Driving Customer Analytics From the Top

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.

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Fig. 1

Notes

  1. 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. 2.

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

  3. 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 However, their findings and others of its type are not peer-reviewed and are largely based on anecdotes and opinions.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. 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 See for example: https://bgr.com/2018/04/18/iphone-x-plus-vs-android-p-notch-copycats-everywhere-soon-outdated/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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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.

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Correspondence to Frank Germann.

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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.

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

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Keywords

  • Customer analytics
  • Top management team advocacy
  • Firm performance
  • Knowledge utilization