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- Methodological Paper
- Published: 10 April 2017
Marketing survey research best practices: evidence and recommendations from a review of JAMS articles
- John Hulland 1 ,
- Hans Baumgartner 2 &
- Keith Marion Smith 3
Journal of the Academy of Marketing Science volume 46 , pages 92–108 ( 2018 ) Cite this article
Survey research methodology is widely used in marketing, and it is important for both the field and individual researchers to follow stringent guidelines to ensure that meaningful insights are attained. To assess the extent to which marketing researchers are utilizing best practices in designing, administering, and analyzing surveys, we review the prevalence of published empirical survey work during the 2006–2015 period in three top marketing journals— Journal of the Academy of Marketing Science ( JAMS ), Journal of Marketing ( JM ), and Journal of Marketing Research ( JMR )—and then conduct an in-depth analysis of 202 survey-based studies published in JAMS . We focus on key issues in two broad areas of survey research (issues related to the choice of the object of measurement and selection of raters, and issues related to the measurement of the constructs of interest), and we describe conceptual considerations related to each specific issue, review how marketing researchers have attended to these issues in their published work, and identify appropriate best practices.
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In conducting their topical review of publications in JMR , Huber et al. ( 2014 ) show evidence that the incidence of survey work has declined, particularly as new editors more skeptical of the survey method have emerged. They conclude (p. 88)—in looking at the results of their correspondence analysis—that survey research is more of a peripheral than a core topic in marketing. This perspective seems to be more prevalent in JMR than in JM and JAMS , as we note above.
A copy of the coding scheme used is available from the first author.
Several studies used more than one mode.
Traditionally, commercial researchers used phone as their primary collection mode. Today, 60% of commercial studies are conducted online (CASRO 2015 ), growing at a rate of roughly 8% per year.
Although the two categories are not necessarily mutually exclusive, the overlap was small ( n = 4).
This is close to the number of studies in which an explicit sampling frame was employed, which makes sense (i.e., one would not expect a check for non-response bias when a convenience sample is used).
It is interesting to note that Cote and Buckley examined the extent of CMV present in papers published across a variety of disciplines, and found that CMV was lowest for marketing (16%) and highest for the field of education (> 30%). This does not mean, however, that marketers do a consistently good job of accounting for CMV.
In practice, these items need to be conceptually related yet empirically distinct from one another. Using minor variations of the same basic item just to have multiple items does not result in the advantages described here.
In general, the use of PLS (which is usually employed when the measurement model is formative or mixed) was uncommon in our review, so it appears that most studies focused on using reflective measures.
Most of the studies discussing discriminant validity used the approach proposed by Fornell and Larcker ( 1981 ). A recent paper by Voorhees et al. ( 2016 ) suggests use of two approaches to determining discriminant validity: (1) the Fornell and Larcker test and (2) a new approach proposed by Henseler et al. ( 2015 ).
This solution is not a universal panacea. For example, Kammeyer-Mueller et al. ( 2010 ) show using simulated data that under some conditions using distinct data sources can distort estimation. Their point, however, is that the researcher must think carefully about this issue and resist using easy one-size-fits-all solutions.
Podsakoff et al. ( 2003 ) also mention two other techniques—the correlated uniqueness model and the direct product model—but do not recommend their use. Only very limited use of either technique has been made in marketing, so we do not discuss them further in this paper.
These techniques are described more extensively in Podsakoff et al. ( 2003 ), and contrasted to one another. Figure 1 (p. 898) and Table 4 (p. 891) in their paper are particularly helpful in understanding the differences across approaches.
It is unclear why the procedure is called the Harman test, because Harman never proposed the test and it is unlikely that he would be pleased to have his name associated with it. Greene and Organ ( 1973 ) are sometimes cited as an early application of the Harman test (they specifically mention “Harman’s test of the single-factor model,” p. 99), but they in turn refer to an article by Brewer et al. ( 1970 ), in which Harman’s one-factor test is mentioned. Brewer et al. ( 1970 ) argued that before testing the partial correlation between two variables controlling for a third variable, researchers should test whether a single-factor model can account for the correlations between the three variables, and they mentioned that one can use “a simple algebraic solution for extraction of a single factor (Harman 1960 : 122).” If measurement error is present, three measures of the same underlying factor will not be perfectly correlated, and if a single-factor model is consistent with the data, there is no need to consider a multi-factor model (which is implied by the use of partial correlations). It is clear that the article by Brewer et al. does not say anything about systematic method variance, and although Greene and Organ talk about an “artifact due to measurement error” (p. 99), they do not specifically mention systematic measurement error. Schriesheim ( 1979 ), another early application of Harman’s test, describes a factor analysis of 14 variables, citing Harman as a general factor-analytic reference, and concludes, “no general factor was apparent, suggesting a lack of substantial method variance to confound the interpretation of results” (p. 350). It appears that Schriesheim was the first to conflate Harman and testing for common method variance, although Harman was only cited as background for deciding how many factors to extract. Several years later, Podsakoff and Organ ( 1986 ) described Harman’s one-factor test as a post-hoc method to check for the presence of common method variance (pp. 536–537), although they also mention “some problems inherent in its use” (p. 536). In sum, it appears that starting with Schriesheim, the one-factor test was interpreted as a check for the presence of common method variance, although labeling the test Harman’s one-factor test seems entirely unjustified.
Ahearne, M., Haumann, T., Kraus, F., & Wieseke, J. (2013). It’s a matter of congruence: How interpersonal identification between sales managers and salespersons shapes sales success. Journal of the Academy of Marketing Science, 41 (6), 625–648.
Article Google Scholar
Armstrong, J. S., & Overton, T. S. (1977). Estimating nonresponse bias in mail surveys. Journal of Marketing Research, 14 (3), 396–402.
Arnold, T. J., Fang, E. E., & Palmatier, R. W. (2011). The effects of Customer acquisition and retention orientations on a Firm’s radical and incremental innovation performance. Journal of the Academy of Marketing Science, 39 (2), 234–251.
Bagozzi, R. P., & Yi, Y. (1990). Assessing method variance in Multitrait-Multimethod matrices: The case of self-reported affect and perceptions at work. Journal of Applied Psychology, 75 (5), 547–560.
Baker, R., Blumberg, S. J., Brick, J. M., Couper, M. P., Courtright, M., Dennis, J. M., & Kennedy, C. (2010). Research synthesis AAPOR report on online panels. Public Opinion Quarterly, 74 (4), 711–781.
Baker, T. L., Rapp, A., Meyer, T., & Mullins, R. (2014). The role of Brand Communications on front line service employee beliefs, behaviors, and performance. Journal of the Academy of Marketing Science, 42 (6), 642–657.
Baumgartner, H., & Steenkamp, J. B. E. (2001). Response styles in marketing research: A cross-National Investigation. Journal of Marketing Research, 38 (2), 143–156.
Baumgartner, H., & Weijters, B. (2017). Measurement models for marketing constructs. In B. Wierenga & R. van der Lans (Eds.), Springer Handbook of marketing decision models . New York: Springer.
Bell, S. J., Mengüç, B., & Widing II, R. E. (2010). Salesperson learning, Organizational learning, and retail store performance. Journal of the Academy of Marketing Science, 38 (2), 187–201.
Bergkvist, L., & Rossiter, J. R. (2007). The predictive validity of multiple-item versus single-item measures of the same constructs. Journal of Marketing Research, 44 (2), 175–184.
Berinsky, A. J. (2008). Survey non-response. In W. Donsbach & M. W. Traugott (Eds.), The SAGE Handbook of Public Opinion research (pp. 309–321). Thousand Oaks: SAGE Publications.
Chapter Google Scholar
Brewer, M. B., Campbell, D. T., & Crano, W. D. (1970). Testing a single-factor model as an alternative to the misuse of partial correlations in hypothesis-testing research. Sociometry, 33 (1), 1–11.
Carmines, E. G., and Zeller, R.A. (1979). Reliability and validity assessment. Sage University Paper Series on Quantitative Applications in the Social Sciences , no. 07-017. Beverly Hills: Sage.
CASRO. (2015). Annual CASRO benchmarking financial survey.
Cote, J. A., & Buckley, M. R. (1987). Estimating trait, method, and error variance: Generalizing across 70 construct validation studies. Journal of Marketing Research, 24 (3), 315–318.
Curtin, R., Presser, S., & Singer, E. (2005). Changes in telephone survey nonresponse over the past quarter century. Public Opinion Quarterly, 69 (1), 87–98.
De Jong, A., De Ruyter, K., & Wetzels, M. (2006). Linking employee confidence to performance: A study of self-managing service teams. Journal of the Academy of Marketing Science, 34 (4), 576–587.
Diamantopoulos, A., Riefler, P., & Roth, K. P. (2008). Advancing formative measurement models. Journal of Business Research, 61 (12), 1203–1218.
Doty, D. H., & Glick, W. H. (1998). Common methods bias: Does common methods variance really bias results? Organizational Research Methods, 1 (4), 374–406.
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18 (3), 39–50.
Goodman, J. K., Cryder, C. E., & Cheema, A. (2013). Data collection in a flat world: The strengths and weaknesses of Mechanical Turk samples. Journal of Behavioral Decision Making, 26 (3), 213–224.
Graesser, A. C., Wiemer-Hastings, K., Kreuz, R., Wiemer-Hastings, P., & Marquis, K. (2000). QUAID: A questionnaire evaluation aid for survey methodologists. Behavior Research Methods, Instruments, & Computers, 32 (2), 254–262.
Graesser, A. C., Cai, Z., Louwerse, M. M., & Daniel, F. (2006). Question understanding aid (QUAID) a web facility that tests question comprehensibility. Public Opinion Quarterly, 70 (1), 3–22.
Graham, J. W. (2009). Missing data analysis: Making it work in the real world. Annual Review of Psychology, 60 , 549–576.
Greene, C. N., & Organ, D. W. (1973). An evaluation of causal models linking the received role with job satisfaction. Administrative Science Quarterly , 95-103.
Grégoire, Y., & Fisher, R. J. (2008). Customer betrayal and retaliation: When your best customers become your worst enemies. Journal of the Academy of Marketing Science, 36 (2), 247–261.
Groves, R. M. (2006). Nonresponse rates and nonresponse bias in household surveys. Public Opinion Quarterly, 70 (5), 646–675.
Groves, R. M., & Couper, M. P. (2012). Nonresponse in household interview surveys . New York: Wiley.
Groves, R. M., Couper, M. P., Lepkowski, J. M., Singer, E., & Tourangeau, R. (2004). Survey methodology (Second ed.). New York: McGraw-Hill.
Harman, H. H. (1960). Modern factor analysis . Chicago: University of Chicago Press.
Heckman, J. J. (1979). Sample selection bias as a specification error. Econometrica, 47 , 153–161.
Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43 (1), 115–135.
Hillygus, D. S., Jackson, N., & Young, M. (2014). Professional respondents in non-probability online panels. In M. Callegaro, R. Baker, J. Bethlehem, A. S. Goritz, J. A. Krosnick, & P. J. Lavrakas (Eds.), Online panel research: A data quality perspective (pp. 219–237). Chichester: John Wiley & Sons.
Hinkin, T. R. (1995). A review of scale development practices in the study of organizations. Journal of Management, 21 (5), 967–988.
Huber, J., Kamakura, W., & Mela, C. F. (2014). A topical history of JMR. Journal of Marketing Research, 51 (1), 84–91.
Hughes, D. E., Le Bon, J., & Rapp, A. (2013). Gaining and leveraging Customer-based competitive intelligence: The pivotal role of social capital and salesperson adaptive selling skills. Journal of the Academy of Marketing Science, 41 (1), 91–110.
Hulland, J. (1999). Use of partial least squares (PLS) in Strategic Management research: A review of four recent studies. Strategic Management Journal, 20 (2), 195–204.
Jap, S. D., & Anderson, E. (2004). Challenges and advances in marketing strategy field research. In C. Moorman & D. R. Lehman (Eds.), Assessing marketing strategy performance (pp. 269–292). Cambridge: Marketing Science Institute.
Jarvis, C. B., MacKenzie, S. B., & Podsakoff, P. M. (2003). A critical review of construct indicators and measurement model misspecification in marketing and consumer research. Journal of Consumer Research, 30 (2), 199–218.
Kamakura, W. A. (2001). From the Editor. Journal of Marketing Research, 38 , 1–2.
Kammeyer-Mueller, J., Steel, P. D., & Rubenstein, A. (2010). The other side of method bias: The perils of distinct source research designs. Multivariate Behavioral Research, 45 (2), 294–321.
Kemery, E. R., & Dunlap, W. P. (1986). Partialling factor scores does not control method variance: A reply to Podsakoff and Todor. Journal of Management, 12 (4), 525–530.
Lance, C. E., Dawson, B., Birkelbach, D., & Hoffman, B. J. (2010). Method effects, measurement error, and substantive conclusions. Organizational Research Methods, 13 (3), 435–455.
Lenzner, T. (2012). Effects of survey question comprehensibility on response quality. Field Methods, 24 (4), 409–428.
Lenzner, T., Kaczmirek, L., & Lenzner, A. (2010). Cognitive burden of survey questions and response times: A psycholinguistic experiment. Applied Cognitive Psychology, 24 (7), 1003–1020.
Lenzner, T., Kaczmirek, L., & Galesic, M. (2011). Seeing through the eyes of the respondent: An eye-tracking study on survey question comprehension. International Journal of Public Opinion Research, 23 (3), 361–373.
Lindell, M. K., & Whitney, D. J. (2001). Accounting for common method variance in cross-sectional research designs. Journal of Applied Psychology, 86 (1), 114–121.
Lohr, S. (1999). Sampling: Design and analysis . Pacific Grove: Duxbury Press.
MacKenzie, S. B., Podsakoff, P. M., & Jarvis, C. B. (2005). The problem of measurement model misspecification in Behavioral and Organizational research and some recommended solutions. Journal of Applied Psychology, 90 (4), 710.
MacKenzie, S. B., Podsakoff, P. M., & Podsakoff, N. P. (2011). Construct measurement and validation procedures in MIS and Behavioral research: Integrating new and existing techniques. MIS Quarterly, 35 (2), 293–334.
Meade, A. W., & Craig, S. B. (2012). Identifying careless responses in survey data. Psychological Methods, 17 (3), 437–455.
Nunnally, J. (1978). Psychometric methods (Second ed.). New York: McGraw Hill.
Oppenheimer, D. M., Meyvis, T., & Davidenko, N. (2009). Instructional manipulation checks: Detecting satisficing to increase statistical power. Journal of Experimental Social Psychology, 45 (4), 867–872.
Ostroff, C., Kinicki, A. J., & Clark, M. A. (2002). Substantive and operational issues of response bias across levels of analysis: An example of climate-satisfaction relationships. Journal of Applied Psychology, 87 (2), 355–368.
Paolacci, G., Chandler, J., & Ipeirotis, P. G. (2010). Running experiments on Amazon Mechanical Turk. Judgment and Decision making, 5 (5), 411–419.
Phillips, L. W. (1981). Assessing measurement error in key informant reports: A methodological note on Organizational analysis in marketing. Journal of Marketing Research, 18 , 395–415.
Podsakoff, P. M., & Organ, D. W. (1986). Self-reports in Organizational research: Problems and prospects. Journal of Management, 12 (4), 531–544.
Podsakoff, P. M., MacKenzie, S. B., Lee, J. Y., & Podsakoff, N. P. (2003). Common method biases in Behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88 (5), 879–903.
Podsakoff, P. M., MacKenzie, S. B., & Podsakoff, N. P. (2012). Sources of method bias in social Science research and recommendations on how to control it. Annual Review of Psychology, 63 , 539–569.
Richardson, H. A., Simmering, M. J., & Sturman, M. C. (2009). A tale of three perspectives: Examining post hoc statistical techniques for detection and correction of common method variance. Organizational Research Methods, 12 (4), 762–800.
Rindfleisch, A, & Antia, K. D. (2012). Survey research in B2B marketing: Current challenges and emerging opportunities. In G. L. Lilien, & R. Grewal (Eds.), Handbook of Business-to-Business marketing (pp 699–730). Northampton: Edward Elgar.
Rindfleisch, A., Malter, A. J., Ganesan, S., & Moorman, C. (2008). Cross-sectional versus longitudinal survey research: Concepts, findings, and guidelines. Journal of Marketing Research, 45 (3), 261–279.
Rossiter, J. R. (2002). The C-OAR-SE procedure for scale development in marketing. International Journal of Research in Marketing, 19 (4), 305–335.
Schaller, T. K., Patil, A., & Malhotra, N. K. (2015). Alternative techniques for assessing common method variance: An analysis of the theory of planned behavior research. Organizational Research Methods, 18 (2), 177–206.
Schriesheim, C. A. (1979). The similarity of individual directed and group directed leader behavior descriptions. Academy of Management Journal., 22 (2), 345–355.
Schuman, H., & Presser, N. (1981). Questions and answers in attitude surveys . New York: Academic.
Schwarz, N., Groves, R., & Schuman, H. (1998). Survey methods. In D. Gilbert, S. Fiske, & G. Lindzey (Eds.), Handbook of social psychology (Vol. 1, 4th ed., pp. 143–179). New York: McGraw Hill.
Simmering, M. J., Fuller, C. M., Richardson, H. A., Ocal, Y., & Atinc, G. M. (2015). Marker variable choice, reporting, and interpretation in the detection of common method variance: A review and demonstration. Organizational Research Methods, 18 (3), 473–511.
Song, M., Di Benedetto, C. A., & Nason, R. W. (2007). Capabilities and financial performance: The moderating effect of Strategic type. Journal of the Academy of Marketing Science, 35 (1), 18–34.
Stock, R. M., & Zacharias, N. A. (2011). Patterns and performance outcomes of innovation orientation. Journal of the Academy of Marketing Science, 39 (6), 870–888.
Sudman, S., Bradburn, N. M., & Schwarz, N. (1996). Thinking about answers: The application of cognitive processes to survey methodology . San Francisco: Jossey-Bass.
Summers, J. O. (2001). Guidelines for conducting research and publishing in marketing: From conceptualization through the review process. Journal of the Academy of Marketing Science, 29 (4), 405–415.
The American Association for Public Opinion Research. (2016). Standard definitions: Final dispositions of case codes and outcome rates for surveys (9th ed.) AAPOR.
Tourangeau, R., Rips, L. J., & Rasinski, K. (2000). The psychology of survey response . Cambridge: Cambridge University Press.
Book Google Scholar
Voorhees, C. M., Brady, M. K., Calantone, R., & Ramirez, E. (2016). Discriminant validity testing in marketing: An analysis, causes for concern, and proposed remedies. Journal of the Academy of Marketing Science, 44 (1), 119–134.
Wall, T. D., Michie, J., Patterson, M., Wood, S. J., Sheehan, M., Clegg, C. W., & West, M. (2004). On the validity of subjective measures of company performance. Personnel Psychology, 57 (1), 95–118.
Wei, Y. S., Samiee, S., & Lee, R. P. (2014). The influence of organic Organizational cultures, market responsiveness, and product strategy on firm performance in an emerging market. Journal of the Academy of Marketing Science, 42 (1), 49–70.
Weijters, B., Baumgartner, H., & Schillewaert, N. (2013). Reversed item bias: An integrative model. Psychological Methods, 18 (3), 320–334.
Weisberg, H. F. (2005). The Total survey error approach: A guide to the new Science of survey research . Chicago: Chicago University Press.
Wells, W. D. (1993). Discovery-oriented consumer research. Journal of Consumer Research, 19 (4), 489–504.
Williams, L. J., Hartman, N., & Cavazotte, F. (2010). Method variance and marker variables: A review and comprehensive CFA marker technique. Organizational Research Methods, 13 (3), 477–514.
Winship, C., & Mare, R. D. (1992). Models for sample selection bias. Annual Review of Sociology, 18 (1), 327–350.
Wittink, D. R. (2004). Journal of marketing research: 2 Ps. Journal of Marketing Research, 41 (1), 1–6.
Zinkhan, G. M. (2006). From the Editor: Research traditions and patterns in marketing scholarship. Journal of the Academy of Marketing Science, 34 , 281–283.
The constructive comments of the Editor-in-Chief, Area Editor, and three reviewers are gratefully acknowledged.
Authors and affiliations.
Terry College of Business, University of Georgia, 104 Brooks Hall, Athens, GA, 30602, USA
Smeal College of Business, Penn State University, State College, PA, USA
D’Amore-McKim School of Business, Northeastern University, Boston, MA, USA
Keith Marion Smith
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Correspondence to John Hulland .
Aric Rindfleisch served as Guest Editor for this article.
Putting the Harman test to rest
A moment’s reflection will convince most researchers that the following two assumptions about method variance are entirely unrealistic: (1) most of the variation in ratings made in response to items meant to measure substantive constructs is due to method variance, and (2) a single source of method variance is responsible for all of the non-substantive variation in ratings. No empirical evidence exists to support these assumptions. Yet when it comes to testing for the presence of unwanted method variance in data, many researchers suspend disbelief and subscribe to these implausible assumptions. The reason, presumably, is that doing so conveniently satisfies two desiderata. First, testing for method variance has become a sine qua non in certain areas of research (e.g., managerial studies), so it is essential that the research contain some evidence that method variance was evaluated. Second, basing a test of method variance on procedures that are strongly biased against detecting method variance essentially guarantees that no evidence of method variance will ever be found in the data.
Although various procedures have been proposed to examine method variance, the most popular is the so-called Harman one-factor test, which makes both of the foregoing assumptions. Footnote 14 While the logic underlying the Harman test is convoluted, it seems to go as follows: If a single factor can account for the correlation among a set of measures, then this is prima facie evidence of common method variance. In contrast, if multiple factors are necessary to account for the correlations, then the data are free of common method variance. Why one factor indicates common method variance and not substantive variance (e.g., several substantive factors that lack discriminant validity), and why several factors indicate multiple substantive factors and not multiple sources of method variance remains unexplained. Although it is true that “if a substantial amount of common method variance is present, either (a) a single factor will emerge from the factor analysis, or (b) one ‘general’ factor will account for the majority of the covariance in the independent and criterion variables” (Podsakoff and Organ 1986 , p. 536), it is a logical fallacy (i.e., affirming the consequent) to argue that the existence of a single common factor (necessarily) implicates common method variance.
Apart from the inherent flaws of the test, several authors have pointed out various other difficulties associated with the Harman test (e.g., see Podsakoff et al. 2003 ). For example, it is not clear how much of the total variance a general factor has to account for before one can conclude that method variance is a problem. Furthermore, the likelihood that a general factor will account for a large portion of the variance decreases as the number of variables analyzed increases. Finally, the test only diagnoses potential problems with method variance but does not correct for them (e.g., Podsakoff and Organ 1986 ; Podsakoff et al. 2003 ). More sophisticated versions of the test have been proposed, which correct some of these shortcoming (e.g., if a confirmatory factor analysis is used, explicit tests of the tenability of a one-factor model are available), but the faulty logic of the test cannot be remedied.
In fact, the most misleading application of the Harman test occurs when the variance accounted for by a general factor is partialled from the observed variables. Since it is likely that the general factor contains not only method variance but also substantive variance, this means that partialling will not only remove common method variance but also substantive variance. Although researchers will most often argue that common method variance is not a problem since partialling a general factor does not materially affect the results, this conclusion is also misleading, because the test is usually conducted in such a way that the desired result is favored. For example, in most cases all loadings on the method factor are restricted to be equal, which makes the questionable assumption that the presumed method factor influences all observed variables equally, even though this assumption is not imposed for the trait loadings.
In summary, the Harman test is entirely non-diagnostic about the presence of common method variance in data. Researchers should stop going through the motions of conducting a Harman test and pretending that they are performing a meaningful investigation of systematic errors of measurement.
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Hulland, J., Baumgartner, H. & Smith, K.M. Marketing survey research best practices: evidence and recommendations from a review of JAMS articles. J. of the Acad. Mark. Sci. 46 , 92–108 (2018). https://doi.org/10.1007/s11747-017-0532-y
Received : 19 August 2016
Accepted : 29 March 2017
Published : 10 April 2017
Issue Date : January 2018
DOI : https://doi.org/10.1007/s11747-017-0532-y
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New tools to improve performance
What is a Marketing Research Report and How to Write It?
There is nothing more embarrassing for a marketer than to hear a client say “…this doesn’t quite address the business questions that we need to answer.” And unfortunately, this is a rather common occurrence in market research reporting that most marketers would care to admit.
So, why do most market research reports fail to meet client expectations? Well, in most cases, because there is more emphasis on methodology and analytic techniques used to craft the report rather than relying on data visualization, creative story-telling, and outlining actionable direction/steps.
Now, our next big question is, how do you avoid your client’s dreaded deer-in-the-headlights reaction when presenting such a report? This blog post will answer this and much more, as we go through the following:
What Is a Market Research Report?
Why is market research important, differences between primary and secondary market research, types of market research, market research reports advantages and disadvantages, how to do market research, how to prepare a market research report: 5 steps, marketing research report templates, marketing research reports best practices, bring your market research reports a step further with databox.
The purpose of creating a market research report is to make calculated decisions about business ideas. Market research is done to evaluate the feasibility of a new product or service, through research conducted with potential consumers. The information obtained from conducting market research is then documented in a formal report that should contain the following details:
- The characteristics of your ideal customers
- You customers buying habits
- The value your product or service can bring to those customers
- A list of your top competitors
Every business aims to provide the best possible product or service at the lowest cost possible. Simply said, market research is important because it helps you understand your customers and determine whether the product or service that you are about to launch is worth the effort.
Here is an example of a customer complaint that may result in more detailed market research:
Suppose you sell widgets, and you want your widget business to succeed over the long term. Over the years, you have developed many different ways of making widgets. But a couple of years ago, a customer complained that your widgets were made of a cheap kind of foam that fell apart after six months. You didn’t think at the time that this was a major problem, but now you know it.
The customer is someone you really want to keep. So, you decide to research this complaint. You set up a focus group of people who use widgets and ask them what they think about the specific problem. After the conducted survey you’ll get a better picture of customer opinions, so you can either decide to make the changes regarding widget design or just let it go.
PRO TIP: How Well Are Your Marketing KPIs Performing?
Like most marketers and marketing managers, you want to know how well your efforts are translating into results each month. How much traffic and new contact conversions do you get? How many new contacts do you get from organic sessions? How are your email campaigns performing? How well are your landing pages converting? You might have to scramble to put all of this together in a single report, but now you can have it all at your fingertips in a single Databox dashboard.
Our Marketing Overview Dashboard includes data from Google Analytics 4 and HubSpot Marketing with key performance metrics like:
- Sessions . The number of sessions can tell you how many times people are returning to your website. Obviously, the higher the better.
- New Contacts from Sessions . How well is your campaign driving new contacts and customers?
- Marketing Performance KPIs . Tracking the number of MQLs, SQLs, New Contacts and similar will help you identify how your marketing efforts contribute to sales.
- Email Performance . Measure the success of your email campaigns from HubSpot. Keep an eye on your most important email marketing metrics such as number of sent emails, number of opened emails, open rate, email click-through rate, and more.
- Blog Posts and Landing Pages . How many people have viewed your blog recently? How well are your landing pages performing?
Now you can benefit from the experience of our Google Analytics and HubSpot Marketing experts, who have put together a plug-and-play Databox template that contains all the essential metrics for monitoring your leads. It’s simple to implement and start using as a standalone dashboard or in marketing reports, and best of all, it’s free!
You can easily set it up in just a few clicks – no coding required.
To set up the dashboard, follow these 3 simple steps:
Step 1: Get the template
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Step 3: Watch your dashboard populate in seconds.
Marketing research requires both primary and secondary market research. But what does that mean and what are the main differences?
Primary market research takes in information directly from customers, usually as participants in surveys. Usually, it is consisted of:
- Exploratory Primary Research – This type of research helps to identify possible problem areas, and it’s not focused on discovering specific information about customers. As with any research, exploratory primary research should be conducted carefully. Researchers need to craft an interviewing or surveying plan, and gather enough respondents to ensure reasonable levels of statistical reliability.
- Specific Primary Research – This type of research is one of the best ways to approach a problem because it relies on existing customer data. Specific research provides a deeper, more thorough understanding of the problem and its potential solutions. The greatest advantage of specific research is that it lets you explore a very specific question, and focus on a specific problem or an opportunity.
Secondary market research collects information from other sources such as databases, trend reports, market or government statistics, industry content, etc. We can divide secondary market research into 3 categories:
- Public market data – Public sources range from academic journals and government reports to tax returns and court documents. These sources aren’t always easy to find. Many are available only in print in libraries and archives. You have to look beyond search engines like Google to find public source documents.
- Commercial data – Those are typically created by specialized agencies like Pew, Gartner or Forrester. the research agencies are quite expensive, but they provide a lot of useful information.
- Internal data – Your organization’s databases are gold mines for market research. In the best cases, your salespeople can tell you what they think about customers. Your salespeople are your direct sources of information about the market. Don’t underestimate your internal data.
In general, primary research is more reliable than secondary research, because researchers have to interview people directly. But primary research is expensive and time-consuming. Secondary research can be quicker and less expensive.
There are plenty of ways to conduct marketing research reports. Mostly, the type of research done will depend on your goals. Here are some types of market research often conducted by marketers.
Product/service use research, observation-based research, buyer persona research, market segmentation research, pricing research, competitive analysis research, customer satisfaction and loyalty research, brand awareness research, campaign research.
An interview is an interactive process of asking and answering questions and observing your respondent’s responses. Interviews are one of the most commonly used tools in market research . An interview allows an organization to observe, in detail, how its consumers interact with its products and services. It also allows an organization to address specific questions.
A focus group is a group of people who get together to discuss a particular topic. A moderator leads the discussion and takes notes. The main benefit of focus groups is that they are quick and easy to conduct. You can gather a group of carefully-selected people, give them a product to try out, and get their feedback within a few hours/days.
Product or service use research helps you obtain useful information about your product or service such as:
- What your current customers do with the product/service
- Which features of the product/service are particularly important to your customers
- What they dislike about the product/service
- What they would change about the product/service
Observation-based research helps you to observe your target audience interacting with your product or service. You will see the interactions and which aspects work well and which could be improved. The main point is to directly experience the feedback from your target audience’s point of view.
Personas are an essential sales tool. By knowing your buyers’ pain points and the challenges they face, you can create better content, target messaging, and campaigns for them. Buyer persona research is based on market research, and it’s built around data that describes your customers’ demographics, behaviors, motivations, and concerns. Sales reporting software can significantly help you develop buyer personas when you gain insights after you collected all information.
Market segmentation research is carried out to better understand existing and potential market segments. The objective is to determine how to target different market segments and how they differ from each other. The three most important steps in writing a market segmentation research report are:
- Defining the problem
- Determining the solution [and]
- Defining the market
Related : 9 Customer Segmentation Tips to Personalize Ecommerce Marketing and Drive More Sales
A price that is too high, or too low, can kill a business. And without good market research, you don’t really know what is a good price for your product. Pricing research helps you define your pricing strategy.
In a competitive analysis, you define your “competition” as any other entity that competes with you in your market, whether you’re selling a widget or a piece of real estate. With competitive analysis research, you can find out things like:
- Who your competitors are
- What they’ve done in the past
- What’s working well for them
- Their weaknesses
- How they’re positioned in the market
- How they market themselves
- What they’re doing that you’re not
Related : How to Do an SEO Competitive Analysis: A Step-by-Step Guide
In today’s marketplace, companies are increasingly focused on customer loyalty. What your customers want is your product, but, more importantly, they want it delivered with a service that exceeds their expectations. Successful companies listen to their customers and respond accordingly. That’s why customer satisfaction and loyalty research is a critical component of that basic equation.
Related : 11 Tactics for Effectively Measuring Your Customer Service ROI
Who you are, what you stand for, what you offer, what you believe in, and what your audience thinks of you is all wrapped up in brand. Brand awareness research tells what your target audience knows about your brand and what’s their experience like.
A campaign research report is a detailed account of how your marketing campaign performed. It includes all the elements that went into creating the campaign: planning, implementation, and measurement.
Here are some of the top advantages and disadvantages of doing market research and crafting market research reports.
- Identify business opportunities – A market research report can be used to analyze potential markets and new products. It can give information about customer needs, preferences, and attitudes. Also, it compare products and services.
- A clear understanding of your customers – A market report gives company’s marketing department an in-depth picture about customers’ needs and wants. This knowledge can be used to improve products, prices, and advertising.
- Mitigates risks – 30% of small businesses fail within the first two years. Why is this so? The answer is that entrepreneurs are risk takers. However, there are risks that could be avoided. A good marketing research will help you identify those risks and allow you to mitigate them.
- Clear data-driven insights – Market research encompasses a wide range of activities, from determining market size and segment to forecasting demand, and from identifying competitors to monitoring pricing. All of these are quantified and measurable which means that gives you a clear path for building unique decisions based on numbers.
- It’s not cheap – Although market research can be done for as little as $500, large markets like the United States can run into millions of dollars. If a research is done for a specific product, the budget may be even much higher. The budget also depends on the quality of the research. The more expensive it is, the more time the research will take.
- Some insights could be false – For example, if you are conducting a survey, data may be inadequate or inaccurate because respondents can, well, simply be dishonest and lie.
Here are the essential steps you need to take when doing market research:
Define your buyer persona
Identify a persona group to engage, prepare research questions for your market research participants, list your primary competitors, summarize your findings.
The job of a marketing persona is to describe your ideal customer and to tell you what they want, what motivates them, what frustrates them, and what limits them. Finding out these things means you have a better chance of designing your products, services, marketing messages, and brand around real customers. There is no one right way to create a buyer persona, though.
For example, if you’re in an industry focused on education, you could include things like:
- Educational level
- Education background
It’s recommended that you create 3-5 buyer personas for your products, based on your ideal customer.
This should be a representative sample of your target customers so you can better understand their behavior. You want to find people who fit both your target personas and who represent the broader demographic of your market. People who recently made a purchase or purposefully decided not to make one are a good sample to start with.
The questions you use determine the quality of your results. Of course, the quality of your results also depends on the quality of your participants.
Don’t ask questions that imply a yes or no answer. Instead, use open questions. For example, if you are researching customers about yogurt products, you could ask them: „ What have you heard about yogurt ?” or “ What do you think of yogurt ?“.
Avoid questions that use numbers, such as “ How many times a week do you eat yogurt ?”
Avoid questions that suggest a set of mutually exclusive answers, such as “ Do you like yogurt for breakfast, lunch, or dinner ?”
Avoid questions that imply a scale, such as “ Do you like chocolate-flavored yogurt ?”
Market researchers sometimes call one company the top competitor, another middle competitor, and the third one small competitor. However you classify them, you want to identify at least three companies in each category. Now, for each business on your list, list its key characteristics. For example, if your business sells running shoes, a key characteristic might be the product’s quality.
Next, make a list of your small business’s competitive advantages. These include the unique qualities or features of your business that make it the best choice of customers for the products or services it offers. Make a list of these competitive advantages and list them next to the key characteristics you listed for your business.
You have just finished writing your marketing research report. Everything is out there quantified or qualified. You just have to sum it up and focus on the most important details that are going to make a big impact on your decisions. Clear summary leads to a winning strategy!
Related : How to Prepare a Complete Marketing Report: The KPIs, Analysis, & Action Plan You Need
Here’s how to prepare a market research report in 5 simple steps:
Step 1: Cluster the data
Step 2: prepare an outline, step 3: mention the research methods, step 4: include visuals with narrative explanations, step 5: conclude the report with recommendations.
Your first step is to cluster all the available information into a manageable set. Clustering is the process of grouping information together in a way that emphasizes commonalities and minimizes differences. So, in market research, this will help to organize all the information you have about a product, service, or target market and identify your focus areas.
A marketing research report should be written so that other people can understand it:
- Include background information at the beginning to explain who your audience is and what problem you are trying to solve for them.
- In the body of the report, include a description of the methodology – Explain to the reader how your research was done, what was involved, and why you selected the methodology you used.
- Also in the body of the report, include the results of your market research. These may be quantitative or qualitative, but either way they should answer the questions you posed at the beginning.
- Include the executive summary – A summary of the entire report.
The market research methodology section includes details on the type of research, sample size, any limitations of the studies, research design, sample selection, data collection procedures, and statistical analyses used.
Visuals are an essential part of the presentation. Even the best-written text can be difficult to understand. Charts and graphs are easier to understand than text alone, and they help the reader see how the numbers fit the bigger picture.
But visuals are not the whole story. They are only one part of the presentation. Visuals are a cue for the reader. The narrative gives the story, not just the numbers.
Recommendations tend to follow logically from conclusions and are a response to a certain problem. The recommendation should always be relevant to the research rationale, that is, the recommendation should be based on the results of the research reported in the body of the report.
Now, let’s take a look at some dashboard reporting templates you could use to enhance your market research:
- Semrush (Position Tracking) Report
Brand Awareness Report
Sales pipeline performance report, customer success overview report, stripe (mrr & churn) report, semrush (position tracking) report template.
This free SEMRush dashboard template will help you monitor how your website’s search visibility on search engines evolves on a monthly basis. This dashboard contains all of the information you need to make changes and improve the ranking results of your business in Google Search.
This Brand Awareness Report will help you to get a sense of your brand awareness performance in Google Analytics, Google Organic Search, and Facebook. Use this dashboard to track brand awareness the same way you track other marketing campaigns.
Are your sales and marketing funnel healthy and growing? How is your sales and marketing funnel performing? What are the key conversion rates between your lifecycle stages? With a pipeline performance dashboard , you’ll get all of the answers quickly.
This Customer Success Overview Dashboard allows you to analyze how your customer service team’s responsiveness impacts your business. Use this dashboard to assess the correlation between your customer service performance and churn rate.
This Stripe dashboard tracks your churn rate and MRR growth in real-time and shows you which customers (and how many of them) you have at any given point in time. All you have to do to get started is to connect your Stripe account.
As we said earlier, there are no strict rules when it comes to writing marketing research reports. On the other hand, you must find your focus if you want to write a report that will make a difference. Here are some best practices you should keep in mind when writing a research report.
- Objectives – The objective of a market research report is to define the problems, identify key issues, and suggest recommendations for further research. If you answer them successfully, you’re on the right way.
- Don’t worry about the format – Be creative. The report could be in a form of a PowerPoint presentation, Excel sheet, interactive dashboard or even a video. Use the format that best fits your audience, but make sure to make it easy to read.
- Include an executive summary, scorecard , or a dashboard – This is really important because time is money, and most people don’t have time to waste. So, how to put everything important in a short role? Address all of the objectives and put them in a graphic dashboard or scorecard. Also, you can write an executive summary (heart of the report) that can be easily read by managers or CEOs.
- Use storytelling – A good story always makes a great point because it’s so memorable. Your research report results can double the effect with a catchy story.
- Keep it short – It’s not a secret that we are reading so little in the digital era. Use a lot of white space and bullet points. Too much text on a page means less focus for the reader.
- Be organized – Maintain the order of information. It’s important for the reader to navigate through the report easily. If they want to find some details or specific information it would be great to divide all sections with appropriate references.
- Methodological information – Methodological details could be boring. Include only the most important details that the reader needs to know to understand the big picture.
- Use images (or other visualizations) whenever you can – A good picture speaks for 1.000 words! If you can communicate the point visually, don’t hesitate to do it. It would be a lot easier for those who don’t like a lot of text to understand your results. But don’t push them where you can’t.
- Create readable graphs – The crown of marketing research reports is a comprehensive graph. Make sure to design precise and attractive graphs that will power up and round your story.
- Use the Appendix – You can include all secondary information such as methodological details and other miscellaneous data in the Appendix at the end of the report.
Market research reports are all about presenting your data in an easy-to-understand way and making calculated decisions about business ideas. But this is something easier said than done.
When busy stakeholders and executives grab a report, they need something that will give them an idea of the results – the big picture that addresses company wide-business goals.
Can a PowerPoint presentation or a PDF report meet those expectations? Most likely not. But a dashboard can.
Keep in mind that even with the best market analysis in the world, your market research report won’t be actionable if you don’t present the data efficiently and in a way that everyone understands what the next steps are. Databox is your key ally in the matter.
Databox dashboards are designed to help you present your market research data with clarity – from identifying what is influencing your business, and understanding where your brand is situated in the market, to gauging the temperature of your niche or industry before a new product/service launch.
Present your research results with efficient, interactive dashboards now by signing up for a free trial .
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Perfume Market Analysis, Size, Trends, Share by Brands and Research Report 2023-2028
IMARC Group, a leading market research company, has recently released a report titled “ Perfume Market: Global Industry Trends, Share, Size, Growth, Opportunity and Forecast 2023-2028 .” The study provides a detailed analysis of the industry, including the global perfume market size, share, trends, and growth forecasts. The report also includes competitor and regional analysis and highlights the latest advancements in the market.
How Big is the Perfume Market?
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What is Perfume?
Perfume is a fragrant liquid substance created to produce a pleasant aroma when applied to the skin or clothing. It comprises a blend of essential oils, solvents, and fixatives and it is engineered to evoke various sensory experiences. Perfumes are often categorized into different fragrance families, such as floral, fruity, oriental, woody, or fresh. They are widely manufactured using flower extracts, wood, and spices, along with synthetic compounds that mimic natural scents. They assist in improving self-confidence, enhancing attractiveness, and creating a personal scent identity.
What are the Growth Prospects and Trends in the Perfume Industry?
The increasing demand for luxury perfumes due to the Rising disposable income and growing urbanization is one of the primary factors contributing to the market growth. In addition to this, high-profile endorsements and strategic partnerships with fashion icons and celebrities lend prestige to perfume brands, thereby stimulating the demand. Furthermore, companies are constantly innovating, offering new fragrances, limited editions, and even personalized scent experiences, enticing consumers to expand their perfume collection. Apart from this, the increasing number of artisanal and niche perfumes targeting specific consumer preferences, such as organic or cruelty-free options, is propelling the market growth. Moreover, the launch of as various holiday gifting and summer fragrance options is positively influencing the market growth. Other factors, such as an increase in travel, changing consumer preferences and fashion trends, growing marketing and promotional activities, easy product availability across e-commerce and online retailing platforms, increasing focus on personal grooming and self-care, and various product innovations, are anticipated to drive the market towards growth.
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Note: In this report you will get detailed analysis of the market, growth analysis graphs, historical period analysis, forecast period analysis, major market segmentation, top leading key players of the market, table of content, list of figures, and list of tables.
Who are the Key Players Operating in the Perfume Market?
The competitive landscape of the market has also been examined with some of the key players.
- Avon Products Inc.
- Natura Cosméticos SA
- L'Oréal SA.
What is Included in Market Segmentation?
The report has categorized the market based on perfume type, category, distribution channel and region.
Breakup by Perfume Type:
- Premium Perfume Products
- Mass Perfume Products
Based on Category:
- Female Fragrances
- Male Fragrances
- Unisex Fragrances
Breakup by Distribution Channel:
- Speciality stores
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- United States
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