Skip to main content
SpringerLink
Log in

Driving Customer Analytics From the Top

  • Research Article
  • Published:
Customer Needs and Solutions Aims and scope Submit manuscript

A Correction to this article was published on 09 December 2020

This article has been updated

Abstract

Customer analytics has moved to center stage and customer analytics budgets are rising rapidly. It is surprising, then, that many chief marketing officers (CMOs) are uncertain about whether these investments improve firm performance. We address this apparent disconnection using a multi-method approach. Using a large-scale global survey at two points in time, we first show that the use of customer analytics is positively related to firm performance. Second, from among a set of factors identified in the literature, we show that top management team (TMT) advocacy of customer analytics is by far the most important one influencing the degree to which firms use customer analytics. Third, we find that TMT advocacy is also critical for ensuring that customer analytics use results in a positive payoff—an outcome that remains elusive in many firms. Finally, we report on a set of in-depth interviews that offer insight into why TMT advocacy plays such an influential role and identify steps TMTs should take to facilitate the use of customer analytics in their firms.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

Similar content being viewed by others

Change history

  • 09 December 2020

    A Correction to this paper has been published: <ExternalRef><RefSource>https://doi.org/10.1007/s40547-020-00113-6</RefSource><RefTarget Address="10.1007/s40547-020-00113-6" TargetType="DOI"/></ExternalRef>

Notes

  1. Researchers have used the terms customer analytics and marketing analytics interchangeably. We use customer analytics throughout except when quoting a specific researcher who uses the marketing analytics term. Customer analytics can arise from any source, including digital, social, mobile, retail, sales, and/or other secondary data (see Wedel and Kannan [2016, p. 97] for key domains of customer analytics applications).

  2. Companies use customer analytics for different purposes. Table 8 in the Appendix indicates, by industry, why our sample firms use customer analytics.

  3. There are several management treatises that have addressed some of the issues we address here. For example, Davenport and Harris [16] argue that using analytics has a positive impact on firm performance and also propose that senior management plays an important role in the adoption of analytics in firms. However, their findings and others of its type are not peer-reviewed and are largely based on anecdotes and opinions.

  4. The rapid imitation of hardware and software features in the smartphone industry provides one highly visible example. See for example: https://bgr.com/2018/04/18/iphone-x-plus-vs-android-p-notch-copycats-everywhere-soon-outdated/

  5. An SBU is an independent entity within a larger parent company that usually has its own marketing department and that is often big enough to have its own support functions such as human resources etc.

  6. We used the following items to measure firm use of customer analytics (on a 1–7 scale): (1) In our firm/business unit, we extensively use customer analytics; (2) virtually everyone in our firm/business unit uses customer analytics–based insights to support decisions; (3) when making decisions, we back arguments with customer analytics–based facts. Also, we used the following items to measure firm performance (on a 1–7 scale): Please describe the performance of your firm/business unit in the following areas relative to your average competitor (consider the immediate past year in responding to these items)—(1) total sales growth, (2) profit, (3) return on investment.

  7. We introduced a new variable corresponding to the potentially endogenous independent variable—customer analytics use—in Eq. 2 (i.e., control function correction). After accounting for the influence of the control function correction on firm performance, the endogenous independent variable should no longer correlate with the error term in Eq. 2.

  8. As part of the survey, we asked respondents to report the degree to which “customer analytics is used extensively in our [their] industry (on a 1–7 scale).” We used this variable as the excluded variable in the models with control function.

  9. Use of the focal, endogenous variable in the same industry is commonly used as an excluded variable. The identifying assumption is that industry levels of the focal variable are unaffected by firm-level idiosyncratic performance changes or shocks and hence should not correlate strongly with the residual in Eq. 2 (e.g., [28]). In our case, the industry average represents the collective view on whether customer analytics should be used. Hence, while a firm’s competitor’s analytics use might directly impact a firm’s performance, it is unlikely that the industry’s average customer analytics use directly correlates with the focal firm’s performance. However, we do expect that a firm’s use of customer analytics is correlated with the firm’s respective industry’s average customer analytics use (e.g., [22]).

  10. Sande and Ghosh [66] note that in survey research, when you have panel data and think that you might have an endogeneity problem, if you “deal with it using fixed effects or time series models…..you probably do not have an endogeneity problem.” (also see [63]).

  11. Many respondents completed the survey in reference to their SBU and/or worked for firms that were not publicly traded, making it difficult to obtain objective performance data. Moreover, reliable objective performance data was difficult to obtain for firms in some regions. In addition, some respondents wished to remain anonymous. Hence, this procedure yielded objective ROA data for only 61 of the 1118 sample observations.

  12. We used the following question to capture customer analytics spending (on a 1–7 scale): “Considering the immediate past year, what percentage of your firm’s/business unit’s overall marketing budget (not including sales force expenditures) was spent on customer analytics.”

  13. We used the following question to capture percent customer analytics use (on a 1–7 scale): “In what percent of projects does your company use available or requested customer analytics before a decision is made.”

  14. Building on Germann et al. [26], we conceptualize TMT advocacy of customer analytics use as the TMT’s support, involvement in, and expectation of customer analytics use in the firm.

  15. Some firms (e.g., Adore Me, Google, Netflix) were started with the goal to use customer analytics as their basis of competition. Hence, for these firms, organizational change is not needed.

  16. We used the following items to measure TMT advocacy (on a 1–7 scale): (1) Our top management expects customer analytics-based insights to support important marketing and sales decisions; (2) our top management has a favorable attitude towards using the results of customer analytics to inform their decisions; (3) to what extent is top management involved in customer analytics initiatives (also see Appendix Table 9).

  17. Single-item measures are most appropriate when participants are busy and hence likely dismissive of and aggravated by multi-item scales that seem redundant (e.g., [24, 78]). Researchers have shown that appropriate single-item measures demonstrate excellent psychometric properties (e.g., [5, 24]).

  18. We also investigated the TMT’s role as both a driver of customer analytics use and as a moderator of customer analytics use’s effect on firm performance (i.e., moderated mediation) using Hayes model 74. Our conclusions remained the same.

References

  1. Ackoff RL (1989) From data to wisdom. J Appl Syst Anal 16(1):3–9

    Google Scholar 

  2. Ailawadi KL, Harlam BA, César J, Trounce D (2007) Quantifying and improving promotion effectiveness at CVS. Mark Sci 26(4):566–575

    Google Scholar 

  3. Argote L (2013) Organizational learning: creating. Retaining and Transferring Knowledge, Springer Science & Business Media

    Google Scholar 

  4. Baker WE, Sinkula JM (1999) The synergistic effect of market orientation and learning orientation on organizational performance. J Acad Mark Sci 27(4):411–427

    Google Scholar 

  5. Bergkvist L, Rossiter JR (2007) The predictive validity of multiple-item versus single-item measures of the same constructs. J Mark Res 44(3):175–184

    Google Scholar 

  6. Bharadwaj AS, Bharadwaj SG, Konsynski BR (1999) Information technology effects on firm performance as measured by Tobin’s q. Manag Sci 45(7):1008–1024

    Google Scholar 

  7. Brynjolfsson, E., Hitt, L. M. & Kim H. H. (2011). Strength in numbers: how does data-driven decision making affect firm performance? Working Paper

    Google Scholar 

  8. Carpenter MA, Geletkanycz MA, Sanders WG (2004) Upper echelons research revisited: antecedents, elements, and consequences of top management team composition. J Manag 30(6):749–778

    Google Scholar 

  9. Challagalla G, Murtha BR, Jaworski B (2014) Marketing doctrine: a principles-based approach to guiding marketing decision making in firms. J Mark 78(4):4–20

    Google Scholar 

  10. Churchill GA Jr (1979) A paradigm for developing better measures of marketing constructs. J Mark Res 16(1):64–73

    Google Scholar 

  11. Coff RW (2002) Human capital, shared expertise, and the likelihood of impasse in corporate acquisitions. J Manag 28(1):107–128

    Google Scholar 

  12. Curren MT, Folkes VS, Steckel JH (1992) Explanations for successful and unsuccessful marketing decisions: the decision maker’s perspective. J Mark 56(2):18–31

    Google Scholar 

  13. Dahl MS (2011) Organizational change and employee stress. Manag Sci 57(2):240–256

    Google Scholar 

  14. Damanpour F (1991) Organizational innovation: a meta-analysis of effects of determinants and moderators. Acad Manag J 34(3):555–590

    Google Scholar 

  15. Danaher PJ, Roberts JH, Roberts K, Simpson A (2011) Applying a dynamic model of consumer choice to guide brand development at Jetstar airways. Mark Sci 30(4):586–594

    Google Scholar 

  16. Davenport TH, Harris JG (2007) Competing on analytics: the new science of winning. Harvard Business Press

  17. Davis FD (1989) Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q 13(3):319–340

    Google Scholar 

  18. Davis-Sramek B, Germain R, Iyer K (2010) Supply chain technology: the role of environment in predicting performance. J Acad Mark Sci 38(1):42–55

    Google Scholar 

  19. Deshpandé R, Farley JU (2004) Organizational culture, market orientation, innovativeness, and firm performance: an international research odyssey. Int J Res Mark 21(1):3–22

    Google Scholar 

  20. Deshpandé, R., & Zaltman, G. (1982). Factors affecting the use of market research information: a path analysis. J Mark Res 19(1):14–31

  21. Devaraj S, Kohli R (2003) Performance impacts of information technology: is actual usage the missing link? Manag Sci 49(3):273–289

    Google Scholar 

  22. DiMaggio PJ, Powell WW (1983) The iron cage revisited: institutional isomorphism and collective rationality in organizational fields. Am Sociol Rev 48(2):147–160

    Google Scholar 

  23. Donaldson L (2001) The contingency theory of organizations. Sage Publications

  24. Drolet AL, Morrison D (2001) Do we really need multiple-item measures in service research? J Serv Res 3(3):196–204

    Google Scholar 

  25. Gebhardt GF, Carpenter GS, Sherry JF Jr (2006) Creating a market orientation: a longitudinal, multifirm, grounded analysis of cultural transformation. J Mark 70(4):37–55

    Google Scholar 

  26. Germann F, Lilien GL, Rangaswamy A (2013) Performance implications of deploying marketing analytics. Int J Res Mark 30(2):114–128

    Google Scholar 

  27. Germann F, Ebbes P, Fiedler L, Kraus M (2014) Do retailers benefit from deploying customer analytics? J Retail 90(4):587–593

    Google Scholar 

  28. Germann F, Ebbes P, Grewal R (2015) The chief marketing officer matters! J Mark 79(3):1–22

    Google Scholar 

  29. Glazer R (1991) Marketing in an information-intensive environment: strategic implications of knowledge as an asset. J Mark 55(4):1–19

    Google Scholar 

  30. Grant RM (1996) Toward a knowledge-based theory of the firm. Strateg Manag J 17(S2):109–122

    Google Scholar 

  31. Gupta S, Lehmann DR (2003) Customers as assets. J Interact Mark 17(1):9–24

    Google Scholar 

  32. Hambrick DC (2007) Upper echelons theory: an update. Acad Manag Rev 32(2):334–343

    Google Scholar 

  33. Hambrick DC, Mason PA (1984) Upper echelons: the organization as a reflection of its top managers. Acad Manag Rev 9(2):193–206

    Google Scholar 

  34. Hayes AF (2013) Introduction to mediation, moderation, and conditional process analysis: a regression-based approach. Guilford Press

  35. Hitt MA, Bierman L, Shimizu K, Kochhar R (2001) Direct and moderating effects of human capital on strategy and performance in professional service firms: a resource-based perspective. Acad Manag J 44(1):13–28

    Google Scholar 

  36. Jaworski, B., Malcom, R., & Morgan, N. (2016). The 7 big problems in the marketing industry. (accessed March 7, 2017), [available at https://www.ama.org/publications/MarketingNews/Pages/7-big-problems-marketing.aspx]

  37. Johnson PO, Fay LC (1950) The Johnson-Neyman technique, its theory and application. Psychometrika 15(4):349–367

    Google Scholar 

  38. Kirca AH, Jayachandran S, Bearden WO (2005) Market orientation: a meta-analytic review and assessment of its antecedents and impact on performance. J Mark 69(2):24–41

    Google Scholar 

  39. Kleinbaum, D., Kupper, L., Nizam, A., & Muller, K. (1998). Applied regression analysis and other multivariable methods, Cengage Learning

    Google Scholar 

  40. Kohli AK, Jaworski BJ (1990) Market orientation: the construct, research propositions, and managerial implications. J Mark 54(2):1–18

    Google Scholar 

  41. Kotler, P. & Keller, K. L. (2016). Marketing management, Pearson

    Google Scholar 

  42. Kumar N, Stern LW, Anderson JC (1993) Conducting interorganizational research using key informants. Acad Manag J 36(6):1633–1651

    Google Scholar 

  43. Kumar V, Bhaskaran V, Mirchandani R, Shah M (2013) Creating a measurable social media marketing strategy: increasing the value and ROI of intangibles and tangibles for Hokey Pokey. Mark Sci 32(2):194–212

    Google Scholar 

  44. Lilien GL (2011) Bridging the academic–practitioner divide in marketing decision models. J Mark 75(4):196–210

    Google Scholar 

  45. Lilien GL, Roberts JH, Shankar V (2013) Effective marketing science applications: insights from the ISMS-MSI practice prize finalist papers. Mark Sci 32(2):229–245

    Google Scholar 

  46. McKinsey & Co. (2016). Why customer analytics matter? (), [available at http://www.mckinsey.com/business-functions/marketing-and-sales/our-insights/why-customer-analytics-matter]

  47. Mellert LD, Scherbaum C, Oliveira J, Wilke B (2015) Examining the relationship between organizational change and financial loss. J Organ Chang Manag 28(1):59–71

    Google Scholar 

  48. Melville N, Kraemer K, Gurbaxani V (2004) Review: information technology and organizational performance: an integrative model of IT business value. MIS Q 28(2):283–322

    Google Scholar 

  49. Menon A, Varadarajan PR (1992) A model of marketing knowledge use within firms. J Mark 56(4):53–71

    Google Scholar 

  50. Mintz O, Currim IS (2013) What drives managerial use of marketing and financial metrics and does metric use affect performance of marketing-mix activities? J Mark 77(2):17–40

    Google Scholar 

  51. Mithas S, Krishnan MS, Fornell C (2005) Why do customer relationship management applications affect customer satisfaction? J Mark 69(4):201–209

    Google Scholar 

  52. Moorman C (1995) Organizational market information processes: cultural antecedents and new product outcomes. J Mark Res 32(3):318–335

    Google Scholar 

  53. Moorman C, Day GS (2016) Organizing for marketing excellence. J Mark 80(6):6–35

    Google Scholar 

  54. Nag R, Hambrick DC, Chen M-J (2007) What is strategic management, really? Inductive derivation of a consensus definition of the field. Strateg Manag J 28(9):935–955

    Google Scholar 

  55. Ng, S. (2013). Marketers track flu outbreaks closely,” (), [available at https://www.wsj.com/articles/marketers-track-flu-outbreaks-closely-1387763359]

  56. Ozkaya HE, Droge C, Hult GTM, Calantone R, Ozkaya E (2015) Market orientation, knowledge competence, and innovation. Int J Res Mark 32(3):309–318

    Google Scholar 

  57. Penrose ET (1959) The theory of the growth of the firm. John Wiley, New York

    Google Scholar 

  58. Peterson, H. & Lutz, A. (2015). Why Kroger is Americas most underrated grocery store,” (), [available at http://www.businessinsider.com/why-people-love-kroger-2015-3]

  59. Petrin A, Train K (2010) A control function approach to endogeneity in consumer choice models. J Mark Res 47(1):3–13

    Google Scholar 

  60. Podsakoff PM, MacKenzie SB, Lee J-Y, Podsakoff NP (2003) Common method biases in behavioral research: a critical review of the literature and recommended remedies. J Appl Psychol 88(5):879–903

    Google Scholar 

  61. Ransbotham S, Kiron D, Prentice PK (2016) Beyond the hype: the hard work behind analytics success. MIT Sloan Manag Rev 57(3):1–17

    Google Scholar 

  62. Rohan, S. (2017). Advanced analytics market. (), [available at http://www.marketsandmarkets.com/PressReleases/advanced-analytics.asp]

  63. Rossi PE (2014) Even the rich can make themselves poor: a critical examination of IV methods in marketing applications. Mark Sci 33(5):655–672

    Google Scholar 

  64. Ruekert RW (1992) Developing a market orientation: an organizational strategy perspective. Int J Res Mark 9(3):225–245

    Google Scholar 

  65. Rust RT, Moorman C, Dickson PR (2002) Getting return on quality: revenue expansion, cost reduction, or both? J Mark 66(4):7–24

    Google Scholar 

  66. Sande JB, Ghosh M (2018) Endogeneity in survey research. Int J Res Mark 35(2):185–204

    Google Scholar 

  67. Shankar V, Azar P, Fuller M (2008) BRAN* EQT: a multicategory brand equity model and its application at Allstate. Mark Sci 27(4):567–584

    Google Scholar 

  68. Short SE, Short MW (2005) Essay: role of the coach-athlete relationship. Lancet 366:S29–S30

    Google Scholar 

  69. Sinha A, Sahgal A, Mathur SK (2013) Category optimizer: a dynamic-assortment, new-product-introduction, mix-optimization, and demand-planning system. Mark Sci 32(2):221–228

    Google Scholar 

  70. Sinkula JM, Baker WE, Noordewier T (1997) A framework for market-based organizational learning: linking values, knowledge, and behavior. J Acad Mark Sci 25(4):305–318

    Google Scholar 

  71. Spender J-C (1996) Making knowledge the basis of a dynamic theory of the firm. Strateg Manag J 17(S2):45–62

    Google Scholar 

  72. Srinivasan R, Lilien GL, Sridhar S (2011) Should firms spend more on research and development and advertising during recessions? J Mark 75(3):49–65

    Google Scholar 

  73. Srivastava RK, Shervani TA, Fahey L (1998) Market-based assets and shareholder value: a framework for analysis. J Mark 62(1):2–18

    Google Scholar 

  74. The CMO Survey (2018). Highlights and insight report--February. (), [available at https://cmosurvey.org/results/february-2018/]

  75. The CMO Survey (2019). Highlights and insight report—August. (), [available at https://cmosurvey.org/results/august-2019/]

  76. Thompson JD (1967) Organizations in action: social science bases of administrative theory. Transaction Publishers

  77. Verhoef PC, Leeflang PSH (2009) Understanding the marketing department’s influence within the firm. J Mark 73(2):14–37

    Google Scholar 

  78. Wanous JP, Reichers AE, Hudy MJ (1997) Overall job satisfaction: how good are single-item measures? J Appl Psychol 82(2):247–252

    Google Scholar 

  79. Wedel M, Kannan PK (2016) Marketing analytics for data-rich environments. J Mark 80(6):97–121

    Google Scholar 

  80. Wernerfelt B (1984) A resource-based view of the firm. Strateg Manag J 5(2):171–180

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. University of Notre Dame, 395 Mendoza College of Business, Notre Dame, IN, 46556, USA

    Frank Germann

  2. Smeal College of Business, Pennsylvania State University, 468 Business Building, University Park, PA, 16802, USA

    Gary L. Lilien

  3. Fuqua School of Business, Duke University, 100 Fuqua Drive, Box 90120, Durham, NC, 27708, USA

    Christine Moorman

  4. McKinsey & Company, Inc., Am Sandtorkai 77, 20457, Hamburg, Germany

    Lars Fiedler

  5. McKinsey & Company, Inc., Kennedydamm 24, 40027, Düsseldorf, Germany

    Till Groβmaβ

Authors
  1. Frank Germann
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Gary L. Lilien
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Christine Moorman
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Lars Fiedler
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Till Groβmaβ
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Frank Germann.

Ethics declarations

Conflict of Interest

Gary Lilien is President of DecisionPro, a marketing analytics consultancy. Lars Fiedler and Till Grossmass are Partners at McKinsey & Co., a global top management consulting firm, and regularly work on client projects that involve implementing customer analytics solutions. All other authors declare no conflicts of interests.

Disclaimer

The views expressed in the paper are those of the authors and do not represent those of any organization.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

ESM 1

(DOCX 58 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Germann, F., Lilien, G.L., Moorman, C. et al. Driving Customer Analytics From the Top. Cust. Need. and Solut. 7, 43–61 (2020). https://doi.org/10.1007/s40547-020-00109-2

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40547-020-00109-2

Keywords

Search

Navigation