

Scenario forecasts require an in-depth knowledge of your business and industry, and the quality of the forecast will vary with the expertise of the person or group who prepares the estimate.

To create a scenario forecast, think about the key factors that affect sales, external forces that could influence the outcome, and major uncertainties. Then, write a narrative and numerical description of how the scenario would play out under various combinations of these key factors, external forces, and uncertainties.

Here is an example of the scenarios method in action: Your company sells components for military vehicles. You notice that the most impactful things your sales reps do are meeting with procurement officers in the defense departments of major nations and holding factory tours and product demonstrations for them. These are your key factors. 

The external forces are the number of tenders or requests for proposals that military procurement departments announce, and the value of those items. The risk of conflict in various parts of the world, scarcity of your raw materials, and trends in budget authorizations for defense by major countries are your critical uncertainties. 

You look at how your key factors, external factors, and major uncertainties might combine. One scenario might entail the outcome if your reps increased the number of meetings and product events by 20 percent, the value of U.S. tenders launched rose by six percent, and France decreased defense spending by two percent. 

Under this scenario, you might forecast a six percent increase in unit sales resulting from the following: 

• Having more in-person sales contacts should boost sales by five percent based on past performance.
• You can increase revenue by three percent due to greater U.S. tender opportunities and your current market share.
• Major customer France will not purchase anything, reducing sales by two percent.

Sales Category Method

The category forecasting method looks at the probability that an opportunity will close and divides opportunities into groups based on this probability. The technique relies somewhat on intuition, as does the intuitive method, but the sales category method brings more structure and discipline to the process.

The categories that each company uses vary widely, but they correspond broadly to stages in the sales pipeline. These are some typical labels and definitions:

• Omitted: The deal has been lost or the prospect is no longer engaging. 
• Pipeline: The opportunity will not realistically close during the quarter.
• Possible, Best Case, Upside, or Longshot: There is a realistic possibility that the deal could close at the projected value in the quarter if everything falls into place, but this is not certain. Overall, fewer than half of the opportunities in this group end up closing in the quarter at the planned value.
• Probable or Forecast: The sales rep is confident that the deal will close at the planned value in the quarter. Most of these opportunities will come to fruition as expected.
• Commit or Confident: The salesperson is highly confident that the deal will close as expected in this quarter, and only something extraordinary and unpredictable could derail it. The probability in this category is 80 to 90 percent. Any deal that does not close as forecast should generally experience only a short, unanticipated delay, rather than a total loss.
• Closed: The deal has been completed; payment and delivery have been processed; and the sale is already counted in the quarter’s revenue. 

To compile your forecast, look at the combined value of the potential deals in the categories under three scenarios:

• Worst Case: This is the minimum value you can anticipate, based on the closed and committed deals. If you have very good historical data for your sales reps and categories and feel confident making adjustments, such as counting a portion of probable deals, you may do so, but it is important to be consistent and objective.
• Most Likely: This scenario is your most realistic forecast and looks at closed, committed, and probable deal values, again with possible adjustments based on historical results. For example, if you have tracked that only 60 percent of your probable deals tend to close in the quarter, adjust their contribution downward by 40 percent.
• Best Case: This is your most optimistic forecast and hinges on executing your sales process perfectly. You count deals in the closed, commit, probable, and possible categories, with adjustments based on past performance. The possible category, in particular, requires a downward adjustment.  

As the quarter or period progresses, you revise the forecast based on updated information. This method can quickly get cumbersome and time consuming without an analytics solution.

Here is an example of the sales category method in action: You interview your sales team and get details from the reps on each deal they are working on. You assign the opportunities to a category, then make adjustments for each scenario based on past results. For example, you see that over the past three years, only half the deals in the possible category each quarter came to fruition. Here’s what the forecast looks like:

Top-Down Sales Forecasting

In top-down sales forecasting, you start by looking at the size of your entire market, called the total addressable market (TAM), and then estimate what percentage of the market you can capture. 

This method requires access to industry and geographic market data, and sales experts say top-down forecasting is vulnerable to unrealistic objectives, because expectations of future market share are often largely conjecture.

Here is an example of top-down sales forecasting in action: You operate a new car dealership in San Diego County, California. From industry and government statistics, you learn that in 2018, 112 dealers sold approximately 36,000 new cars and light trucks in the county. You represent the top-selling brand in the market, you have a large sales force, and your dealership is located in the most populous part of the county. You estimate that you can capture eight percent of the market (2,880 vehicles). The average selling price per vehicle in the county last year was $36,000, so you forecast gross annual sales of $103.7 million. From there, you determine how many vehicles each rep must sell each month to meet that mark.

Bottom-Up Sales Forecasting

Bottom-up sales forecasting works the opposite way, by starting with your individual business and its attributes and then moving outward. This method takes account of your production capacity, the potential sales for specific products, and actual trends in your customer base. Staff throughout your business participates in this kind of forecasting, and it tends to be more realistic and accurate. 

Begin by estimating how many potential customers you could have contact with in the period. This potential quantity of customers is called your share of market (SOM) or your target market . Then, think about how many of those potential customers will interact with you. Then, make an actual purchase.

Of those who do purchase, factor in how many units of your product they will buy on average and then how much revenue that represents. If you aren’t sure how much your customers will spend, you can interview a few. 

Here is an example of bottom-up sales forecasting in action: Your firm sells IT implementation services to mid-sized manufacturers in the Midwest. You have a booth at a regional trade show, and 3,000 potential customers stop by and give you their contact information. You estimate that you can engage 10 percent of those people in a sales call after the trade show and convert 10 percent of those calls into deals. That represents 30 sales. Your service packages cost an average of $250,000. So, you forecast sales of $7.5 million.

Market Build-Up Method

In the market build-up method, based on data about the industry, you estimate how many buyers there are for your product in each market or territory and how much they could potentially purchase. 

Here is an example of the market build-up method in action: Your company makes safety devices for subways and other rail transit systems. You divide the United States into markets and look at how many cities in each region have subways or rail. In the West Coast territory, you count nine. To implement your product, you need a device for each mile of rail track, so you tally how many miles of track each of those cities have. In the West Coast market, there are a total of 454 miles of track. Each device sells for $25,000, so the West Coast market would be worth a total $11.4 million. From there, you would estimate how much of that total you could realistically capture.

Historical Method

The historical sales forecasting technique is a classic example of the time-series forecasting that we discussed under quantitative methods. 

With historical models, you use past sales to forecast the future. To account for growth, inflation, or a drop in demand, you multiply past sales by your average growth rate in order to compile your forecast. 

This method has the advantage of being simple and quick, but it doesn’t account for common variables, such as an increase in the number of products you sell, growth in your sales force, or the hot, new product your competitor has introduced that is drawing away your customers.

Here is an example of the historical method in action: You are forecasting sales for March, and you see that last year your sales for the month were $48,000. Your growth rate runs about eight percent year over year. So, you arrive at a forecast of $51,840 for this March.

Opportunity Stage Method

The opportunity stage technique is popular, especially for high-value enterprise sales that require a lot of nurturing. This method entails looking at deals in your pipeline and multiplying the value of each potential sale by its probability of closing. 

To estimate the probability of closing, you look at your sales funnel and historical conversion rates from top to bottom. The further a deal progresses through the stages in your funnel or pipeline, the higher likelihood it has of closing.

The strong points of this method are that it is straightforward to calculate and easy to do with most CRM systems. 

But, opportunity-stage forecasting can be time consuming. 

Moreover, this method doesn’t account for the unique characteristics of each deal (such as a longtime repeat customer vs. a new prospect). In addition, the deal value, stage, and projected close date have to be accurate and updated. And, the age of the potential deal is not reflected. This method treats a deal progressing quickly through the stages of your pipeline the same as one that has stalled for months. 

If your sales process, products, or marketing have changed, the use of historical data may make this method unreliable.

Here is an example of the opportunity stage method in action: Say your sales pipeline comprises six stages. Based on historical data, you calculate the close probability at each stage. Then, to arrive at a forecast, you look at the potential value of the deals at each stage and multiply them by the probability.

Length-of-Sales-Cycle Method

This is another quantitative method that shares some similarities with the deal stage method. However, this model looks at the length of your average sales cycle. 

First, determine the average length in days of your sales process. This figure is also known as time to purchase or sales velocity . Add the total number of days it took to close all of the past year’s deals and divide by the number of deals. Then, calculate the probability of new deals closing in a certain period of time as a percentage of the average sales cycle length. 

With this method, the biases of individual reps are less of a factor than with the deal stage model. Also, with this technique, you can fine-tune the probabilities for different lead types. (For example, prospects referred by current customers may close in an average of 27 days, while prospects who make contact after an online search need an average of 62 days.) But, this technique requires you to know and record how and when prospects enter your pipeline, which can be time intensive.

Here is an example of the length-of-sales-cycle method in action: You review the 37 deals your company won last year and see that they took a total of 2,997 days to close. To calculate the average length of the sales cycle, you divide 2,997 by 37 and see that the average sales cycle lasted 81 days. You then look at the five deals currently in your pipeline.

Lead Scoring Method

This technique requires you to have lead scoring in place. With lead scoring, you profile your ideal customers based on attributes (like industry, size, and location) as well as behavior (such as whether they have recently raised capital or whether the contact person has requested a demonstration of your product). 

You then classify future leads based on how closely they match your ideal customer. You can label the categories with distinctions such as A, B, or C or hot, warm, or cold, or you can assign numbers up to one hundred using formulas that add and subtract points for different attributes and behaviors. (For example, “They requested a demo, which adds 15 points, but they are not in your ideal industry, which subtracts 10 points.”)  

To create your forecast, you then look at the historical close rate for leads in each category and multiply that by the value of the opportunities currently in the group. 

Here is an example of the lead scoring method in action: Your company sells textbooks for advanced math and science. Your ideal customer is a university with at least 25,0000 students that has an engineering school and is located on the east coast. These are your A prospects. B prospects have at least 10,000 students. C prospects have at least 10,000 students, but are located elsewhere in the country.

You then look at the close rates and potential deal values for each lead score. Finally, you multiply the close rate by the potential value of the deals in the category or by your average sales value.

Lead Source Method

This model forecasts future sales based on how you acquired the lead, using the behavior of previous leads as a benchmark.

For example, say your company sells a software application. Some leads come from search traffic to your website; some originate with demonstration requests at conferences, and some are referrals from existing customers. 

Look at your historical data to track the percentage of leads who converted to sales for each lead source. In addition, calculate the average value of a sale for each source. Then, by using the conversion probability and sales values, you can forecast the sales that the leads at the top of your funnel are likely to generate. 

Here is an example of the lead source method in action: Based on source, you compile your historical data and discover the following conversion rates and sales value for leads.

One advantage of this sales forecasting method is that you can project how many leads of each type you would need to generate in order to hit a target. Suppose you have a conference coming up where participants will be able to request demonstrations of your product, and you would like to win an additional $30,000 in sales from the demo leads. Based on the average lead value of $600, you know you will want to generate 50 leads who request demos at the conference. 

One drawback to lead source forecasting is that the method does not account for potential differences in the length of the sales cycle for the lead types. That makes it difficult to pinpoint the period in which the revenue will occur. Therefore, you should do a separate analysis of time to purchase in order to allocate sales to the right period.

Another challenge is that sometimes you may not be sure of the lead source. For example, suppose that another customer has recommended your product to a contact and that that contact decides to first check you out on your website. You might very well assign a lower lead value to this prospect, assuming they will behave like our web-originated leads, when, in reality, they will probably behave more like the customer referral leads. 

Lastly, remember that this method won’t account for changes in your marketing or pricing that influence conversion rates and customer behavior.

Sales by Row Method

This method is a good fit for small businesses that sell different products or services. Rather than forecasting sales for each individual product type, you project sales for categories. 

Each row in your forecast will cover different physical products (such as pick-up trucks, heavy trucks, and delivery vans) and service units (such as hours of labor or service types like replacing a faucet, unclogging a drain, or installing a toilet). 

You can employ this method to forecast units and then factor them by average prices to arrive at revenue. Or, you can look exclusively at revenue. If you sell a subscription service, you can calculate recurring revenue for each product type.

For each row, you would look at how much you sold in the same period a year earlier and then adjust for factors such as inflation, organic growth, new products, increased workforce, or special circumstances.

Here is an example of the sales by row method: You operate a combination fuel station and mini-market. Your forecast would cover the broad categories of your business, such as sales of gasoline, diesel, food, beverages, and sundries.

For March’s forecast, you take into account that the new housing development near your business, which was under construction last year, is now almost completely sold and that there are many more commuters filling up. Your gas sales have been growing by almost 15 percent year over year. Also, in March, there will be a special event at the nearby fairgrounds that could draw thousands of additional vehicles to your area. 

On the downside, a new retail complex with a full-service grocery store has opened nearby, so your sales of food and drinks have slipped. Also, increased congestion in the neighborhood has caused some long-haul truckers who used to stop for fuel to reroute.

Regression or Multivariable Analysis Method

Regression or multivariable analysis is one of the most sophisticated forecasting methods, and allows you to build a custom model combining any factors that you feel are relevant to your sales.

For regression analysis, you need accurate historical data on all the variables under consideration, expertise in statistics, and, for practical purposes, an analytics solution or application that can perform the analysis. 

Because this method incorporates a multitude of influences on your sales, the resulting forecast is the most accurate. But, the costs tend to be high because of the data collection, expertise, and technology requirements.  

Regression analysis looks at the dependent variable (the factor that you are trying to predict, in this case, the amount of future sales) and independent variables (the factors that you believe affect sales results, such as opportunity stage or lead score). 

In a simple example, you would create a chart, plotting the sales results on the Y axis and the independent variable on the X axis. This chart will reveal correlations. If you draw a line through the middle of the data points, you can calculate the degree to which the independent variable affects sales. 

This line is called the regression line , and, by calculating the slope of the line, you can use numbers to represent the relationship between the variable and sales. The equation for this is Y = a + bX. Excel and other software will perform this analysis and calculate a and b for you. In more sophisticated applications, the formula will also include a factor for error to account for the reality that other variables are also at work.

Going further, you can look at how multiple variables interplay, such as individual rep close rate, customer size, and deal stage. Making these kinds of calculations becomes increasingly difficult with simple charts and demands more advanced math knowledge. 

Remember that correlation is not the same as causation. Bear in mind that while two variables may seem closely related to each other, the reality may be more subtle. 

Here is an example of the regression method in action: You want to look at the relationship between the amount of time a prospect has progressed in your sales cycle and the probability of the deal closing. 

So, plot on a chart the probability of close for past deals when they were at various stages of your sales cycle, which lasts an average of 100 days. Deals early in the sales cycle have a low probability of closing compared to those that occur in the later stages of negotiation and contract signing on day 85 and up. (Be sure to eliminate any prospects that stall or disengage at any stage.)

By drawing a line through those points (i.e., the intersection between the sales close probability and the percentage of the average sales cycle), you can see that there is a nearly one-to-one relationship between percentage point increases in time elapsed relative to the average sales cycle and percentage point increases in the probability of closing.

This calculation becomes more complex when you consider multiple variables. Let’s say you have two sales reps working with prospects. Gloria, your best closer, is giving a product demonstration to a new Fortune 500 account. Leonard, a strong performer, whose close rate is a little lower than Gloria’s, is negotiating with a repeat customer, a mid-sized company. 

Your multivariable analysis of these situations could take into account each rep’s average close rate for an opportunity, given the following factors: the specific stage; deal size; time left in the period; probability of close for a repeat customer versus a new customer; and time to close for an enterprise customer with more than 10 people involved in decision making versus a mid-sized business with a single decision maker.

Time Horizons in Sales Forecasting

Choosing the time period for your sales forecast is an important step. Depending on your business, the purpose of your forecast, and the resources you can devote to making forecasts, the time frame you target will vary. 

A short-term forecast will help set sales rep bonus levels for next quarter, but you need a long-term forecast to decide whether you should plan to build a new factory. A startup that has been doubling revenue every year will have more difficulty making a 20-year forecast than a century-old concern in a mature industry. Here are the three time frames for forecasts: 

• Short-Term Forecasts: These cover up to a year and can include monthly or quarterly forecasts. They help set production levels, sales targets, and overhead costs.
• Medium-Term Forecasts: These range from one to four years and guide product development, workforce planning, and real estate needs.
• Long-Term Forecasts: These extend from five to 20 years and inform capital investment, capacity planning, long-range financing programs, succession planning, and workforce skill and training requirements.

Getting Started with Sales Forecasting: What You Need to Know

Regardless of the sales forecast method you use, you generally need to have certain pieces of information and conditions in place. These include the following:

• Well-Documented and Defined Sales Process: You need to understand your customer journey and have an established sequence for nurturing each prospect. Without this, you cannot predict which opportunities are getting closer to purchasing. This structure creates accountability. 
• Consensus on Pipeline Stages: Your sales team needs to have a clear and shared understanding of what you mean by lead, prospect, qualified, possible, probable, committed, and other relevant terms. 
• Definition of Success: Communicate clearly what your sales team is striving for in terms of sales quotas or goals; include these quotas and goals for each individual rep, for the team as a whole, and for conversion through each stage of your pipeline.
• Historical Data: You require benchmarks for data points, such as average time to close, conversion rates, average deal size, lifetime customer value, win-loss ratio, and seasonal sales trends. These sales metrics and KPIs are often critical pieces of your forecast.
• Current Status: Up-to-date knowledge of your pipeline is essential, including how many opportunities are at each stage and the potential value of these sales.
• Forecasting Tools: This will almost always include a CRM application and may also include financial management or accounting software, analytics solutions, and spreadsheets.

Influences and Assumptions in Sales Forecasting

Sales forecasting should not happen in a vacuum. Take into account changes in the business environment and question assumptions, such as that past growth will continue. Also, be sure to factor in your ideas about global economic trends and competitor behavior.

Here are some common factors to consider regarding your sales forecast. Many of these can have either a positive or negative influence on sales. For example, changing reps’ account assignments may reduce sales, because members of your team will have to familiarize themselves with customers that are new to them. However, sales could increase if your new hotshot gets your biggest opportunity.

• Economic Trends: Inflation, growth, consumer sentiment, risk appetite, and purchasing power
• Regulation: Trade policies such as tariffs, duties, and quotas; health, safety, and environmental rulings on products or processes; court decisions; intellectual property disputes; and competition policy
• Seasonal Trends: Cyclical demand fluctuation, production patterns, and variation in raw material availability 
• Competitor Behavior: New product innovations, pricing changes, and market entries and exits
• Business Economics: Selling prices, direct prices, unit costs, gross margins, and the impact of accrual versus cash accounting on when you can book a sale
• Staffing and Compensation: Hiring or firing new reps, changes in leadership, policies on commissions and bonuses, and training
• Territory Management: Redrawing of territories and changes in account assignments
• Products and Services: Product lifecycle, new products and services, user experience, defects, ticket resolution, changes in distribution, and market entries and exits
• Marketing: Demand generation, advertising, pricing, special campaigns, social media activity, and prospecting

Sales Forecasting for New Businesses and Products

If you are starting a new business or launching a new product, your sales forecasts are crucial because they will determine how much you can spend in order to break even. However, when dealing with a new entity, you lack the advantage of historical data, which you need for almost every forecasting technique. 

If you don’t have historical data, you can use industry benchmarks from trade publications, industry associations, and consultants. For example, if you are launching a new recipe app, look at market research on how other cooking apps have performed. 

Dining establishments can look at number of tables, hours of service, and menu prices to estimate average order amounts and table turnover. Retail outlets use square feet, foot traffic, and average selling prices to forecast sales.

If you are adding a new product to your line, you can forecast sales by looking at how your most similar existing product performed at launch. Then, you can make tweaks based on other relevant information, such as that the new product is harder to master than its predecessor, that it is a later entrant into a crowded space, or that it already has a backlog of orders before launch.

New service businesses can base forecasts on capacity, such as number of staff and service hours and how much to charge for the most popular services. Once you have this data, you can make adjustments accordingly.

Michael Barbarita, President of Next Step CFO , works as a contracted CFO to produce sales forecasts for companies. He likes to tie the sales forecast for service businesses to a metric called sales per direct labor hour , which you can calculate this by dividing sales by the working hours of people in the field performing customer work. For example, an electrical contractor would calculate the sales per direct labor hour of its electricians and multiply that figure by the number of electricians and the hours they work.  

For instance, you may decide that operating at half capacity is a good estimate for your first six months in business. Then, you may operate at three-quarters capacity for the second six months. Therefore, you would multiply maximum capacity by average revenue and then multiply that resulting figure by 0.50 and 0.75, respectively.

Quick-Start: Sales Forecasting Formulas

If you are eager to dive in and want to generate some simple sales forecasts, you can make use of basic equations. Here are a few easy ones:

• Simple Forecast with No Organic Growth: This formula assumes that this period will duplicate the prior period, except for the impact of inflation.  Revenue Prior Period) + (Revenue Prior Period x Inflation Rate) = Sales Forecast  
• Historical Plus Growth: This formula helps you reflect current trends.You look at the prior year and then factor it by your recent growth rate. (Last Year Revenue x Percentage Growth Rate) + Last Year Revenue = Sales Forecast
• Partial Year: In this method, you project the rest of the year based on historical patterns and early results. Imagine that you know your sales for the first two months of the year and that last year these months represented seven and nine percent of your sales respectively and totaled $100,000. Using the formula below, you would forecast sales of $625,00 for the year: ($100,000 x 100) ÷ 16 = $625,000. (Current Period Revenue x 100) ÷ Percent That Equivalent Period Represented Last Year = Forecast Sales
• Pipeline Formula: This formula replicates the opportunity stage method that we discussed earlier. You calculate the value of deals at each stage of your pipeline by multiplying the potential deal value by the close probability and adding up the result for each stage. (Deal Amount x Close Probability) + (Deal Amount x Close Probability) etc. = Sales Forecast

How to Make a Basic Sales Forecast Step by Step

Here are step-by-step instructions for a manually generated sales forecast:

• Pick Your Time Period: The way in which you will use your forecast determines the most appropriate time interval, whether that be monthly, quarterly, annually, or on an even longer timeline. If you are making your first forecast, estimating on a monthly or quarterly basis for the upcoming year is a good starting point. Experts suggest doing monthly estimates for the first year and then doing annual forecasts for years two through five. 
• List Products or Services: Write down the items or services that you sell. If you have a lot of them, group them into categories. For example, if you sell clothing, your rows might include shirts, pants, and shoes. Match these revenue streams to the way you organize your accounting. So, if your books look at women’s and men’s clothing separately, do the same for your sales forecast. That way, you can pair your sales forecast with information on your cost of goods sold and overhead to project profit.  
• Estimate Unit Sales: Predict how many units you will sell in the selected time period. If you have historical data, use that and then factor in assumptions about demand for the upcoming period. For example, is your business growing? Is the economy in recession? Did you launch a big promotion? Use the answers to these questions to make downward or upward adjustments to the historical figure. You can also interview some customers to get insights into their likely purchasing plans. Lastly, don’t forget to factor in seasonal fluctuations. 
• Multiply by the Selling Price: Multiply the unit sales numbers by the average selling price (ASP). Determine the ASP by analyzing historical sales and adjusting for inflation and other factors. To obtain this figure, you also need to consider discounts, free trials, and unsold inventory. 
• Repeat for Each Forecast Period: Go through the same calculation for each category and time interval. As you forecast more distant periods, your estimates are likely to be less accurate, so you may want to make a range of forecasts, such as for best, worst, and average scenarios. As time passes, add the actual values and fine-tune your forecast. For instance, you may see that for the first few months of the year, you underestimated sales by 12 percent. Therefore, you decide to increase your forecasted sales amounts in the upcoming months.

How to Forecast Sales in Excel

Here is a step-by-step guide to building your own sales forecast in Excel:

• Enter Historical Data: Open a worksheet and enter your past date data in the first column. Then, in the second column, enter the corresponding sales values. If possible, make sure you space the dates consistently (e.g., the first day of every month). 
• Create Forecast: In the date column, fill out the next date cell with the future date you are forecasting. Select the corresponding sales value cell and in the function field, type: =(FORECAST( A10, B2:B9, A2:A9)), where A10 is the future date cell, B2 to B9 are the historical sales amounts, and A2 to A9 are the historical dates. Hit enter and the forecast sales amount will appear.
• Repeat: Continue the pattern for your remaining future dates. Remember that the formula uses only known variables, so do not add forecasted amounts to the cell ranges. This function is a linear forecasting method.
• Power Up: If you have Excel 2016, you can use the forecast sheet function, which automates forecasting and adds a chart. To use this function, select both data columns, and, on the data tab, click the forecast sheet. In the create forecast worksheet box, select whether you want a line or bar chart. In the forecast end field, choose an ending date and then click create. Excel will create a new worksheet that contains both historical and forecast sales data as well as a visual representation. 

For a pre-made basic sales forecast, download this template that projects product sales with both units and sales amount.

Basic Sales Forecast Sample Template

Excel | Google Sheets | Smartsheet

For a wide range of pre-built sales forecast templates in a variety of formats, see this comprehensive collection .

How to Choose the Right Sales Forecasting Methodology

Your goal is to build the most reliable forecast possible, with the minimum amount of resources you need to be effective. To choose the method that fits best, consider these seven questions:

• Is the Time Frame Short, Medium, or Long Term? Qualitative methods are a good choice for short-term horizons, but they generally underperform quantitative methods for periods beyond a few months. Similarly, consider where you are in your business or product lifecycle. If you are ramping up or in a high-growth phase, you may be making costly investment decisions, so you need a method with a high degree of accuracy, but also relatively quick production time. When you are in a mature phase of your business, decisions about production and marketing are more routine. 
• How Much Data Do You Have? The less data you have, the more likely you will be to select a qualitative technique. If you have limited data, you will turn toward more simplistic models. A company that has collected a lot of data and has great confidence in its reliability can choose sophisticated quantitative models. 
• How Relevant Will History Be in Predicting the Future?  If your business has undergone big changes, such as launching major new products, experiencing large growth in the sales force, or introducing a different pricing structure, your past results will have less value as a guide to future performance. So, methods that diminish the weight put on historical data and qualitative techniques are a better choice.  
• In Terms of Time and Money, How Much Does It Cost to Produce the Forecast? How Does This Cost Compare to the Value of the Potential Benefits?You will need to make tradeoffs between the time and cost to build your forecast and the potential benefits, such as cost savings. Also, consider the potential cost of error. For example, suppose you are contemplating a high-cost sales-forecasting technique (one that takes a lot of data gathering, the creation of a custom model, and expensive staff and technology to produce). The forecast could allow your company to reduce the amount of inventory it holds. Weigh the value of inventory savings against the forecasting cost. If you reduce inventory and the forecast proves inaccurate, what are the potential costs of lost sales — because you did not stock adequately or because you did not cut back enough?  
• What Degree of Accuracy Do You Need?  Forecast accuracy rises with the cost and complexity of the methodology. Depending on how you will use the forecast, the size of your company, and the variability of your business, you may feel that it’s not cost effective to produce a maximum-accuracy forecast. If you are a giant global company, a fraction of a percentage point error in your sales forecast could represent many millions. So, the bigger the dollar values, the more meaningful every degree of enhanced accuracy becomes.
• How Complex Are the Factors That Will Drive the Forecast?   If your sales dynamic is straightforward — the more sunny days there are, the more beach umbrellas you sell at your beach kiosk — then building a sophisticated, AI-driven forecasting model will be overkill. “It's important not to spend time and energy developing a complex model, when a much simpler one will do the job,” says Nicholas. But when you are facing a subtle and complex interplay of variables, you need a technique that accounts for them. Suppose you have new products, changes in your marketing, and additional sales reps. A sophisticated model would allow you to forecast the net effects and also try out different scenarios in which the variables fluctuated.

Why Accuracy Is Important in Sales Forecasts

According to CSO Insights, 60 percent of forecasted deals do not close and 25 percent of sales managers are unhappy with the accuracy of their forecasts. Inaccuracy in sales forecasts causes problems for businesses and impacts performance. 

People throughout your company depend on your forecasts to make a multitude of decisions — from pay raises to real estate acquisitions. Let’s look at some of the important reasons to strive for accuracy:

• Early Warning: Your sales forecast helps you spot trouble early, like when revenues are not materializing as expected; the forecast also allows you to intervene and problem solve before this underperformance becomes a crisis.
• Decision Making: The forecast gives leaders confidence and a sound basis for deciding how much and where to spend or invest. Production planners, HR, and others will use the forecast.
• Goal Setting: You set achievable targets for sales reps when you have an accurate forecast. Goal setting prevents sales reps from getting discouraged by unrealistic expectations. Following this strategy also ensures that your commission and bonus scale are calibrated appropriately. 
• Customer Satisfaction: When you are prepared for the right level of demand, your company can improve its record of fulfilling orders on time and in full.
• Inventory Management: You will be more likely to have the right level of inventory if your sales forecasts are accurate. Making accurate predictions allows you to better manage your supply chain and order raw materials or parts in a timely fashion. You also gain more control over your pricing if you have the right amount of inventory. When you have to resort to discounting to get rid of excess inventory, your profitability suffers.

How to Improve Sales Forecast Accuracy and More Best Practices from Experts

Producing high-quality forecasts takes organizational commitment and long-term effort, and best practices will help improve accuracy.

”Sales forecasting is both an art and a science. Where companies tend to go wrong is relying too heavily on one or the other. You need a consistent process and reliable data,” says Charlene DeCesare, CEO of sales training and advisory firm Charlene Ignites .

She emphasizes five best practices:

• Ensure that the pipeline feeding the forecast is accurate. You don't need historical data to predict the future when you have a well-defined sales process.
• Everyone must use the CRM, and should enter notes and coding opportunities in a clear, consistent way. 
• Buyer behavior is a much more reliable predictor of future sales than gut feel. Challenge optimism that doesn't align with the applicable stage in the sales cycle or isn't supported by clear, mutually agreed-upon next steps.
• In general, buyer/seller behavior is the leading indicator to rely upon. Too many companies rely on results, which is actually the lagging indicator.
• Sales leadership can have a huge impact. Sales reps must be rewarded for both honesty and accuracy. Sales forecasting must be an individual, team, and company priority. 

Rob Stephens, a CPA whose firm CFO Perspective advises businesses on forecasts, adds: “A big planning mistake is spending too much of your precious time trying to find the one right scenario… Start with a range of reasonable forecasts based on solid fundamentals. For example, you may project from historical growth rates, customer indications of future sales, or projections of market growth. A company with a new product may need to extrapolate from existing products or early indications from potential customers. Use a higher-probability scenario as a beginning base scenario, but identify why the future may deviate from it.”

Common Mistakes and Pitfalls in Sales Forecasts

Sales pros say they see the same sales forecasting errors on a regular basis and that these often relate to letting the discipline of the forecasting process lapse. 

“The most common operational mistakes are basing forecasts on hope rather than evidence, ignoring repeated close date slippage, failing to take into account the historic forecast accuracy (or inaccuracy) of the salesperson concerned, and failing to hold salespeople accountable for the relative accuracy of their forecasts,” notes Bob Apollo, Founder of Inflexion-Point Strategy Partners, a sales training firm.  

“The most common cultural mistake is when sales leaders press salespeople to forecast a target number without any evidence or confidence that it will actually be achieved," he notes.

Evan Lorendo , Director of Revenue Accelerator, which advises service companies on revenue strategies, says he sees companies with monthly recurring revenue (MRR), such as software as a service (SaaS), frequently make mistakes in sales forecasting.

He gives the example of a company with an MRR product that wants to generate $120,000 in revenue a year. How much in new sales do they need each month? “Most of my clients say $10,000/month, but that is wrong. Because a client is paying on a monthly basis, a client that signs up in January is actually paying 12 times during the year. On the flip side, a client signing up in July will make six payments during the year,” he explains. 

That means there are a total of 78 potential payment configurations per year, not 12. The customer who buys in January will make 12 payments, but November’s buyer will make two. (12 + 11 + 10 + 9 + 8 + 7+ 6 + 5 + 4 + 3 + 2 + 1 = 78.)

“If you want to know how much you need to sell in new sales each month to hit that $120,000 goal, the answer is $1,539 ($120,000/78). That actually seems much more manageable, doesn't it? Based on poor forecasting, a miscalculation can turn off good salespeople who can't hit their quota,” he says.

KPIs for Sales Forecasting

As your sales forecasting improves, you reap bigger benefits, such as better planning and higher profits. So, you will want to assess and monitor your forecasting effort by using key performance indicators (KPIs).

Below are the main KPIs for sales forecasting. Some of them draw from statistics concepts, such as standard deviation, and computer applications and statistics guides can help you calculate them.

• Bias or Variance: This KPI tells how much the actual results deviated from the forecast over a given period of time. Calculate bias as an absolute number of dollars or units or as a percent of sales. A positive number means sales exceeded projections and a negative number indicates underperformance. Actual Units - Forecast Units = Bias
• Mean Absolute Deviation (MAD): This metric describes the size of your forecast error in total units or dollars. You calculate how much the actual results deviated from the forecast average, add the deviations, and divide the result by the total number of data points.   
• Mean Absolute Percentage Error (MAPE): This is similar to MAD, but gives the forecast error as a percent of sales volume. 
• Tracking Signal: This is another expression of forecast error and looks at how the error rate varies among forecast values. Normally, you expect all forecast amounts to be wrong by about the same degree. If, from one data point to another, there is a large variation in the error rate, you need to rework your model.  Tracking Signal = Accumulated Forecast Errors ÷ Mean Absolute Deviation
• Forecast Value Added: This metric measures how much better the forecast was than simply using unadjusted historical data. If your forecasting effort got you closer to actual than the so-called naive forecast (i.e., using historical figures as your forecast), you have added positive value. You calculate this metric by comparing the MAPE of your forecast to the naive forecast.
• Linearity: This looks at how sales are paced over the course of the period. As your reps seek to meet quota, you might see a flurry of deals at the end of the quarter. Or, deals might be spread evenly across the time period. The most stable situation is a deal cadence or velocity that is constant. If expressed as a trend line, this stable situation would appear visually as a flat line. This pattern is called highly linear .

Application of Sales Forecasting

Your sales forecast obviously gives you an idea of how much you will sell in the future, but sales forecasting has other important use cases. Here are five ways you can apply your forecast to business questions:

• Sales Planning: As noted earlier, your sales plan encompasses your goals, tactics, and processes for achieving your sales forecast. As part of this plan, your sales forecast helps you decide if you need to hire more sales reps to achieve your forecast and if you need to put more energy and resources into marketing.
• Demand Planning: Demand planning is the process of forecasting how much product your customers will want to buy and making sure inventory aligns with that forecast. In ideal conditions, forecast demand and sales would be virtually the same. But, consider a scenario in which your new product becomes the hot gift of the holiday season. You forecast demand of 100,000 units (the number consumers will want to buy). A large shipment turns out to be defective, and the product is unsellable. So, you forecast sales of just 75,000 units (how much you will actually sell.)   
• Financial Planning: Your sales forecast is vital to the work of your finance department. The finance team will rely on the forecast to build a budget, manage overhead, and figure out long-term capital needs. 
• Operations Planning: The unit-sales numbers in your forecast are also important for operations planners. They will look at the production required to meet those sales and confirm that manufacturing capacity can accommodate them. They will want to know when sales are likely to rise or fall, so they can avoid excess inventory. A big increase in sales will also require operations managers to make changes in warehousing and distribution. Retailers may change the product mix at individual stores based on your sales forecast.
• Product Planning: The trends you foresee in sales will have big implications for product managers too. They will look at products that you forecast as top sellers for ideas about new products or product modifications they should introduce. A forecast of declining sales may signal it is time to discontinue or revamp a product.

Levels of Maturity in Sales Forecasting

Sales forecasts can be simply scribbled-down estimates, or they can be statistical masterpieces produced with the aid of the most sophisticated technology. The style you pursue relates in large part to your level of forecasting maturity (as well as the size and history of your business). 

Below is a description of the four levels of the sales forecasting maturity model:

• Level One: In the beginning stages of sales forecasting, the estimates are usually not very accurate and take a lot of time to produce. The forecasting process depends on reps’ best guesses, and sales managers spend a lot of time gathering these guesses by interviewing each rep. Then, they roll them up into a consolidated forecast. Inconsistent data collection and personal bias can skew the results. Sales managers use spreadsheets, which quickly become outdated, and the forecasts often reflect little more than intuition.
• Level Two: As your forecasting culture grows, you are probably still inputting data by hand, and the forecast is often inaccurate or outdated. But, a CRM solution is enabling your team to have a shared repository for contacts, sales activity, and deal status. Reps don’t see value in spending time contributing to the forecast, and quality is weak. Your CRM automatically aggregates those results, so you can start to examine trends and anomalies. But, your system is not very flexible, and forecasting remains unwieldy and resource intensive.
• Level Three: At this point, automation starts to offer radical improvements in sales forecasting. Solutions backed by artificial intelligence automatically bring together data from a multitude of sources, including email, CRM, marketing platforms, chat logs, and calendars. There is no more manual data entry, and sales managers gain increased visibility into the sales pipeline. KPIs become reliable and an important tool for monitoring performance.
• Level Four: Technology ensures sales that data is accurate and timely. AI and machine learning find patterns and correlations in your historical data, and predictive analytics offer robust forecasting. The forecasting model is continually refined. Forecast accuracy rises, and sales managers can focus more of their time on supporting reps and developing opportunities. These tools make it apparent when reps are sandbagging or being too optimistic, and accountability increases.

Advances in Sales Forecasting Methodologies

While sales forecasting has been around as long as private enterprise, the field continues to evolve, and researchers are looking at ways to improve sales forecasting methodologies. 

Indiana University Professor Douglas J. Dalrymple performed an influential study in 1987 that surveyed how businesses prepared sales forecasts. He found that qualitative and naive techniques predominated, but that early adopters were reducing errors by using computer analysis. At this time, PCs were starting to proliferate and come down in price. 

By 2008, Zhan-Li Sun and his researchers at the Institute of Textiles and Clothing at Hong Kong Polytechnic University were experimenting with an advanced AI-driven technique called extreme learning machine to see if they could improve forecasts for the volatile retail fashion industry by quantifying the influence of factors such as design on sales.  

Scholars F.L. Chen and T.Y. Ou at the National Tsing Hua University in Taiwan took this further with a 2011 study. The study documented sales forecasting advances when combining extreme learning-machine, so-called Taguchi statistical methods for manufacturing quality with novel analysis theories that work on variables with imperfect information.

Features to Look for in a Sales Forecasting Tool

Paper forecasts and Excel spreadsheets quickly become cumbersome. Sales forecasting capability is available in CRM software, sales analytics and automation platforms, and AI-driven sales technology. These capabilities often overlap among these applications.

Here are some of the features to look for when evaluating a sales forecasting tool:

• Integrations with other software, such as ERP, CRM, marketing suites, contact management, calendars, and more
• Automated collection of data and sales rep activity
• Real-time reporting
• Robust data security
• Analytics and automated scoring of deals
• Insights on most promising deals
• Scenario modeling
• Lead scoring
• Automated forecast roll-ups or summaries by category and team
• Dashboards and graphic displays of KPIs
• Benchmarking
• Customizable forecasting algorithms
• Forecast auditing and error analysis

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• Sales forecasting: How to create a sale ...

Sales forecasting: How to create a sales forecast template (with examples)

A strong sales team is the key to success for most companies. They say a good salesperson can sell sand at the beach, but whether you’re selling products in the Caribbean or Antarctica, it all comes down to strategy. When you’re unsure if your current strategy is working, a sales forecast can help.

What is a sales forecast?

A sales forecast predicts future sales revenue using past business data. Your sales forecast can predict a number of different things, including the number of new sales for an existing product, the new customers you’ll gain, or the memberships you’ll sell in a given time period. These forecasts are then used during project planning to determine how much you should allocate towards new products and services. 

Why is sales forecasting important?

Sales forecasting helps you keep a finger on your business’s pulse. It sets the ground rules for a variety of business operations, including your sales strategy and project planning. Once you calculate your sales projections, you can use the results to assess your business health, predict cash flow, and adjust your plans accordingly.

An effective sales forecasting plan:

Predicts demand: When you have an idea of how many units you may sell, you can get a head start on production.

Helps you make smart investments: If you have future goals of expanding your business with new locations or products, knowing when you’ll have the income to do so is important. 

Contributes to goal setting: Your sales forecast can help you set goals outside of investments as well, like outshining competitors or hiring new team members.

Guides spending: Your sales forecast may be the wake-up call you need to set a budget and use cost control to reduce expenses.

Improves the sales process: You can change your current sales process based on the sales projections you’re unhappy with.

Highlights financial problems: Your sales forecast template will open your eyes to problem areas you may not have noticed otherwise. 

Helps with resource management: Do you have the resources you need to fill orders if it’s an accurate sales forecast? Your sales forecast can guide how you allocate and manage resources to hit targets.

When you have an accurate prediction of your future sales, you can use your projections to adjust your current sales process.

Sales forecasting methods

Sales forecasting is an important part of strategic business planning because it enables sales managers and teams to predict future sales and make informed decisions. But why are there multiple sales forecasting methods? Simply put, businesses vary in size, industry, and market dynamics, so no single methodology suits all.

Choosing the right sales forecasting method is more of an art than a science. It involves:

Analyzing your business size and industry

Assessing the available data and tools

Understanding your sales cycle's complexity

A few telltale signs that you've picked the correct approach include:

Improved accuracy in sales target predictions

Enhanced understanding of market trends

Better alignment with your business goals

Opportunity stage forecasting

Opportunity stage forecasting is a dynamic approach ideal for businesses using CRM systems like Salesforce. It assesses the likelihood of sales closing based on the stages of the sales pipeline. This method is particularly beneficial for sales organizations with a clearly defined sales process.

For example, a software company might use this method to forecast sales by examining the number of prospects in each stage of their funnel, from initial contact to final negotiation.

Pipeline forecasting method

The pipeline forecasting method is similar to opportunity stage forecasting but focuses more on the volume and quality of leads at each pipeline stage. It's particularly useful for businesses that rely heavily on sales forecasting tools and dashboards for decision-making.

A real estate agency could use it by examining the number of properties listed, the stage of negotiations, and the number of closings forecasted in the pipeline.

Length of sales cycle forecasting

Small businesses often prefer the length of sales cycle forecasting. It's straightforward and involves analyzing the duration of past sales cycles to predict future ones. This method is effective for businesses with consistent sales cycle lengths.

A furniture manufacturer, for instance, might use this method by analyzing the average time taken from initial customer contact to closing a sale in the past year.

Intuitive forecasting

Intuitive forecasting relies on the expertise and intuition of sales managers and their teams. It's less about spreadsheets and more about market research and understanding customer behavior. This method is often used with other, more data-driven approaches.

A boutique fashion store, for example, might use this method, relying on the owner's deep understanding of fashion trends and customer preferences.

Historical forecasting

Historical forecasting uses past performance data to predict future sales. This method is advantageous for businesses with ample historical sales data. It's less effective for new markets or rapidly changing industries.

An established book retailer could use historical data from previous years, considering seasonal trends and past marketing campaigns, to forecast next quarter's sales.

Multivariable analysis forecasting

Multivariable analysis forecasting is a more sophisticated method that's ideal for larger sales organizations. It analyzes factors like market trends, economic conditions, and marketing efforts to provide a holistic view of potential sales outcomes.

An automotive company, for example, could analyze factors like economic conditions, competitor activity, and past sales data to forecast future car sales.

How to calculate sales forecast

Sales forecasts determine how much you expect to do in sales for a given time frame. For example, let’s say you expect to sell 100 units in Q1 of fiscal year 2024. To calculate sales forecasts, you’ll use past data to predict future trends. 

When you’re first creating a forecast, it’s important to establish benchmarks that determine how much you normally sell of any given product to how many people. Compare historical sales data against sales quotas—i.e., how much you sold vs. how much you expected to sell. This type of analysis can help you set a baseline for what you expect to achieve every week, month, quarter, and so on.

For many companies, this means establishing a formula. The exact inputs will vary based on your products or services, but generally, you can use the following:

Sales forecast = Number of products you expect to sell x The value of each product

For example, if you sell SaaS products, your sales forecast might look something like this: 

SaaS FY24 Sales forecast = Number of expected subscribers x Subscription price

Ultimately, the sales forecasting process is a guess—but it’s an educated one. You’ll use the information you already have to create a data-driven forecasting model. How accurate your forecast is depends on your sales team. The sales team uses facts such as their prospects, current market conditions, and their sales pipeline. But they will also use their experience in the field to decide on final numbers for what they think will sell. Because of this, sales leaders are more likely to have better forecasting accuracy than new members of the sales team.

Sales forecast vs. sales goal

Your sales forecast is based on historical data and current market conditions. While you always hope your sales goals are attainable—and you can use data to estimate what your team is capable of—your goals might not line up directly with your forecast. This can be for a number of reasons, including wanting to create stretch goals that push your sales team beyond what they’ve done in the past or big, pie-in-the-sky goals that boost investor confidence.

How to create a sales forecast

There are different sales forecasting methods, and some are simpler than others. With the steps below, you’ll have a basic understanding of how to create a sales forecast template that you can customize to the method of your choice. 

1. Track your business data

Without details from your past sales, you won’t have anything to base your predictions on. If you don’t have past sales data, you can begin tracking sales now to create a sales forecast in the future. The data you’ll need to track includes:

Number of units sold per month

Revenue of each product by month

Number of units returned or canceled (so you can get an accurate sales calculation)

Other items you can track to make your predictions more accurate include:

Growth percentage

Number of sales representatives

Average sales cycle length

There are different ways to use these data points when forecasting sales. If you want to calculate your sales run rate, which is your projected revenue for the next year, use your revenue from the past month and multiply it by 12. Then, adjust this number based on other relevant data points, like seasonality.

Tip: The best way to track historical data is to use customer relationship management (CRM) software. When you have a CRM strategy in place, you can easily pull data into your sales forecast template and make quick projections.

2. Set your metrics

Before you perform the calculations in your sales forecast template, you need to decide what you’re measuring. The basic questions you should ask are:

What is the product or service you’re selling and forecasting for? Answering this question helps you decide what exactly you’re evaluating. For example, you can investigate future trends for a long-standing product to decide whether it’s worth continuing, or you can predict future sales for a new product. 

How far in the future do you want to make projections? You can decide to make projections for as little as six months or as much as five years in the future. The complexity of your sales forecast is up to you.

How much will you sell each product for, and how do you measure your products? Set your product’s metrics, whether they be units, hours, memberships, or something else. That way, you can calculate revenue on a price-per-unit basis.

How long is your sales cycle? Your sales cycle—also called a sales funnel—is how long it takes for you to make the average sale from beginning to end. Sales cycles are often monthly, quarterly, or yearly. Depending on the product you’re selling, your sales cycle may be unique. Steps in the sales cycle typically include:

Lead generation

Lead qualification

Initial contact

Making an offer

Negotiation

Closing the deal

Tip: You can still project customer growth versus revenue even if your company is in its early phases. If you don’t have enough historical data to use for your sales forecast template, you can use data from a company similar to yours in the market. 

3. Choose a forecasting method

While there are many forecasting methods to choose from, we’ll concentrate on two straightforward approaches to provide a clear understanding of how sales forecasting can be implemented efficiently. The top-down method starts with the total size of the market and works down, while the bottom-up method starts with your business and expands out.

Top-down method: To use the top-down method, start with the total size of the market—or total addressable market (TAM). Then, estimate how much of the market you think your business can capture. For example, if you’re in a large, oversaturated market, you may only capture 3% of the TAM. If the total addressable market is $1 billion, your projected annual sales would be $30 million. 

Bottom-up method: With the bottom-up method, you’ll estimate the total units your company will sell in a sales cycle, then multiply that number by your average cost per unit. You can expand out by adding other variables, like the number of sales reps, department expenses, or website views. The bottom-up forecasting method uses company data to project more specific results. 

You’ll need to choose one method to fill in your sales forecast template, but you can also try both methods to compare results.

Tip: The best forecasting method for you may depend on what type of business you’re running. If your company experiences little fluctuation in revenue, then the top-down forecasting method should work well. The top-down model can also work for new businesses that have little business data to work with. Bottom-up forecasting may be better for seasonal businesses or startups looking to make future budget and staffing decisions.

4. Calculate your sales forecast

You’ve already learned a basic way to calculate revenue using the top-down method. Below, you’ll see another way to estimate your projected sales revenue on an annual scale.

Divide your sales revenue for the year so far by the number of months so far to calculate your average monthly sales rate.

Multiply your average monthly sales rate by the number of months left in the year to calculate your projected sales revenue for the rest of the year.

Add your total sales revenue so far to your projected sales revenue for the rest of the year to calculate your annual sales forecast.

A more generalized way to estimate your future sales revenue for the year is to multiply your total sales revenue from the previous year.

Example: Let’s say your company sells a software application for $300 per unit and you sold 500 units from January to March. Your sales revenue so far is $150,000 ($300 per unit x 500 units sold). You’re three months into the calendar year, so your average monthly sales rate is $50,000 ($150,000 / 3 months). That means your projected sales revenue for the rest of the year is $450,000 ($50,000 x 9 months).

5. Adjust for external factors

A sales forecast predicts future revenue by making assumptions about your growth rate based on past success. But your past success is only one component of your growth rate. There are external factors outside of your control that can affect sales growth—and you should consider them if you want to make accurate projections. 

Some external factors you can adjust your calculations around include:

Inflation rate: Inflation is how much prices increase over a specific time period, and it usually fluctuates based on a country’s overall economic state. You can take your annual sales forecast and factor in inflation rate to ensure you’re not projecting a higher or lower number of sales than the economy will permit.

The competition: Is your market becoming more competitive as time goes on? For example, are you selling software during a tech boom? If so, assess whether your market share will shrink because of rising competition in the coming year(s).

Market changes: The market can shift as people change their behavior. Your audience may spend an average of six hours per day on their phones in one year. In the next year, mental health awareness may cause phone usage to drop. These changes are hard to predict, so you must stay on top of market news.

Industry changes: Industry changes happen when new products and technologies come on the market and make other products obsolete. One instance of this is the invention of AI technology.

Legislation: Although not as common, changes in legislation can affect the way companies sell their products. For example, vaping was a multi-million dollar industry until laws banned the sale of vape products to people under the age of 21. 

Seasonality: Many industries experience seasonality based on how human behavior and human needs change with the seasons. For example, people spend more time inside during the winter, so they may be on their computers more. Retail stores may also experience a jump in sales around Christmas time.

Tip: You can create a comprehensive sales plan to set goals for team members. Aside from revenue targets and training milestones, consider assigning each of these external factors to your team members so they can keep track of essential information. That way, you’ll have your bases covered on anything that may affect future sales growth. 

Sales forecast template

Below you’ll see an example of a software company’s six-month sales forecast template for two products. Product one is a software application, and product two is a software accessory. 

In this sales forecast template, the company used past sales data to fill in each month. They projected their sales would increase by 10% each month because of a 5% increase in inflation and because they gained 5% more of the market. They kept their price per unit the same as the previous year.

Putting both products in the same chart can help the company see that their lower-cost product—the software accessory—brings in more revenue than their higher-cost product. The company can then use this insight to create more low-cost products in the future.

Sales forecast examples

Sales forecasting is not a one-size-fits-all process. It varies significantly across industries and business sizes. Understanding this through practical examples can help businesses identify the most suitable forecasting method for their unique needs.

Sales forecasting example 1: E-commerce

In the e-commerce sector, where trends can shift rapidly, intuitive forecasting is often useful for making quick, informed decisions.

Scenario: An e-commerce retailer specializing in fashion accessories is planning for the upcoming festive season.

Trend analysis phase: The team spends the first week analyzing customer feedback and current fashion trends on social media, using intuitive forecasting to predict which products will be popular.

Inventory planning phase: Based on these insights, the next three weeks are dedicated to selecting and ordering inventory, focusing on products predicted to be in high demand.

Sales monitoring and adjustment: As the holiday season approaches, the team closely monitors early sales data, ready to adjust their inventory and marketing strategies based on real-time sales performance.

This approach allows the e-commerce retailer to stay agile , adapting quickly to market trends and customer preferences.

Sales forecasting example 2: Software development

For a software development company, especially one working with B2B clients, opportunity stage forecasting can help predict sales and manage the sales pipeline effectively.

Scenario: A software development company is launching a new project management tool.

Lead generation and qualification phase: In the initial month, the sales team focuses on generating leads, qualifying them, and categorizing potential clients based on their progress through the sales pipeline.

Proposal and negotiation phase: For the next two months, the team works on creating tailored proposals for high-potential leads and enters negotiation stages, using opportunity stage forecasting to predict the likelihood of deal closures.

Closure and review: In the final phase, the team aims to close deals, review the accuracy of their initial forecasts, and refine their approach based on the outcomes.

Opportunity stage forecasting enables the software company to efficiently manage its sales pipeline , focusing resources on the most promising leads and improving their chances of successful deal closures.

Pair your sales forecast with a strong sales process

A sales forecast is only one part of the larger sales picture. As your team members acquire leads and close deals, you can track them through the sales pipeline. A solid sales plan is the foundation of future success.  

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How to Create a Sales Forecast

11 min. read

Updated October 27, 2023

Business owners are often afraid to forecast sales. But, you shouldn’t be. Because you can successfully forecast your own business’s sales.

You don’t have to be an MBA or CPA. It’s not about some magic right answer that you don’t know. It’s not about training you don’t have. It doesn’t take spreadsheet modeling (much less econometric modeling) to estimate units and price per unit for future sales. You just have to know your own business. 

Forecasting isn’t about seeing into the future

Sales forecasting is much easier than you think and much more useful than you imagine.

I was a vice president of a market research firm for several years, doing expensive forecasts, and I saw many times that there’s nothing better than the educated guess of somebody who knows the business well. All those sophisticated techniques depend on data from the past — and the past, by itself, isn’t the best predictor of the future. You are.

It’s not about guessing the future correctly. We’re human; we don’t do that well. Instead, it’s about setting down assumptions, expectations, drivers, tracking, and management. It’s about doing your job, not having precognitive powers. 

• Successful forecasting is driven by regular reviews

What really matters is that you review and revise your forecast regularly. Spending should be tied to sales, so the forecast helps you budget and manage. You measure the value of a sales forecast like you do anything in business, by its measurable business results.

That also means you should not back off from forecasting because you have a new product, or new business, without past data. Lay out the sales drivers and interdependencies, to connect the dots, so that as you review plan-versus-actual results every month, you can easily make course corrections.

If you think sales forecasting is hard, try running a business without a forecast. That’s much harder.

Your sales forecast is also the backbone of your business plan . People measure a business and its growth by sales, and your sales forecast sets the standard for  expenses , profits, and growth. The sales forecast is almost always going to be the first set of numbers you’ll track for plan versus actual use, even if you do no other numbers.

If nothing else, just forecast your sales, track plan-versus-actual results, and make corrections — that process alone, just the sales forecast and tracking is in itself already business planning. To get started on building your forecast follow these steps.

And if you run a subscription-based business, we have a guide dedicated to building a sales forecast for that business model.

• Step 1: Set up your lines of sales

Most forecasts show several distinct lines of sales. Ideally, your sales lines match your accounting, but not necessarily in the same level of detail.   

For example, a restaurant ought not to forecast sales for each item on the menu. Instead, it forecasts breakfasts, lunches, dinners, and drinks, summarized. And a bookstore ought not to forecast sales by book, and not even by topic or author, but rather by lines of sales such as hardcover, softcover, magazines, and maybe categories (such as fiction, non-fiction, travel, etc.) if that works.

Always try to set your streams to match your accounting, so you can look at the difference between the forecast and actual sales later. This is excellent for real business planning. It makes the heart of the process, the regular review, and revision, much easier. The point is better management.

For instance, in a bicycle retail store business plan, the owner works with five lines of sales, as shown in the illustration here.  

In this sample case, the revenue includes new bikes, repair, clothing, accessories, and a service contract. The bookkeeping for this retail store tracks sales in those same five categories.

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• Step 2: Forecast line by line

There are many ways to forecast a line of sales.

The method for each row depends on the business model

Among the main methods are:.

• Unit sales : My personal favorite. Sales = units times price. You set an average price and forecast the units. And of course, you can change projected pricing over time. This is my favorite for most businesses because it gives you two factors to act on with course corrections: unit sales, or price.
• Service units : Even though services don’t sell physical units, most sell billable units, such as billable hours for lawyers and accountants, or trips for transportations services, engagements for consultants, and so forth.
• Recurring charges : Subscriptions. For each month or year, it has to forecast new signups, existing monthly charges, and cancellations. Estimates depend on both new signups and cancellations, which is often called “churn.”
• Revenue only : For those who prefer to forecast revenue by the stream as just the money, without the extra information of breaking it into units and prices.

Most sales forecast rows are simple math

For a business plan, I recommend you make your sales forecast a detailed look at the next 12 months and then broadly cover two years after that. Here’s how to approach each method of line-by-line forecasting.

Start with units if you can

For unit sales, start by forecasting units month by month, as shown here below for the new bike’s line of sales in the bicycle shop plan:

I recommend looking at the visual as you forecast the units because most of us can see trends easier when we look at the line, as shown in the illustration, rather than just the numbers. You can also see the numbers in the forecast near the bottom. The first year, fiscal 2021 in this forecast, is the sum of those months.

Estimate price assumptions

With a simple revenue-only assumption, you do one row of units as shown in the above illustration, and you are done. The units are dollars, or whatever other currency you are using in your forecast. In this example, the new bicycle product will be sold for an average of $550.00. 

That’s a simplifying assumption, taking the average price, not the detailed price for each brand or line. Garrett, the shop owner, uses his past results to determine his actual average price for the most recent year. Then he rounds that estimate and adds his own judgment and educated guess on how that will change. 

Multiply price times units

Multiplying units times the revenue per unit generates the sales forecast for this row. So for example the $18,150 shown for October of 2020 is the product of 33 units times $550 each. And the $21,450 shown for the next month is the product of 39 units times $550 each. 

Subscription models are more complicated

Lately, a lot of businesses offer their buyers subscriptions, such as monthly packages, traditional or online newspapers, software, and even streaming services. All of these give a business recurring revenues, which is a big advantage. 

For subscriptions, you normally estimate new subscriptions per month and canceled subscriptions per month, and leave a calculation for the actual subscriptions charged. That’s a more complicated method, which demands more details. 

For that, you can refer to detailed discussions on subscription forecasting in How to Forecast Sales for a Subscription Business .

• But how do you know what numbers to put into your sales forecast?

The math may be simple, yes, but this is predicting the future, and humans don’t do that well. So, don’t try to guess the future accurately for months in advance.

Instead, aim for making clear assumptions and understanding what drives your sales, such as web traffic and conversions, in one example, or the direct sales pipeline and leads, in another. Review results every month, and revise your forecast. Your educated guesses become more accurate over time.

Experience in the field is a huge advantage

In a normal ongoing business, the business owner has ample experience with past sales. They may not know accounting or technical forecasting, but they know their business. They are aware of changes in the market, their own business’s promotions, and other factors that business owners should know. They are comfortable making educated guesses.

If you don’t personally have the experience, try to find information and make guesses based on the experience of an employee,  your mentor , or others you’ve spoken within your field.

Use past results as a guide

Use results from the recent past if your business has them. Start a forecast by putting last year’s numbers into next year’s forecast, and then focus on what might be different this year from next.

Do you have new opportunities that will make sales grow? New marketing activities, promotions? Then increase the forecast. New competition, and new problems? Nobody wants to forecast decreasing sales, but if that’s likely, you need to deal with it by cutting costs or changing your focus.

Look for drivers

To forecast sales for a new restaurant, first, draw a map of tables and chairs and then estimate how many meals per mealtime at capacity, and in the beginning. It’s not a random number; it’s a matter of how many people come in.

To forecast sales for a new mobile app, you might get data from the Apple and Android mobile app stores about average downloads for different apps. A good web search might also reveal some anecdotal evidence, blog posts, and news stories, about the ramp-up of existing apps that were successful.

Get those numbers and think about how your case might be different. Maybe you drive downloads with a website, so you can predict traffic from past experience and then assume a percentage of web visitors who will download the app.

• Estimate direct costs

Direct costs are also called the cost of goods sold (COGS) and per-unit costs. Direct costs are important because they help calculate gross margin, which is used as a basis for comparison in financial benchmarks, and are an instant measure (sales less direct costs) of your underlying profitability.

For example, I know from benchmarks that an average sporting goods store makes a 34 percent gross margin. That means that they spend $66 on average to buy the goods they sell for $100.

Not all businesses have direct costs. Service businesses supposedly don’t have direct costs, so they have a gross margin of 100 percent. That may be true for some professionals like accountants and lawyers, but a lot of services do have direct costs. For example, taxis have gasoline and maintenance. So do airlines.

A normal sales forecast includes units, price per unit, sales, direct cost per unit, and direct costs. The math is simple, with the direct costs per unit related to total direct costs the same way price per unit relates to total sales.

Multiply the units projected for any time period by the unit direct costs, and that gives you total direct costs. And here too, assume this view is just a cut-out, it flows to the right. In this example, Garrett the shop owner projected the direct costs of new bikes based on the assumption of 49 percent of sales.

Given the unit forecast estimate, the calculation of units times direct costs produces the forecast shown in the illustration below for direct costs for that product. So therefore the projected direct costs for new bikes in October is $8,894, which is 49% of the projected sales for that month, $18,150.

• Never forecast in a vacuum

Never think of your sales forecast in a vacuum. It flows from the strategic action plans with their assumptions,  milestones , and metrics. Your marketing milestones affect your sales. Your business offering milestones affect your sales.

When you change milestones—and you will, because all business plans change—you should change your sales forecast to match.

• Timing matters

Your sales are supposed to refer to when the ownership changes hands (for products) or when the service is performed (for services). It isn’t a sale when it’s ordered, or promised, or even when it’s contracted.

With proper  accrual accounting , it is a sale even if it hasn’t been paid for. With so-called cash-based accounting, by the way, it isn’t a sale until it’s paid for. Accrual is better because it gives you a more accurate picture, unless you’re very small and do all your business, both buying and selling, with cash only.

I know that seems simple, but it’s surprising how many people decide to do something different. The penalty for doing things differently is that then you don’t match the standard, and the bankers, analysts, and investors can’t tell what you meant.

This goes for direct costs, too. The direct costs in your monthly  profit and loss statement  are supposed to be just the costs associated with that month’s sales. Please notice how, in the examples above, the direct costs for the sample bicycle store are linked to the actual unit sales.

• Live with your assumptions

Sales forecasting is not about accurately guessing the future. It’s about laying out your assumptions so you can manage changes effectively as sales and direct costs come out different from what you expected. Use this to adjust your sales forecast and improve your business by making course corrections to deal with what is working and what isn’t.

I believe that even if you do nothing else, by the time you use a sales forecast and review plan versus actual results every month, you are already managing with a business plan . You can’t review actual results without looking at what happened, why, and what to do next.

See why 1.2 million entrepreneurs have written their business plans with LivePlan

Tim Berry is the founder and chairman of Palo Alto Software , a co-founder of Borland International, and a recognized expert in business planning. He has an MBA from Stanford and degrees with honors from the University of Oregon and the University of Notre Dame. Today, Tim dedicates most of his time to blogging, teaching and evangelizing for business planning.

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• Forecasting isn’t about seeing into the future

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Market Forecast Definition, Benefits, and Techniques

February 8, 2023 (Updated: May 4, 2023)

The more you know about your industry and your audience, the better your business marketing will be. But the real question is, where do you get the information you need to influence your business strategies? There are multiple types of forecasts and  analyses  you can run to collect the information you need to make informed decisions about your marketing and other areas of your company. Today, we’re looking at the market forecast definition and what its benefits and techniques mean for your marketing and content planning:

What Is a Market Forecast?

What factors should you consider when planning a market forecast, benefits of a market forecast, market forecasting techniques.

A market forecast helps you predict future engagement, characteristics, and trends in your brand’s target market. It’s one of the main parts of market analysis. Forecasting is a mathematical way to estimate future business performance. Most market forecasts use information like sales data and surveys to better understand what happened in a market in the past and the current climate to understand implications for the future. Some specific areas a market forecast covers include:

• Potential customer behaviors through the marketing funnel
• Number of average leads generated within a period
• Rate of leads as they move through the marketing funnel
• Effectiveness of marketing campaigns and channels in new customer acquisition
• Market potential of a product or service
• Future sales and revenue for your company
• Acquisition, retention, and monetization potential of your brand, products, and services

When you run a market forecast, you can collect information about all these features and consolidate them into a market analysis report.

Because a market forecast focuses on multiple areas of your business, there are different factors you can look at to collect the data you want. Some of them include:

Market Size

What is the size of your industry market? Do you belong to a small but dedicated niche? Or do you fit in with a larger, more general group of businesses? Understanding your market size provides context to the other forecast data you collect. All market data is relative to the industry and it’s important to make apples-to-apples comparisons to truly understand where your brand, products, and services fall in the market.

You wouldn’t want to do a comparative market analysis on Walmart vs. Joe’s Corner Deli. They’re not competing within the same market and they might not even target the same customers. Getting an idea of the size of your market and how that affects data collection can help as you progress through the forecasting process.

Market Value

Market value tells you what your products and services are worth in the current industry landscape. Like other factors, market value is relative to industry type and size. While high-end products might cost more money, audience members in those segments may actually buy less. This makes the market value of a high-end item actually lower than one with more repetitive sales and demand for your company.

When you research market value for a forecast, it’s important to look at the value of each of your customer segments. Which group spends the most money? Who returns most often as a repeat customer? This type of data can help you decide who your true target audience is and how you can cater to them to make more sales.

Target Markets

The target markets themselves are your audience segments filled with people to sell your products and services to. Most businesses often have one target market with a bunch of smaller target audiences within it. You can examine the  demographics and psychographics  of each target audience to learn more about who people are and what they think or believe.

Related:   Target Market and Target Audience: What’s the Difference?

Accurate Data

When doing any kind of forecasting and analysis, it’s important to make sure you’re collecting accurate data. This type of data is often raw, meaning it hasn’t undergone any other type of manipulation before your forecasting. And if it has, you know who did the prior analysis and what processes they used to get the results. Accurate data also comes from honest data sources, like your target audience, or correctly calibrated data collection programs.

If you’re not using accurate data in your forecast, you run the risk of skewing your forecast. This can have long-term consequences for your business such as misappropriation of funds or failure to meet your audience’s needs.

Basis in Reality

The results of any kind of forecasting should seem realistic. If you find your projected growth numbers are really high, or you have something like a 99% satisfaction rate with your customers, something is probably off with your data or analysis techniques. Always run a gut check on your forecast results. If something seems too good to be true or “off,” run another test or collect more data. You could also ask for help from another department or an outside consultant to recheck your results.

Preparing a market forecast helps your team get a better picture of your brand’s future within its industry. Doing so lets you make more strategic marketing and sales plans because you’ve rooted them in data and facts. Here are some other benefits of developing a market forecast:

Learn About Future Trends

Part of a market forecast is learning about current and upcoming trends in your industry. To find these trends you often use market and consumer data to predict the unpredictable: human behavior. You can look at how customers interact with your and competitor brands and review shifts in their purchasing habits over time.

For example, if you run an all-season business, maybe you compare customer buying trends in each season. When do they spend the most money? What are they most likely to buy during these times? This kind of analysis helps you predict when demand for products or services increases and how your customers or clients react when they have a need to be met.

Then you can adjust your marketing and sales strategies accordingly. In the case of a seasonal book, you may budget more money for paid advertising at these times to get better reach when people are already looking for your products and services.

Get More Targeted With Your Marketing

The more you know about your industry and your audience, the better you can target your marketing campaigns to the people who want and need what you offer. Market forecasts study user behavior and try to predict which behaviors throughout the marketing and sales funnel lead to the highest conversion rates. When you know how your customers behave and what gets them to take action, you can create more  personalized messaging , among other tactics to entice new clients and keep your old ones.

Understanding how your audience interacts with your content channels or how they move through your website is another factor you can study in your market forecast. Analyzing these activities can help you optimize the user experience on your website to increase conversions and influencer consumer behavior.

Increase Customer Retention

While we often focus on how to gain new customers with content marketing, it actually costs less overall for your brand to focus on customer retention. Doing a market forecast helps you better understand your customer churn rate, or how many customers you’re losing over a period. The forecast can identify behaviors that indicate a churn risk to help you identify clients that need more attention from your brand.

Once you know who might be ready to leave your company for a competitor, you can experiment with marketing campaigns to increase their engagement and boost brand loyalty. For example, you may run an email marketing campaign with this segment where you offer discount coupons or ask clients and customers to fill out a satisfaction survey to get more insight into why these customers want to leave.

Develop Proactive Planning

Image via Unsplash by @sushioutlaw

Doing a market forecast lets you do proactive business planning instead of reactive planning. When you do reactive planning, you’re playing catch-up. Something happened unexpectedly and now your business has to respond. The COVID-10 pandemic, for example, led to a lot of reactive planning for many companies. They had to figure out how to stay in business while navigating a global pandemic.

Proactive planning comes before a major crisis hits. Creating a fire safety plan for your home is a type of proactive planning. So is going to the grocery store and stocking up on essentials before a snowstorm or a hurricane. In business, proactive planning comes from looking at all potential shifts in the economy, politics, technology, and customer behavior. Then you have to decide how to handle those upcoming shifts.

While you may not have to use all your proactive planning like a disaster plan, knowing what’s coming and preparing for it before it arrives can help you stay more focused and stable throughout a change-up than you would if you weren’t prepared.

Get More Precise Budgeting

Part of a market forecast is looking at the different budget allocations for different business activities. Reviewing your sales and revenue forecasting can help you learn how much money you have to spend on certain business activities. It can also help you plan for more staffing and marketing campaigns and initiatives. Understanding your cash flow makes it easier to play for investments in your company’s future and activities that let your brand scale and grow.

Manage Your Inventory Better

When you understand your busy seasons, or what products and services are in demand, you can better manage your inventory or service capacity. Think about it this way, let’s say there are two hardware stores in a town. The owner of one watches the weather every day and the other doesn’t. When a late-season snowstorm is coming to town, which owner is going to be more prepared with the right supplies their customer wants?

The owner who watches the weather forecast will likely have shovels and deicer in stock, while the other owner probably has more gardening supplies than winter gear. The more you know about what your customers want and when they want it, the better you can prepare your inventory. For service-based businesses, this part of the forecast can also help you plan for season hiring and staffing issues that could arise if you have more service requests than you do team members.

Develop More Accurate Salary and Payment Allocation

When you know if the market is on an upswing or downswing, you can better prepare for how business changes affect your staffing. When your market goes into a recession, you may have to deal with salary cuts, decreases in hourly wages, or even layoffs to stay afloat. But in good times, a market forecast can help you predict if you can hire more team members or give raises and bonuses.

Because market forecasts look at a variety of different variables about your business, there are different techniques you can use to find and analyze the information. Each technique gives you different insight and metrics so it’s actually helpful to use multiple methods when developing your market forecast. Here are some of the options you can choose from:

Correlational Analysis

In math, a correlational analysis shows the relationship between two variables. In a market forecast, those two variables are often your clients and customers vs. your products or services. Through a correlational analysis, you can find how certain features of your offerings and the way you market them affect your audience. From a correlational analysis, your team can learn what features and marketing help attract new customers and which ones help with customer retention. You can also learn things that might put off your target audience, based on a competitive correlational analysis.

Predictive Analysis

Predictive analysis lets you analyze specific customer behaviors to see what characteristics certain customers have in common. Then you can  segment those groups  based on characteristics to drill down deeper and learn more about your audience. Predictive analysis can help you understand how certain groups may react to features like product or service pricing,  upselling  strategies, and marketing messaging. When you understand what a group has in common, you can then predict how someone else in the same group may react to your marketing and sales strategies.

Customer Surveying

Surveying customers is one of the best and most effective ways to find out what they’re thinking or feeling about a brand, product, or service. You can carry out customer surveys at any stage f the marketing funnel to understand information like:

• Customer intent
• Preferred price ranges
• Satisfaction with a product or service
• Demographic data about the target audience

You can also carry out your surveys in a variety of ways, including:

• Post-conversion on your website
• Pop-up boxes or scroll ads on your website
• Email marketing
• Social media posts or stories
• Links within content

The more information you collect, the more you understand how your audience perceives your brand and its products or services. Use this data and compare it to what you find from your competitor analysis to see if customers are more satisfied with your brand and offerings than others.

“CopyPress gives us the ability to work with more dealership groups. We are able to provide unique and fresh content for an ever growing customer base. We know that when we need an influx of content to keep our clients ahead of the game in the automotive landscape, CopyPress can handle these requests with ease.”

Kevin Doory

Director of SEO at Auto Revo

Expert Advice

Believe it or not, asking for help from an expert is a true market forecast technique. Experts in your business or industry may include influential higher-ups in your company or external consultants who have worked with many different organizations in the field.

Consulting an expert about the state of your market has several advantages. First, they may have access to more data or information than your sales or marketing team alone. They may also have more experience with analysis frameworks and how to arrange and manipulate data to get more accurate results. Finally, the external experts likely won’t hold any kind of bias toward your business. They won’t just tell you what you want to hear. Instead, they’ll give you the truth about the state of your industry and where your business fits within it.

Sales Team Analysis

Your sales team often knows a lot about what’s going on with your customers and with the market in general because they’re in the thick of it every day. Who’s selling comparable products at the lowest price? what new advancements are hitting the market? What does the audience  really  want? If you’re looking for information about your current products and services and the way your audience reacts to them, a sales team analysis could be the right option.

You can also use the team’s insight to determine how you structure content and marketing materials for the funnel. Your sales team likely knows what draws your audience in and what stage of the funnel they’re in when they get to your company. Knowing these tricks and being able to replicate them could help your future campaigns.

Time Series Techniques

Time series analysis techniques look at sales patterns over specific periods. You can look at the data by month, quarter, or year. Most teams use a times series technique to predict financial figures like revenue or return on investment. You can also use a time series technique to predict scaling and growth.

For example, if you look at all your sales data during the second quarter of the last three years and see growth, there’s a good chance you can predict you’ll see growth at the same time  this  year. You can then use mathematical formulas to calculate an average growth percentage to see just how much to expect sales to go up this year.

Market Forecasting Makes for Better Strategies

The more you know about a market, the better prepared you’ll be to do business within it. Whether you’re trying to amp up your content marketing campaigns, better segment your audience, or stay more competitive in your industry, a market forecast can help. Staying consistent with your forecasts and updating them as new information becomes available helps them stay relevant and keeps your business aware of any surprises that could be on the horizon.

CopyPress writer

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Business Plan Market Analysis - Your Road Map to Success

Welcome to our comprehensive guide on the business plan market analysis section of a business plan. Market Analysis is a key part of any good business plan, which will help you better assess and understand your market. ‍ The business plan market analysis section is the heart and soul of your strategy, impacting everything from marketing to operations to the financial forecast. The market analysis helps you understand your position within the industry, the potential size of your market, the competitive landscape, and most importantly, it assists in identifying your target customers. In this blog post, we'll take you through the essentials of market analysis: what it is, why it's crucial, and the components it comprises.

Table of Contents

Business Plan Market Analysis - What Is It?

• Key Components
• How To Implement

Tips and Best Practices

• Market Analysis Case Study

Wrapping It All Up

Market analysis is a comprehensive examination of the dynamics, trends, and competitive landscape of the business environment within which a company operates. It is a vital component of a business plan as it allows entrepreneurs and business owners to understand their industry and market better, enabling them to make well-informed decisions. The business plan market analysis section has two main benefits. Firstly, it Allows you to Identify key opportunities in the market. By studying the market, a business can identify gaps, trends, or customer needs that aren't currently being met and then plan to cater to them effectively. Secondly, it also allows you to recognise potential threats and competition. By understanding your competitors, their offerings, strategies, strengths, and weaknesses, you can position yourself better against their position in the marketplace. Overall the role of market analysis in a business plan cannot be understated. It serves as the foundation upon which the marketing and sales strategies are built. In the following sections, we will take a deep dive into the key components of market analysis and how to conduct it effectively. 

Remember, the opening of your Executive Summary sets the tone for the entire document. Make it memorable and compelling to encourage the reader to continue exploring.

What Are The Key Components of Market Analysis?

Understanding the key components of a market analysis is crucial to conducting one effectively. Each element contributes a unique insight into your market, providing a comprehensive overview of the environment in which your business will operate. Here are the key components:

• Industry Description and Outlook: This involves describing the industry within which your business will operate. Look to identify the key trends influencing it and the outlook of the future of the industry based on reliable industry forecasts.
• Target Market: It's vital to identify and understand your ideal customers. This involves defining the demographics (age, gender, income, etc.), psychographics (interests, values, behaviours, etc.), and geographic location of the customers your business aims to target. Furthermore, it's important to understand their needs, preferences, and buying habits.
• Market Size and Trends: Here, you need to determine the total size of your target market. This involves quantifying the number of potential customers, the total sales volume, or the total market value. Furthermore, it's crucial to identify key market trends, which may include changes in customer behaviour, new technologies, or shifting regulatory environments.
• Market Segmentation: This involves dividing your target market into distinct groups (segments) based on certain characteristics. These might include age, location, buying habits, or customer needs. By taking the time to segment your customers you can develop better targeting strategies for your marketing campaigns.
• Competitive Analysis: This component involves identifying your key competitors and analysing their products, sales strategies, market share, strengths, and weaknesses. A competitive analysis will help your business identify its unique selling proposition (USP) and differentiate itself from competitors.
• SWOT Analysis: Finally, conducting a SWOT (Strengths, Weaknesses, Opportunities, Threats) analysis will allow your business to identify its internal strengths and weaknesses, as well as external opportunities and threats in the market. This can help your business leverage its strengths, address its weaknesses, capitalise on opportunities, and prepare for potential threats.

Business Plan Market Analysis - How to Implement 

Conducting a business plan market analysis might seem like a daunting task, but you can make it more achievable by breaking it down into key tasks.

• Industry Description and Outlook: Start by gathering data on your industry. This can include industry reports, market research data, news articles, and government statistics. Remember to cite your sources to add credibility to your analysis.
• Target Market: Identifying your target market requires an understanding of who is most likely to buy your product or service. You can conduct surveys, interviews, or focus groups to gather data on potential customers. If you already have a customer database, try to delve into this further by conducting post-purchase interviews with customers. Try to identify demographic, geographic, and psychographic characteristics, as well as buying habits and needs. The aim is to create a clear and specific profile of your ideal customer. You will aim to use this data to generate customer segments to target with your marketing campaigns.
• Market Size and Trends: Estimating market size can be challenging but can be done by looking at industry reports, government data, and market research studies. You can also look at the sales of competitors or analogous products. Identify key market trends by examining changes in customer behaviour, technological advances, and regulatory changes.
• Competitive Analysis: Identify your key competitors and analyse their offerings. Look at their products, pricing, marketing strategies, and market share. Try to understand their strengths and weaknesses. You can gather this information from their websites, customer reviews, and industry reports. Use this analysis to identify opportunities for your business to differentiate itself.
• SWOT Analysis: You can consolidate all of your initial research into a SWOT analysis which will help synthesise your learning and make it easier to develop strategies from your research.

Remember, conducting a market analysis isn't a one-time task. Markets are dynamic, with customer preferences, competition, and external factors continually changing. Your aim should be to continually update your business plan market analysis periodically. Here at Action Planr we have a full guide on how to conduct a SWOT Analysis for more detailed information on the full process.

Successfully conducting a market analysis involves more than just understanding its components and knowing where to find the necessary data. Here are some tips and best practices to help make your market analysis more robust, reliable, and useful for decision-making:

• Using Reliable Data Sources for Market Research: The quality of your analysis is directly tied to the quality of your data. Therefore, it's important to use reliable sources such as government databases, industry reports, reputable market research firms, and academic studies. Be wary of data that doesn't come from reliable sources.
• Understanding the Importance of Both Quantitative and Qualitative Research: Quantitative data, like statistics and numerical facts, provides a solid base for your analysis. But don't underestimate the power of qualitative data—opinions, anecdotes, and experiences—which can provide deeper insight into customer behaviours and preferences.
• Keeping the Analysis Current and Updated: Markets change rapidly. What was true last year—or even last month—may not hold today. Regularly updating your market analysis can help you keep up with changes and adjust your business strategies accordingly.
• Ensuring Your Analysis is Relevant to Your Specific Business Model: The insights you need depend on your business model. A B2B company will need a different kind of analysis than a B2C company. Tailor your market analysis to your specific business needs and objectives.
• Importance of Validating Assumptions: In the course of conducting a market analysis, you'll likely make assumptions. Be sure to validate these assumptions with solid data whenever possible.
• Keep your Audience in Mind: If your business plan is read by investors, they'll be interested in market size, growth opportunities, and competitive landscape. Make sure your market analysis addresses these topics and is presented in a clear, easy-to-understand format.

Business Plan Market Analysis - Case Study

To understand how the principles and processes of market analysis work in a real-world context, let's look at a case study of an innovative tech startup, "Techie Toys." Techie Toys is a company that produces educational toys based on augmented reality technology, targeting children aged 6 to 12. Their goal is to make learning fun and interactive.

• Industry Description and Outlook: Techie Toys reviewed multiple industry reports and found that the educational toy market has seen substantial growth over recent years, and this growth is expected to continue due to increasing focus on interactive learning methods. The integration of technology into educational toys, specifically augmented reality, is a significant trend shaping the industry.
• Target Market: Through surveys and focus groups, Techie Toys identified their target customers as parents of children aged 6 to 12 who value educational development and are comfortable with technology integration in toys. These parents have middle to upper-middle income, are mostly city dwellers, and are willing to invest in their children's education.
• Market Size and Trends: By analysing industry reports and sales of similar products, Techie Toys estimated a sizable target market for their augmented reality educational toys. The trend of "edutainment" was identified as a key market trend, with technology-based educational toys gaining popularity.
• Market Segmentation: Techie Toys segmented their market based on age (6-8, 9-12), type of toy preferred (science, math, language arts), and parents' willingness to spend on educational toys. They plan to tailor their products and marketing strategies according to these segments.
• Competitive Analysis: Techie Toys identified several key competitors offering educational toys but found a gap in those providing augmented reality-based learning. They also discovered that their unique selling proposition – interactive learning through augmented reality – is an aspect where they outshine their competitors.
• SWOT Analysis: Strengths identified included a strong development team, unique product offering, and alignment with market trends. Weaknesses involved a higher price point and lack of brand recognition. Opportunities included a growing market and a trend toward edutainment, while threats were potential competitors and rapid technological change.

By conducting this detailed market analysis, Techie Toys was able to effectively position itself within the market, identify its unique selling proposition, and tailor their product development and marketing strategy to their target audience. This comprehensive understanding of their market greatly contributed to their success.

The business plan market analysis section of a business plan is one of its most critical components. Conducting a detailed and accurate market analysis can be a challenging process, but as we've seen in our guide, the benefits are large. Business is all about planning and conducting an in-depth market analysis process, It will allow your business to navigate its environment with knowledge and foresight. The insight gained can help you identify growth opportunities and provide a strong basis for the development of effective marketing and sales strategies. Keep these insights, steps, and tips in mind as you work on your market analysis and remember markets are continually changing so don't make this a one-and-done exercise. If you are looking for help with other sections of the business plan, please check out our Learning Zone homepage.

Marketing forecasting — definition, components, and best methods

Marketing uses a significant portion of company budgets. According to a Gartner report , it represents an average of 9.5% of company spending. So before you invest all that money, you need to know which campaigns are most likely to be successful.

Marketing forecasting helps predict which campaigns will yield the highest ROI. This post will guide you through the basics.

What is a marketing forecast?

Benefits of marketing forecasting, components of a marketing forecast.

• Methods for marketing forecasting

A marketing forecast is a comprehensive data analysis to predict the potential success of specific marketing efforts. The purpose is to ensure that a company focuses on the proper marketing and advertising activities across channels and spends its time and money wisely.

Stakeholders and executives need to know that marketing resources are well-spent. That's why, according to The CMO Survey , 8.9% of the average marketing budget is spent on marketing analytics — and will likely keep growing. Marketing forecasting, as an analytical tool, has several advantages.

• Better planning. Marketing projections demonstrate where you’ll probably have more success or failure. Predictions of poor performance can inspire innovation and guide you toward better strategies.
• Easier decision-making. When managers have data, there’s less room for debate about which marketing strategies will work best. Decisions are made on facts rather than hunches, so teams can work confidently.
• Better budgeting and scheduling. It’s easier to allocate resources to specific tactics or channels once you’ve made researched-based calculations.
• Healthier risk management. While marketing forecasts are only a strategic estimate, they can help avoid catastrophes — or help take corrective action when necessary. When you’ve done your homework, there are fewer surprises.

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There are three considerations that make a marketing forecast effective — the data, the market size under consideration, and your target audience.

1. Accurate data

Accurate forecasts matter. Overestimating success leads to wasted time and effort and a warehouse full of overstock. Underestimating leaves you unprepared to meet demand. To get a helpful outlook on your marketing campaigns, start with accurate data.

First, know your marketing goals and the time and money you can devote to them. If you can only afford a six-month email campaign, then center your marketing projections around that. Keep your options open but be sure to measure what you can realistically achieve.

Next, gather any general statistics and reports you already have available. Consider:

• Third-party data like Google Trends, government statistics, and industry trends and reports
• Company data like past sales reports, competitive intelligence, and customer feedback If your ecommerce site is equipped with good analytics software, you should be able to gather valuable customer insights to help in your forecasting.

2. Market size

Market size is the number of customers to whom you can potentially sell your product. The total addressable market (TAM) is the total potential revenue for a specific product. To get the TAM, multiply the total number of potential customers by your price.

The key is identifying your real customers and what they’re genuinely willing to spend. Don’t take a top-down approach — looking at the total market size and assuming you can easily capture a small percentage. Instead, take a bottom-up approach by showing how your product can reach a specific audience.

Marketing forecasts also help reveal market potential , which is your room for growth. Larger economic trends can drive people to buy — or not. For example, rising gas prices might make it more likely for people to buy your new mopeds. Before committing to a new opportunity, consider natural volatility and sales cycles so you don’t lean too far into a momentary trend.

3. Target audience

Position your product within your market by segmenting the target audience. Building buyer personas is the best way to do this.

A buyer persona is a general sketch of a specific type of customer. It allows you to synthesize audience data and put a face to it. You also can craft a view of your ideal customer. Drafting fictional buyers for different demographics and verticals is an art form, but once you dial it in, you’ll dramatically improve your marketing projections.

Remember that buyer personas should not be static. They are dynamic profiles you hone in over time. Your target audience is apt to change, so factor in what would make them buy at various times. Look for triggers that prompt customers to act.

Marketing forecast data sources

In addition to the hard data you source from your customer data platform (CDP) or other relationship management software, you can collect key insights to inform your marketing forecast from the people with the most experience with your products.

Executive opinion

Asking leadership what they think about a product’s viability and the possible success of specific strategies is a simple place to start. Chief officers often have the most at stake and are intimately acquainted with past performance and challenges. Executives regularly meet with regional marketing managers and share perspectives on what’s working and what strategies they think might create the greatest impact.

Customer or channel surveys

Create customer surveys to test how the market will react to specific products or messaging. Marketers can survey a particular distribution channel, like customers at a retail or online store, or they might target a particular market segment, like middle-aged American males.

Use those surveys to inform your marketing forecast. But remember, while these surveys accurately depict market interest , they don’t necessarily predict sales .

Sales force composite

Because sales reps pitch and sell the product daily, they can offer helpful estimates about future growth. A sales force composite is a survey of the entire sales team to project sales or marketing results.

Salespeople can sometimes be overly optimistic but their opinion is valuable — especially for short-term forecasts. A sales force composite can show how a product or a marketing strategy will succeed in different regions.

Expert opinions

Expert third-party opinions can provide helpful insights as well. But simply soliciting the opinion of a group of experts doesn’t necessarily lead to accurate or helpful conclusions. It’s best to use this method in tandem with quantitative research.

Methods for marketing forecasts

There are several techniques marketers can use to make projections, including qualitative surveys, historical research and projection, and cause-and-effect analysis. The best approach is to use as many methods as possible and then weigh the results against each other.

Delphi technique

The Delphi technique questions anonymous expert panelists over a series of rounds and averages the final round results. It’s more controllable and more accurate than a traditional expert group interview.

Correlation technique

Studying the correlation between different variables is a more sophisticated marketing forecast method. At its simplest, it traces a market factor against marketing performance, usually with a scatter plot graph. You can draw a correlation where trends move in the same direction.

For example, you might study whether CTA clicks go up over time in relation to an email campaign or how many views your product video gets with the support of a Facebook ad.

The correlation technique gets challenging when you factor in multiple trends simultaneously. And remember that correlation does not equal causation — trends can be helpful, but consider other factors and techniques as well.

Time series technique

The time series forecasting method uses various techniques to look at historical patterns in marketing and apply them to upcoming periods. For example, if the company saw a steady 4% increase in website traffic in the past year, marketing can expect the trend to continue. If you notice rates decelerating or accelerating steadily over time, you can factor that in too.

The challenge is that markets are not always stable. Seasonal and cyclical trends affect numbers, but so do unpredictable fluctuations. Use adjustments to account for volatility. For example, a moving average works with the rate of change for several past periods. Exponential smoothing is a moving average that weighs the last period more heavily.

Response model technique

Response models take advantage of direct customer input. Noting customers’ responses to past marketing campaigns can help predict how they’ll react to future efforts. For example, it can gauge what customers are willing to pay for a product.

You can segment customers into categories — demographics, social networks, or how long they’ve been a customer — and then split test or test multiple strategies on different segments at once.

For example, you might split a target audience into three groups and offer a discount to one segment, a buy-one-get-one-free offer to the second, and nothing to the third. Analyzing the results will help you see which offer makes the most sense for your audience in general.

Remember to keep the variables simple. The more options you add, the more complicated and less accurate your analysis becomes.

Access all your marketing forecasting data in one place

Marketing forecasting can better predict which campaigns meet your audience’s needs and your company’s financial goals to yield the most significant ROI.

Once you’ve identified which metrics and data meet your needs, you will need a platform to collect and analyze them. With all your customer data in one easy-to-use platform, you can use any marketing forecasting method to predict which campaigns will be the most efficient.

Adobe Campaign connects big databases and your broader marketing ecosystem, including point-of-sale systems, ecommerce platforms, and offline programs. It helps you understand your market and who your customers are. You can analyze all that data in one place and make better marketing forecasts that lead to stronger sales.

Watch the demo video to learn more about how Adobe Campaign can help you create accurate marketing forecasts that prove your strategies, win budget, and set your team up for success.

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The Complete Guide to Building a Sales Forecast

Set your company up for predictable revenue growth with the right forecasting processes and tools.

Paul Bookstaber

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Building a sales forecast is both an art and a science. Accurate sales forecasts keep your leaders happy and your business healthy. In this guide, we’ll explain everything you need to know about sales forecasting — so you can get a clear picture of your company’s projected sales and keep everyone’s expectations on track.

We’ve organized this reference guide by the top questions sales teams have about the sales forecasting process, based on our internal conversations and more than 20 years of experience developing  sales solutions .

Build sales forecasts with accuracy

Use the real-time data updates and insights of Sales Cloud to keep your forecasts accurate and your teams on track to hit targets.

What you’ll learn:

What is a sales forecast, why is sales forecasting important, who is responsible for sales forecasts, who uses sales forecasts, what are the objectives of sales forecasting, how do i design a sales forecasting plan.

• What happens to sales forecasting in unpredictable times?

How accurate are sales forecasts?

What tools do you use to forecast sales revenue and how do crm systems forecast revenue, how is forecasting better with crm vs. other methods.

If you’re a sales leader who’s already well-versed in the who and what of sales forecasts, skip to the sections on  designing a sales forecasting plan  and  tools to improve sales forecasts  for more relevant knowledge. Sales forecasting can become especially tough when we face an unexpected turn of events, so head to the section on  what happens to sales forecasts in unpredictable times  for more on that.

A sales forecast is an expression of expected sales revenue. A sales forecast estimates how much your company plans to sell within a certain time period (like quarter or year). The best sales forecasts do this with a high degree of accuracy, and they’re only as accurate as the data that fuels them.

A strong data culture is at the heart of an accurate sales forecast. This means all sales data is available to everyone at the company, and all teams do their part in keeping it updated, leaning on AI and automation to help. More on that in the section on  tools used to forecast sales revenue .

All sales forecasts answer two key questions:

• How much:  Each sales opportunity has its own projected amount it’ll bring into the business. Whether that’s $500 or $5 million, sales teams have to come up with one number representing that new business. To create the number, they take everything they know about the prospect into account.
• When:  Sales forecasts pinpoint a month, quarter, or year when the sales team expects the revenue to hit.

Coming up with those two sales projections is no easy feat. So sales teams factor in the important ingredients of who, what, where, why, and how to make their forecasts:

• Who:  Sales teams are responsible for sales forecasting.
• What:  Forecasts should be based on the exact solutions you plan to sell. In turn, that should be based on problems your prospects have voiced, which  your company can uniquely solve .
• Where:  Where is the buying decision made, and where will the actual products be used? Sales teams see better accuracy when they get closer (at least for a visit) to the center of the action.
• Why:  Why is the prospect or existing customer considering new services from your company in the first place? Is there a compelling event making them consider it now? Without a forcing function and a clear why, the deal may stall inevitably.
• How:  How does this prospect tend to make purchasing decisions? If you’re not accounting for how they do it now and how they’ve done it in the past in your forecast, it may be fuzzy math.

Forecasting lets leaders set realistic sales targets, create attainable and motivating quotas for sales reps, and gauge expected revenue, aiding in budgeting and spending decisions for the whole company. If forecasts are inaccurate, businesses may overspend (putting themselves in a risky spot), and set unreachable quotas (which is demoralizing for reps).

To understand why sales forecasting is so important to business health, think about two example scenarios: one with a car manufacturer and another with an e-commerce shop.

In the case of a car manufacturer, cars take a long time to build. The manufacturer has a complex supply chain to ensure every car part is available exactly when they need to build cars, so the number of cars available to purchase will meet demand.

When you buy something online, whether that’s from a large marketplace or a small boutique, you get a delivery estimate. If your delivery comes a day or a week after it’s promised, that’ll affect your satisfaction with the company — and decrease your willingness to want to do business with them again.

Sales forecasting is similar in both cases. Sales forecasts help the entire business plan resources to ship products, pay for marketing, hire employees, and beyond. Accurate sales forecasting yields a well-oiled machine that meets customer demand, both today and in the future. And internally on sales teams, sales revenue that delivers in its estimated time period keeps leaders and collaborators happy, just like a shipment that arrives on time.

If forecasts are off, the company faces challenges that affect everything from pricing to product delivery to the end user. Meanwhile, if forecasts are on point and  sales quotas  are met, the company can make better investments, perhaps hiring 20 new developers instead of 10, or building a much-needed new sales office in a prime new territory.

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Each organization has its own sales forecast owners. These are some of the teams who are usually responsible:

• Product leaders:  They put a stake in the ground for what products will be available to sell when.
• Sales leaders:  They promise the numbers that their teams will deliver. Depending on the seniority of the leader, how they forecast varies. For example, first-line managers forecast collections of opportunities, where third-line managers consider a wide set of numbers and traditional close rates to come up with an overall forecast.
• Sales reps:  They report their own numbers to their managers.

No matter how a company calculates its sales forecasts, the process should be transparent. And at the end of the day, sales leadership has to be responsible to call a number. Whether met, exceeded, or missed, the forecast responsibility falls on them.

Sales forecasts touch virtually all departments in a business. For example, the finance department uses sales forecasts to decide how to make annual and quarterly investments. Product leaders use them to plan demand for new products. And the HR department uses forecasts to align recruiting needs to where the business is going.

At some level, sales forecasting affects everyone in the company.

The main objective of sales forecasting is to paint an accurate picture of expected sales. Leaders are looking to these numbers when they’re building out their operational roadmap and budget. If they’re confident in the projected growth, they can get to planning.

They could decide to staff more customer service touchpoints, fund more external marketing events, or invest more in the community. They could get ahead of purchasing new equipment or upgrades that get more expensive the longer they wait. Without a sales forecast, leaders are making critical spending decisions in the dark. If sales don’t go as planned, it could lead to cutting workforce, reducing support, or halting product development.

Sales forecasting is a muscle, not an item to check off your to-do list. While you should absolutely design a framework for your sales forecasting plan each year, you should also change up your strategies from time to time so new muscles develop.

Craft a sales forecasting plan with your team by focusing on three primary activities:

• Calculating number and time  period:  Your plan should explain how you’ll calculate the estimated monetary amount and what the timeframes will be. See the section on  how a CRM can help with forecasting  later in this guide for more on the sales forecasting tools you can use to do this.
• Reviewing and revising:  You should also plan to review the forecast at key milestones and revise it if necessary. Most sales leaders track progress against their forecast daily! But you’ll also want to schedule designated check-ins throughout the quarter. Make sure you’re reviewing the latest numbers with  sales automation tools  that sync your CRM’s forecast data.
• Breaking the patterns:  Even the best sales organizations need to shake up their  sales process  once in a while. Breaking your patterns can help you find new ways of crafting even more accurate forecasting. Try skip-level forecasting, ask different questions, have executive sponsorship reviews, and take different angles of the data.

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What happens to sales forecasts in unpredictable times.

Unpredictable events have an enormous impact on your sales forecast. Extreme weather or economic crises all dramatically change your forecast. What you thought you knew about expected revenue growth can be suddenly flipped on its head.

As soon as an extraordinary event hits, sales and finance leaders at your company will quickly want to know:

• How’s our  sales pipeline  looking today?
• What are the best- and worst-case scenarios?
• How has the forecast changed from a week or a month ago?

Your forecast implicates resourcing, headcount, and more (see the section on  sales forecasting objectives ). So although things may be changing quickly, you don’t want to give up on your forecast.

Rather than attempt to recalculate your forecast based on dubious estimates or conjecture, your best bet is to  rely on a CRM solution  to get an accurate view of deal status and pipeline in real time.

During a crisis, reps need to feed their CRM with data as events unfold so leaders have clear visibility into the rapidly evolving pipe. That data enables those leaders to support their reps with corporate-level decisions about where they should be focusing their time — and craft the new forecasts. Your forecast is only as good as the data coming into it from your sales teams.

In uncertain times, quick access to sales data and the ability to pivot  sales territory  and resource deployment accordingly can make the difference between business continuity and dissolution. There’s no silver bullet to forecast perfectly in a crisis or unforeseen scenario. But vigilantly updating what’s in the pipeline and analyzing sales data more frequently than usual will help you see trends and retool your forecast accordingly.

Empathy and care are always fundamental, but this is especially true in these situations. Empathizing with your customers’ challenges and caring for your own sales reps should come before anything else. Build trust with internal and external partners. That trust will help you grow again in the future. Learn more about  maintaining customer relationships as a sales leader .

Only 45% of sales leaders are confident in their organization’s sales forecasts,  according to Gartner . While it’s natural for sales reps to bring in some intuition to their sales forecasts, that’s where room for error can creep in.

This brings us back to embracing a  strong data culture . To get a more accurate forecast, everyone in the sales cycle — from reps to managers to execs — should have a stake in making sure those numbers reflect the latest reality. Reps can keep all prospect info up to date, managers can track pipeline progress, and leaders can review how all teams are tracking toward those forecast numbers, with AI playing backup to spot any inaccuracies or chances to adjust along the way.

A  CRM  gives sales leaders a real-time view into their entire team’s forecast. The tool forecasts revenue by giving you:

• An accurate view of your entire business.  Comprehensive forecasts in a CRM come with a complete view of your pipeline.
• Tracking of your top performers.  See which reps are on track to beat their targets with up-to-the-minute leaderboards.
• Forecasting for complex sales teams.  Overlay splits allows you to credit the right amounts to sales overlays, by revenue, contract value, and more.

A forecast is based on the gross rollup of a set of opportunities. You can think of a forecast as a rollup of currency or quantity against a set of dimensions: owner, time, forecast categories, product family, and territory. You can collaborate on forecasts with all the necessary people to see how opportunities are stacking up. Drill down into opportunities by sales leader, operating unit, manager, and individuals.

We also love a CRM with  reports and dashboards . These highlight where the business challenges are, in plain and simple terms. It could be that four of five selling teams are at the right growth rate, and we just need to focus on another one. It could be that a certain product is challenged. The data opens up new doors to grow sales and see what could be working more effectively.

Another thing that’s great about a CRM is the guidance from AI. An  AI for sales  tool offers a neutral perspective on what’s actually happening in sales. For example, AI might note that an opportunity has been pushed out three quarters in a row — a finding that would’ve taken an individual reviewing the data longer to discover. Think of AI as your personal data scientist, taking your forecasting and entire sales operations to a new level.

Predictive AI tools take a look at historical sales data to give you a glimpse of what you might expect in the future. The AI will analyze factors like win rate or number of customer meetings. It takes some of the guesswork out of sales forecasting and helps you get to more accurate numbers. Try to analyze sales data for at least 12 months. Otherwise, there may not be enough data to get accurate sales predictions.

Sales forecasting is significantly more accurate when using a CRM instead of a spreadsheet. When a company is just starting out, sales teams usually rely on spreadsheets or back-of-the-napkin ways to calculate their sales forecasts. This may work for a while, but eventually, you’ll find this doesn’t scale.

The reality is, selling is more complex than ever. It involves everything from how demand generation campaigns are performing to how your phone calls to prospects are landing. The more you want to sell, the more you’ll want to  rely on a CRM .

See how Salesforce manages forecasts with confidence

The secret to an accurate forecast? Reliable, well-maintained pipelines. See how we manage both efficiently (with the help of the right technology), and use our best practices in your business.

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Paul Bookstaber is a writer at Salesforce. He has a decade of experience in content marketing in B2B tech. Before that, he published a magazine and ran a tabloid blog. Today, he splits his time between Florida and the Mountain West, and loves to hike, ski, and watch Bravo. He is in a polyamorous relationship with Luke and Roger, who are cats.

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7 Financial Forecasting Methods to Predict Business Performance

• 21 Jun 2022

Much of accounting involves evaluating past performance. Financial results demonstrate business success to both shareholders and the public. Planning and preparing for the future, however, is just as important.

Shareholders must be reassured that a business has been, and will continue to be, successful. This requires financial forecasting.

Here's an overview of how to use pro forma statements to conduct financial forecasting, along with seven methods you can leverage to predict a business's future performance.

What Is Financial Forecasting?

Financial forecasting is predicting a company’s financial future by examining historical performance data, such as revenue, cash flow, expenses, or sales. This involves guesswork and assumptions, as many unforeseen factors can influence business performance.

Financial forecasting is important because it informs business decision-making regarding hiring, budgeting, predicting revenue, and strategic planning . It also helps you maintain a forward-focused mindset.

Each financial forecast plays a major role in determining how much attention is given to individual expense items. For example, if you forecast high-level trends for general planning purposes, you can rely more on broad assumptions than specific details. However, if your forecast is concerned with a business’s future, such as a pending merger or acquisition, it's important to be thorough and detailed.

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Forecasting with Pro Forma Statements

A common type of forecasting in financial accounting involves using pro forma statements . Pro forma statements focus on a business's future reports, which are highly dependent on assumptions made during preparation⁠, such as expected market conditions.

Because the term "pro forma" refers to projections or forecasts, pro forma statements apply to any financial document, including:

• Income statements
• Balance sheets
• Cash flow statements

These statements serve both internal and external purposes. Internally, you can use them for strategic planning. Identifying future revenues and expenses can greatly impact business decisions related to hiring and budgeting. Pro forma statements can also inform endeavors by creating multiple statements and interchanging variables to conduct side-by-side comparisons of potential outcomes.

Externally, pro forma statements can demonstrate the risk of investing in a business. While this is an effective form of forecasting, investors should know that pro forma statements don't typically comply with generally accepted accounting principles (GAAP) . This is because pro forma statements don't include one-time expenses—such as equipment purchases or company relocations—which allows for greater accuracy because those expenses don't reflect a company’s ongoing operations.

7 Financial Forecasting Methods

Pro forma statements are incredibly valuable when forecasting revenue, expenses, and sales. These findings are often further supported by one of seven financial forecasting methods that determine future income and growth rates.

There are two primary categories of forecasting: quantitative and qualitative.

Quantitative Methods

When producing accurate forecasts, business leaders typically turn to quantitative forecasts , or assumptions about the future based on historical data.

1. Percent of Sales

Internal pro forma statements are often created using percent of sales forecasting . This method calculates future metrics of financial line items as a percentage of sales. For example, the cost of goods sold is likely to increase proportionally with sales; therefore, it’s logical to apply the same growth rate estimate to each.

To forecast the percent of sales, examine the percentage of each account’s historical profits related to sales. To calculate this, divide each account by its sales, assuming the numbers will remain steady. For example, if the cost of goods sold has historically been 30 percent of sales, assume that trend will continue.

2. Straight Line

The straight-line method assumes a company's historical growth rate will remain constant. Forecasting future revenue involves multiplying a company’s previous year's revenue by its growth rate. For example, if the previous year's growth rate was 12 percent, straight-line forecasting assumes it'll continue to grow by 12 percent next year.

Although straight-line forecasting is an excellent starting point, it doesn't account for market fluctuations or supply chain issues.

3. Moving Average

Moving average involves taking the average—or weighted average—of previous periods⁠ to forecast the future. This method involves more closely examining a business’s high or low demands, so it’s often beneficial for short-term forecasting. For example, you can use it to forecast next month’s sales by averaging the previous quarter.

Moving average forecasting can help estimate several metrics. While it’s most commonly applied to future stock prices, it’s also used to estimate future revenue.

To calculate a moving average, use the following formula:

A1 + A2 + A3 … / N

Formula breakdown:

A = Average for a period

N = Total number of periods

Using weighted averages to emphasize recent periods can increase the accuracy of moving average forecasts.

4. Simple Linear Regression

Simple linear regression forecasts metrics based on a relationship between two variables⁠: dependent and independent. The dependent variable represents the forecasted amount, while the independent variable is the factor that influences the dependent variable.

The equation for simple linear regression is:

Y ⁠ = Dependent variable⁠ (the forecasted number)

B = Regression line's slope

X = Independent variable

A = Y-intercept

5. Multiple Linear Regression

If two or more variables directly impact a company's performance, business leaders might turn to multiple linear regression . This allows for a more accurate forecast, as it accounts for several variables that ultimately influence performance.

To forecast using multiple linear regression, a linear relationship must exist between the dependent and independent variables. Additionally, the independent variables can’t be so closely correlated that it’s impossible to tell which impacts the dependent variable.

Qualitative Methods

When it comes to forecasting, numbers don't always tell the whole story. There are additional factors that influence performance and can't be quantified. Qualitative forecasting relies on experts’ knowledge and experience to predict performance rather than historical numerical data.

These forecasting methods are often called into question, as they're more subjective than quantitative methods. Yet, they can provide valuable insight into forecasts and account for factors that can’t be predicted using historical data.

6. Delphi Method

The Delphi method of forecasting involves consulting experts who analyze market conditions to predict a company's performance.

A facilitator reaches out to those experts with questionnaires, requesting forecasts of business performance based on their experience and knowledge. The facilitator then compiles their analyses and sends them to other experts for comments. The goal is to continue circulating them until a consensus is reached.

7. Market Research

Market research is essential for organizational planning. It helps business leaders obtain a holistic market view based on competition, fluctuating conditions, and consumer patterns. It’s also critical for startups when historical data isn’t available. New businesses can benefit from financial forecasting because it’s essential for recruiting investors and budgeting during the first few months of operation.

When conducting market research, begin with a hypothesis and determine what methods are needed. Sending out consumer surveys is an excellent way to better understand consumer behavior when you don’t have numerical data to inform decisions.

Improve Your Forecasting Skills

Financial forecasting is never a guarantee, but it’s critical for decision-making. Regardless of your business’s industry or stage, it’s important to maintain a forward-thinking mindset—learning from past patterns is an excellent way to plan for the future.

If you’re interested in further exploring financial forecasting and its role in business, consider taking an online course, such as Financial Accounting , to discover how to use it alongside other financial tools to shape your business.

Do you want to take your financial accounting skills to the next level? Consider enrolling in Financial Accounting —one of three courses comprising our Credential of Readiness (CORe) program —to learn how to use financial principles to inform business decisions. Not sure which course is right for you? Download our free flowchart .

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What Is Forecasting?

How forecasting works, forecasting techniques, choosing the right forecasting method, the bottom line.

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Forecasting: What It Is, How It’s Used in Business and Investing

Forecasting is a technique that uses historical data as inputs to make informed estimates that are predictive in determining the direction of future trends.

Businesses utilize forecasting to determine how to allocate their budgets or plan for anticipated expenses for an upcoming period of time. This is typically based on the projected demand for the goods and services offered.

Key Takeaways

• Forecasting involves making predictions about the future.
• In finance, forecasting is used by companies to estimate earnings or other data for subsequent periods.
• Traders and analysts use forecasts in valuation models, to time trades, and to identify trends.
• Forecasts are often predicated on historical data.
• Because the future is uncertain, forecasts must often be revised, and actual results can vary greatly.

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Investors utilize forecasting to determine if events affecting a company, such as sales expectations, will increase or decrease the price of shares in that company. Forecasting also provides an important benchmark for firms, which need a long-term perspective of operations.

Equity analysts use forecasting to extrapolate how trends, such as gross domestic product (GDP) or unemployment , will change in the coming quarter or year. Finally, statisticians can utilize forecasting to analyze the potential impact of a change in business operations. For instance, data may be collected regarding the impact of customer satisfaction by changing business hours or the productivity of employees upon changing certain work conditions. These analysts then come up with earnings estimates that are often aggregated into a consensus figure. If actual earnings announcements miss the estimates, it can have a large impact on a company’s stock price.

Forecasting addresses a problem or set of data. Economists make assumptions regarding the situation being analyzed that must be established before the variables of the forecasting are determined. Based on the items determined, an appropriate data set is selected and used in the manipulation of information. The data is analyzed, and the forecast is determined. Finally, a verification period occurs when the forecast is compared to the actual results to establish a more accurate model for forecasting in the future.

The further out the forecast, the higher the chance that the estimate will be inaccurate.

In general, forecasting can be approached using qualitative techniques or quantitative ones. Quantitative methods of forecasting exclude expert opinions and utilize statistical data based on quantitative information. Quantitative forecasting models include time series methods, discounting, analysis of leading or lagging indicators, and econometric modeling that may try to ascertain causal links.

Qualitative Techniques

Qualitative forecasting models are useful in developing forecasts with a limited scope. These models are highly reliant on expert opinions and are most beneficial in the short term. Examples of qualitative forecasting models include interviews, on-site visits, market research , polls, and surveys that may apply the Delphi method (which relies on aggregated expert opinions).

Gathering data for qualitative analysis can sometimes be difficult or time-consuming. The CEOs of large companies are often too busy to take a phone call from a retail investor or show them around a facility. However, we can still sift through news reports and the text included in companies’ filings to get a sense of managers’ records, strategies, and philosophies.

Time Series Analysis

A time series analysis looks at historical data and how various variables have interacted with one another in the past. These statistical relationships are then extrapolated into the future to generate forecasts along with confidence intervals to understand the likelihood of the actual outcomes falling within that scope. As with all forecasting methods, success is not guaranteed.

The  Box-Jenkins Model is a technique designed to forecast data ranges based on inputs from a specified time series. It forecasts data using three principles:  autoregression , differencing, and  moving averages . Another method, known as  rescaled range analysis , can be used to detect and evaluate the amount of persistence, randomness, or  mean reversion  in time series data. The rescaled range can be used to extrapolate a future value or average for the data to see if a trend is stable or likely to reverse.

Most often, time series forecasts involve trend analysis, cyclical fluctuation analysis, and issues of seasonality .

Econometric Inference

Another quantitative approach is to look at cross-sectional data to identify links among variables—although identifying causation is tricky and can often be spurious. This is known as econometric analysis , which often employs regression models . Techniques such as the use of instrumental variables, if available, can help one make stronger causal claims.

For instance, an analyst might look at revenue and compare it to economic indicators such as inflation and unemployment. Changes to financial or statistical data are observed to determine the relationship between multiple variables. A sales forecast may thus be based on several inputs such as aggregate demand, interest rates, market share, and advertising budget (among others).

The right forecasting method will depend on the type and scope of the forecast. Qualitative methods are more time-consuming and costly but can make very accurate forecasts given a limited scope. For instance, they might be used to predict how well a company’s new product launch might be received by the public.

For quicker analyses that can encompass a larger scope, quantitative methods are often more useful. Looking at big data sets, statistical software packages today can crunch the numbers in a matter of minutes or seconds. However, the larger the data set and the more complex the analysis, the pricier it can be.

Thus, forecasters often make a sort of cost-benefit analysis to determine which method maximizes the chances of an accurate forecast in the most efficient way. Furthermore, combining techniques can be synergistic and improve the forecast’s reliability.

What is business forecasting?

Business forecasting tries to make informed guesses or predictions about the future state of certain business metrics such as sales growth or economy-wide predictions such as gross domestic product (GDP) growth in the next quarter. Business forecasting relies on both quantitative and qualitative techniques to improve accuracy. Managers use forecasting for internal purposes to make capital allocation decisions and determine whether to make acquisitions, expand, or divest. They also make forward-looking projections for public dissemination such as earnings guidance .

What Are Some Limitations of Forecasting?

The biggest limitation of forecasting is that it involves the future, which is fundamentally unknowable today. As a result, forecasts can only be best guesses. While there are several methods of improving the reliability of forecasts, the assumptions that go into the models, or the data that is inputted into them, has to be correct. Otherwise, the result will be garbage in, garbage out. Even if the data is good, forecasting often relies on historical data, which is not guaranteed to be valid into the future, as things can and do change over time. It is also impossible to correctly factor in unusual or one-off events like a crisis or disaster.

What Are The Major Forecasting Techniques?

Several forecasting methods can be broadly segmented as either qualitative or quantitative. Within each category, there are several techniques available.

• Under qualitative methods, techniques may involve interviews, on-site visits, the Delphi method of pooling experts’ opinions, focus groups, and text analysis of financial documents, news items, and so forth.
• Under quantitative methods, techniques generally employ statistical models that look at time series or cross-sectional data, such as econometric regression analysis or causal inference (when available).

Forecasts help managers, analysts, and investors make informed decisions about the future. Without good forecasts, many of us would be in the dark and resort to guesses or speculation. By using qualitative and quantitative data analysis, forecasters can get a better handle of what lies ahead.

Businesses use forecasts and projections to inform managerial decisions and capital allocations. Analysts use forecasts to estimate corporate earnings for subsequent periods. Economists may make more macro-level forecasts as well, such as predicting GDP growth or changes to employment. However, since we cannot definitively know the future, and since forecasts often rely on historical data, their accuracy will always come with some room for error—and, in some cases, may end up being way off.

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Artificial intelligence in strategy

Can machines automate strategy development? The short answer is no. However, there are numerous aspects of strategists’ work where AI and advanced analytics tools can already bring enormous value. Yuval Atsmon is a senior partner who leads the new McKinsey Center for Strategy Innovation, which studies ways new technologies can augment the timeless principles of strategy. In this episode of the Inside the Strategy Room podcast, he explains how artificial intelligence is already transforming strategy and what’s on the horizon. This is an edited transcript of the discussion. For more conversations on the strategy issues that matter, follow the series on your preferred podcast platform .

Joanna Pachner: What does artificial intelligence mean in the context of strategy?

Yuval Atsmon: When people talk about artificial intelligence, they include everything to do with analytics, automation, and data analysis. Marvin Minsky, the pioneer of artificial intelligence research in the 1960s, talked about AI as a “suitcase word”—a term into which you can stuff whatever you want—and that still seems to be the case. We are comfortable with that because we think companies should use all the capabilities of more traditional analysis while increasing automation in strategy that can free up management or analyst time and, gradually, introducing tools that can augment human thinking.

Joanna Pachner: AI has been embraced by many business functions, but strategy seems to be largely immune to its charms. Why do you think that is?

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Yuval Atsmon: You’re right about the limited adoption. Only 7 percent of respondents to our survey about the use of AI say they use it in strategy or even financial planning, whereas in areas like marketing, supply chain, and service operations, it’s 25 or 30 percent. One reason adoption is lagging is that strategy is one of the most integrative conceptual practices. When executives think about strategy automation, many are looking too far ahead—at AI capabilities that would decide, in place of the business leader, what the right strategy is. They are missing opportunities to use AI in the building blocks of strategy that could significantly improve outcomes.

I like to use the analogy to virtual assistants. Many of us use Alexa or Siri but very few people use these tools to do more than dictate a text message or shut off the lights. We don’t feel comfortable with the technology’s ability to understand the context in more sophisticated applications. AI in strategy is similar: it’s hard for AI to know everything an executive knows, but it can help executives with certain tasks.

When executives think about strategy automation, many are looking too far ahead—at AI deciding the right strategy. They are missing opportunities to use AI in the building blocks of strategy.

Joanna Pachner: What kind of tasks can AI help strategists execute today?

Yuval Atsmon: We talk about six stages of AI development. The earliest is simple analytics, which we refer to as descriptive intelligence. Companies use dashboards for competitive analysis or to study performance in different parts of the business that are automatically updated. Some have interactive capabilities for refinement and testing.

The second level is diagnostic intelligence, which is the ability to look backward at the business and understand root causes and drivers of performance. The level after that is predictive intelligence: being able to anticipate certain scenarios or options and the value of things in the future based on momentum from the past as well as signals picked in the market. Both diagnostics and prediction are areas that AI can greatly improve today. The tools can augment executives’ analysis and become areas where you develop capabilities. For example, on diagnostic intelligence, you can organize your portfolio into segments to understand granularly where performance is coming from and do it in a much more continuous way than analysts could. You can try 20 different ways in an hour versus deploying one hundred analysts to tackle the problem.

Predictive AI is both more difficult and more risky. Executives shouldn’t fully rely on predictive AI, but it provides another systematic viewpoint in the room. Because strategic decisions have significant consequences, a key consideration is to use AI transparently in the sense of understanding why it is making a certain prediction and what extrapolations it is making from which information. You can then assess if you trust the prediction or not. You can even use AI to track the evolution of the assumptions for that prediction.

Those are the levels available today. The next three levels will take time to develop. There are some early examples of AI advising actions for executives’ consideration that would be value-creating based on the analysis. From there, you go to delegating certain decision authority to AI, with constraints and supervision. Eventually, there is the point where fully autonomous AI analyzes and decides with no human interaction.

Because strategic decisions have significant consequences, you need to understand why AI is making a certain prediction and what extrapolations it’s making from which information.

Joanna Pachner: What kind of businesses or industries could gain the greatest benefits from embracing AI at its current level of sophistication?

Yuval Atsmon: Every business probably has some opportunity to use AI more than it does today. The first thing to look at is the availability of data. Do you have performance data that can be organized in a systematic way? Companies that have deep data on their portfolios down to business line, SKU, inventory, and raw ingredients have the biggest opportunities to use machines to gain granular insights that humans could not.

Companies whose strategies rely on a few big decisions with limited data would get less from AI. Likewise, those facing a lot of volatility and vulnerability to external events would benefit less than companies with controlled and systematic portfolios, although they could deploy AI to better predict those external events and identify what they can and cannot control.

Third, the velocity of decisions matters. Most companies develop strategies every three to five years, which then become annual budgets. If you think about strategy in that way, the role of AI is relatively limited other than potentially accelerating analyses that are inputs into the strategy. However, some companies regularly revisit big decisions they made based on assumptions about the world that may have since changed, affecting the projected ROI of initiatives. Such shifts would affect how you deploy talent and executive time, how you spend money and focus sales efforts, and AI can be valuable in guiding that. The value of AI is even bigger when you can make decisions close to the time of deploying resources, because AI can signal that your previous assumptions have changed from when you made your plan.

Joanna Pachner: Can you provide any examples of companies employing AI to address specific strategic challenges?

Yuval Atsmon: Some of the most innovative users of AI, not coincidentally, are AI- and digital-native companies. Some of these companies have seen massive benefits from AI and have increased its usage in other areas of the business. One mobility player adjusts its financial planning based on pricing patterns it observes in the market. Its business has relatively high flexibility to demand but less so to supply, so the company uses AI to continuously signal back when pricing dynamics are trending in a way that would affect profitability or where demand is rising. This allows the company to quickly react to create more capacity because its profitability is highly sensitive to keeping demand and supply in equilibrium.

Joanna Pachner: Given how quickly things change today, doesn’t AI seem to be more a tactical than a strategic tool, providing time-sensitive input on isolated elements of strategy?

Yuval Atsmon: It’s interesting that you make the distinction between strategic and tactical. Of course, every decision can be broken down into smaller ones, and where AI can be affordably used in strategy today is for building blocks of the strategy. It might feel tactical, but it can make a massive difference. One of the world’s leading investment firms, for example, has started to use AI to scan for certain patterns rather than scanning individual companies directly. AI looks for consumer mobile usage that suggests a company’s technology is catching on quickly, giving the firm an opportunity to invest in that company before others do. That created a significant strategic edge for them, even though the tool itself may be relatively tactical.

Joanna Pachner: McKinsey has written a lot about cognitive biases  and social dynamics that can skew decision making. Can AI help with these challenges?

Yuval Atsmon: When we talk to executives about using AI in strategy development, the first reaction we get is, “Those are really big decisions; what if AI gets them wrong?” The first answer is that humans also get them wrong—a lot. [Amos] Tversky, [Daniel] Kahneman, and others have proven that some of those errors are systemic, observable, and predictable. The first thing AI can do is spot situations likely to give rise to biases. For example, imagine that AI is listening in on a strategy session where the CEO proposes something and everyone says “Aye” without debate and discussion. AI could inform the room, “We might have a sunflower bias here,” which could trigger more conversation and remind the CEO that it’s in their own interest to encourage some devil’s advocacy.

We also often see confirmation bias, where people focus their analysis on proving the wisdom of what they already want to do, as opposed to looking for a fact-based reality. Just having AI perform a default analysis that doesn’t aim to satisfy the boss is useful, and the team can then try to understand why that is different than the management hypothesis, triggering a much richer debate.

In terms of social dynamics, agency problems can create conflicts of interest. Every business unit [BU] leader thinks that their BU should get the most resources and will deliver the most value, or at least they feel they should advocate for their business. AI provides a neutral way based on systematic data to manage those debates. It’s also useful for executives with decision authority, since we all know that short-term pressures and the need to make the quarterly and annual numbers lead people to make different decisions on the 31st of December than they do on January 1st or October 1st. Like the story of Ulysses and the sirens, you can use AI to remind you that you wanted something different three months earlier. The CEO still decides; AI can just provide that extra nudge.

Joanna Pachner: It’s like you have Spock next to you, who is dispassionate and purely analytical.

Yuval Atsmon: That is not a bad analogy—for Star Trek fans anyway.

Joanna Pachner: Do you have a favorite application of AI in strategy?

Yuval Atsmon: I have worked a lot on resource allocation, and one of the challenges, which we call the hockey stick phenomenon, is that executives are always overly optimistic about what will happen. They know that resource allocation will inevitably be defined by what you believe about the future, not necessarily by past performance. AI can provide an objective prediction of performance starting from a default momentum case: based on everything that happened in the past and some indicators about the future, what is the forecast of performance if we do nothing? This is before we say, “But I will hire these people and develop this new product and improve my marketing”— things that every executive thinks will help them overdeliver relative to the past. The neutral momentum case, which AI can calculate in a cold, Spock-like manner, can change the dynamics of the resource allocation discussion. It’s a form of predictive intelligence accessible today and while it’s not meant to be definitive, it provides a basis for better decisions.

Joanna Pachner: Do you see access to technology talent as one of the obstacles to the adoption of AI in strategy, especially at large companies?

Yuval Atsmon: I would make a distinction. If you mean machine-learning and data science talent or software engineers who build the digital tools, they are definitely not easy to get. However, companies can increasingly use platforms that provide access to AI tools and require less from individual companies. Also, this domain of strategy is exciting—it’s cutting-edge, so it’s probably easier to get technology talent for that than it might be for manufacturing work.

The bigger challenge, ironically, is finding strategists or people with business expertise to contribute to the effort. You will not solve strategy problems with AI without the involvement of people who understand the customer experience and what you are trying to achieve. Those who know best, like senior executives, don’t have time to be product managers for the AI team. An even bigger constraint is that, in some cases, you are asking people to get involved in an initiative that may make their jobs less important. There could be plenty of opportunities for incorpo­rating AI into existing jobs, but it’s something companies need to reflect on. The best approach may be to create a digital factory where a different team tests and builds AI applications, with oversight from senior stakeholders.

The big challenge is finding strategists to contribute to the AI effort. You are asking people to get involved in an initiative that may make their jobs less important.

Joanna Pachner: Do you think this worry about job security and the potential that AI will automate strategy is realistic?

Yuval Atsmon: The question of whether AI will replace human judgment and put humanity out of its job is a big one that I would leave for other experts.

The pertinent question is shorter-term automation. Because of its complexity, strategy would be one of the later domains to be affected by automation, but we are seeing it in many other domains. However, the trend for more than two hundred years has been that automation creates new jobs, although ones requiring different skills. That doesn’t take away the fear some people have of a machine exposing their mistakes or doing their job better than they do it.

Joanna Pachner: We recently published an article about strategic courage in an age of volatility  that talked about three types of edge business leaders need to develop. One of them is an edge in insights. Do you think AI has a role to play in furnishing a proprietary insight edge?

Yuval Atsmon: One of the challenges most strategists face is the overwhelming complexity of the world we operate in—the number of unknowns, the information overload. At one level, it may seem that AI will provide another layer of complexity. In reality, it can be a sharp knife that cuts through some of the clutter. The question to ask is, Can AI simplify my life by giving me sharper, more timely insights more easily?

Joanna Pachner: You have been working in strategy for a long time. What sparked your interest in exploring this intersection of strategy and new technology?

Yuval Atsmon: I have always been intrigued by things at the boundaries of what seems possible. Science fiction writer Arthur C. Clarke’s second law is that to discover the limits of the possible, you have to venture a little past them into the impossible, and I find that particularly alluring in this arena.

AI in strategy is in very nascent stages but could be very consequential for companies and for the profession. For a top executive, strategic decisions are the biggest way to influence the business, other than maybe building the top team, and it is amazing how little technology is leveraged in that process today. It’s conceivable that competitive advantage will increasingly rest in having executives who know how to apply AI well. In some domains, like investment, that is already happening, and the difference in returns can be staggering. I find helping companies be part of that evolution very exciting.

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Marketing for financial advisors: how to develop a plan.

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Trey Robinson, Founder of Story Amplify .

When meeting with a prospect for the first time, you probably don’t automatically offer your products or services. Instead, you build a foundation by talking with them to learn where they are now and where they hope to be. Only then do you offer a plan and products to help them reach their goals.

Creating an effective marketing plan requires the same process. First, you must build the foundation, and then you can lay out the strategy. Here's the approach my agency uses to create successful marketing plans for our financial advisor clients.

The Foundation

Part 1: know your audience.

The foundation of financial advisor marketing is the same as that of your first client meeting. You must understand your audience and its pain points. This knowledge helps you craft the right message to solve their problems. For example, an older audience’s pain point might be having financial resources post-retirement, whereas younger families have different worries, such as reducing debt and having enough money to send their kids to college.

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Biden vs trump 2024 election polls trump leads biden by 1 point latest survey shows, ios 17 5 iphone update now live with important new features, part 2: solidify your brand.

A great deal of complex information is out there about the importance of “brands” and “branding.” Boiling it down, your brand is your identity. It’s what comes to your audience’s mind when they hear or think about your name.

To introduce and solidify your brand, you should communicate:

• Your products, services and their benefits.

• What drives you (i.e., why you’re in this business and industry).

• How you differ from the competition.

For example, many financial advisors offer 529 programs to parents and grandparents. Tell those parents and grandparents why your products—and services—are better than others. Maybe you offer workshops in addition to account management, or you might offer budgeting advice as a freebie.

The Strategy

With a solid foundation, developing a marketing strategy is your next step. Digital marketing for financial advisors involves content creation, distribution and lead capturing.

Content Creation

Content includes the text, images and video you use to message your audience. Available content formats include:

• E-books and white papers

• Social media posts

• Newsletters

• Educational videos

• Display ads

• Website copy

Remember your audience. Determine how they consume content. Young families might respond more positively to social media and video, while older adults might prefer written content, such as white papers, blogs and e-books.

The content format you use will also depend on your content distribution strategy.

Content Distribution

There are two types of content distribution: organic channels and paid channels.

Organic Channels

Organic channels mean you don’t pay to build search engine rankings or engage with your audience.

Let’s say a prospect types “financial advisor” into a search engine. A successful organic channel strategy puts your name near or at the top of the resulting search engine results page (SERP). The best way to boost your organic search ranking is to consistently create unique, relevant and valuable content that addresses your audience’s problems.

You can also boost your ranking through the following:

• Google reviews: Positive reviews on Google are great—they position you as a trustworthy source. As a trusted business, search engines could reward you with a higher SERP rank.

• Local business listings: Digital listings on Google, Facebook, LinkedIn, Yelp and the Better Business Bureau's website help leads and prospects locate you. They should include your name, address, phone number, website URL and business description to improve your online presence, potentially leading to a higher SERP ranking.

• Social media: Social media is a terrific outlet for your content. Posting regularly on social media provides you with more backlinks to your website (especially if your content is shared). This increases awareness of your brand—and potentially your SERP ranking.

Another way to boost your visibility in the search results is by earning a free Google Screened badge . Once you've been screened and verified, you receive a green checkmark next to your business's name in your Google Local Services listing, which indicates your trustworthiness to prospective clients.

Paid Channels

To improve traffic flow to your website, you can pay for a more visible presence through paid channels including search engines, other websites or social media. Many paid channels are pay-per-click.

Here are some paid channels to consider using:

• Paid search advertising: A paid search ad guarantees a higher position for your business on a SERP. If a prospect searches for “financial advisors and retirement,” your paid search ad will appear close to the top of that search page.

• Social media: You can post content for free on social media, but paying can help you stand out. When determining your ad’s best social media outlet, consider LinkedIn’s business orientation, Facebook’s friend-to-friend interface and Instagram’s visual requirements.

• Retargeting ads: When prospects visit your website but don’t act, retargeting ads step in. These small display ads “follow” prospects as they visit other sites or social media accounts, encouraging them to leave their contact information or set up an appointment with you.

Lead Capturing

Lead capturing converts your website visitors and prospects to leads so you can follow up.

One way to capture leads is to offer something that entices your audience to return for more. An older audience might appreciate a complimentary dinner and retirement information. Younger families could value a well-written white paper or video about 529 investing.

Appointment setting is another way to capture leads. Encourage prospects to schedule a no-obligation meeting where they can have a no-pressure discussion with you.

How Much Should You Spend?

Even if you decide not to pursue paid channels, you still need resources to support your marketing efforts. How much is enough?

A study by Broadridge found that the average advisor spent $17,400 on marketing in 2022. You can calculate a break-even point by determining your anticipated marketing costs and the revenue necessary to cover that expense.

Final Takeaways

You have a strategy in place when you first meet with clients. You need a similar approach when marketing to and communicating with prospects.

Marketing requires planning and audience knowledge. Take the time to understand your audience’s pain points. Then, develop and distribute content to meet their needs.

Forbes Agency Council is an invitation-only community for executives in successful public relations, media strategy, creative and advertising agencies. Do I qualify?

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From a 65% stock market crash to an imminent recession, here's a roundup of recent high-profile bear forecasts

• Despite a stock market that's less than 1% away from record highs, bearish forecasts are out in full force.
• Top economists and portfolio managers are warnings of everything from an imminent recession to the potential for a 65% stock-market crash.
• Outlined below are assorted high-profile bear forecasts from across Wall Street.

Even with the stock market less than 1% away from a record high, top forecasters who have consistently leaned bearish can't shake the idea that a painful decline is imminent. 

Some warn of an imminent recession following a weaker-than-expected April jobs report that coincided with a jump in weekly jobless claims, while others suggest a stock market crash similar to 1929 is about to happen.

While these forecasts have fallen flat on their face so far, they're still worth monitoring as a way for investors to poke potential holes in the consistently bullish narrative that the economy, corporate profits, and stock market are doing just fine.

Here's a roundup of the most recent bearish forecasts coming from Wall Street.

Gary Shilling: Recession by year-end means 30% stock market plunge

Wall Street veteran Gary Shilling told Business Insider this week that he expects a recession to materialize in the US economy by the end of the year as the labor market shows signs of weakening. And a weakening in the labor market will crush investor confidence and send the stock market falling by as much as 30%.

"You look at all the kind of speculation that we've had out there, it's indicative of a lot of overconfidence, and that usually gets corrected and corrected violently," Shilling told BI's Jennifer Sor. "I think that the safe bet is for a recession starting later this year if we're not already in it."

Shilling is known for correctly identifying the US housing bubble in the mid-2000's, though most of his consistently bearish views over the past decade have yet to pan out. 

John Hussman: A 65% stock-market crash wouldn't be surprising

John Hussman, president of the Hussman Investment Trust, issued a bearish warning on stocks this past week, arguing that the S&P 500 is trading at similar extremes seen in the run-up to the 1929 Great Depression as the fear-of-missing-out takes over investors.

"Statistically, the current set of market conditions looks more 'like' a major bull market peak than any other point in the past century, with the possible exception of the 1929 peak," Hussman said, adding that the combination of "extreme valuations, unfavorable market internals, and dozens of other factors" give him comfort in having a bearish outlook on the stock market.

Hussman said he wouldn't be surprised if the S&P 500 crashed 65%, which would erase a decade of stock market gains and put the index at about 1,800, or where it traded at back in February 2014. 

Hussman is famous for successfully warnings about the 2000 dot-com bubble and the 2008 housing market crash, though his consistently bearish predictions since then have yet to fully materialize.

BCA Research: A recession in early 2025 will cause 30% stock market decline

BCA strategist Roukaya Ibrahim warned that a 30% correction in the stock market could be sparked by a recession early next year.

Ibrahim told Bloomberg TV that the combination of elevated stock valuations and decelerating growth would send the S&P 500 back down to 3,600, which is around where the index bottomed in October 2022.

Ibrahim pointed to the April employment report, which saw 175,000 jobs added to the economy, revealed lower job openings, hires, and quit rates, all of which signal a shifting narrative toward economic downside, not upside.

"Eventually, the unemployment rate is going to take higher and that's going to lead to concerns about a recession," Ibrahim said.

David Rosenberg: Signs are growing of a potential hard-landing in the economy

The US might be "sleepwalking" into a recession as the labor market shows signs of weakening, according to top economist David Rosenberg.

"We're constantly asked when we're planning to throw in the towel on our recession call, but perhaps it's time folks started asking when the rest of the street is going to pick their towels back up," Rosenberg said in a note this week. "We've seen a downshift in the data flow that are starting to indicate that the downturn in the economy may not be as far away as many believe."

Rosenberg said the Sahm Rule is on the verge of flashing a recessionary warning after the unemployment rate ticked higher to 3.9% in April, and that manufacturing activity contracted for the 17th month out of the last 18 months.

"Don't get complacent. The labor market is cracking, a slowdown in services activity is dragging on real-time growth, and forward looking financial signals still point to a coming slowdown," Rosenberg said.

Rosenberg famously predicted the 2008 recession, but his consistently bearish economic outlooks since then have largely fallen flat.

The counterpoint: A bullish take to balance out the doomsayers

One investment strategist who has been consistently bullish, and therefore right, over the past few years is market veteran Ed Yardeni. 

Yardeni said in a note on Friday that the economic doomsayers are likely once again too early in their recession predictions following the weaker-than-expected April jobs report and weekly initial jobless claims data.

"The most widely anticipated recession of all times is turning into the longest widely anticipated recession of all times," market veteran Ed Yardeni said. "One day, the diehard hard-landers will be right."

But that day probably won't come anytime soon, according to Yardeni, as corporate profit estimates continue to hit record highs.

"Forward earnings rose to a record high during April, consistent with a solid labor market. So we don't buy the claim that the latest jobless claims is just the beginning of a significant downturn in the labor market and the economy," Yardeni said. 

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