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To most senior managers, growth is the engine of prosperity and success. Growing companies flourish; shrinking companies die. Good managers understand that the road to growth runs through customers — not just attracting new customers, but holding on to the ones you have, motivating them to spend more and getting them to recommend your products and services to the people they know. However, it is one thing to believe that customers are the driving force for profitable growth. It’s altogether different to know how to measure and manage the customer relationship effectively. Using the right customer metrics is essential to assessing and monitoring how companies deliver for customers and determining customers’ new and unmet needs.
Most companies do a relatively poor job of managing their relationship with their customers. It isn’t that they don’t care, but rarely do they have any insightful information they can act upon to make the relationship more valuable. As a result, understanding how customers perceive the relationship and anticipating what they will do is typically no more reliable than reading tea leaves.
In a world where managers are looking to analytics to help clarify their most critical decisions, this presents a challenge: How do managers measure how customers really feel and what they are likely to do? More importantly, what impact can this information have on the business?
Most companies lack good information about their customers — for example, most do not have good customer databases and, of those that do, almost none tie customer survey information to customer behavior information. Therefore, it is not surprising that growth is often so unpredictable, leaving managers scrambling for useful ways to measure their customer relationships so they can predict how customers will behave — and how successful their business will be in the market.
In recent years, researchers have advanced a number of customer metrics to illustrate the connections between customer behavior and growth. In the harsh reality of the marketplace, however, these efforts have generated more smoke than heat. The best metrics have shown only modest correlations to growth, and none of them have shown themselves to be universally effective across all competitive environments. But the failures and weaknesses of existing metrics have not discouraged company managers from adopting new ones.
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6. For example, B. Cooil, T.L. Keiningham, L. Aksoy and M. Hsu, “A Longitudinal Analysis of Customer Satisfaction and Share of Wallet: Investigating the Moderating Effect of Customer Characteristics,” Journal of Marketing 71, no. 1 (2007): 67-83; T.L. Keiningham, T. Perkins-Munn and H. Evans, “The Impact of Customer Satisfaction on Share of Wallet in a Business-to-Business Environment,” Journal of Service Research 6, no. 1 (2003): 37-50; and V. Mittal and W. Kamakura, “Satisfaction, Repurchase Intent and Repurchase Behavior: Investigating the Moderating Effect of Customer Characteristics,” Journal of Marketing Research 38, no. 1 (February 2001): 131-142.
7. Aksoy, B. Cooil, C. Groening, T.L. Keiningham and A. Yalçin, “The Long Term Stock Market Valuation of Customer Satisfaction,” Journal of Marketing, in press; E.W. Anderson, C. Fornell and S.K. Mazvancheryl, “Customer Satisfaction and Shareholder Value,” Journal of Marketing 68, no. 4 (October 2004): 172-185; C. Fornell, S. Mithas, F. V. Morgeson III and M.S. Krishnan, “Customer Satisfaction and Stock Prices: High Returns, Low Risk,” Journal of Marketing 70, no. 1 (January 2006): 3-14; and T.S. Gruca and L.L. Rego, “Customer Satisfaction, Cash Flow and Shareholder Value,” Journal of Marketing 69, no. 3 (July 2005): 115-130.
8. T.L. Keiningham and T. Vavra, “The Customer Delight Principle” (New York: McGraw-Hill, 2001), 42-43.
9. F.F. Reichheld, “The One Number You Need to Grow,” Harvard Business Review 81, no. 12 (December 2003): 46-54.
10. F.F. Reichheld, “The Microeconomics of Customer Relationships,” MIT Sloan Management Review 47, no. 2 (winter 2006): 73-78; and F. F. Reichheld, “The Ultimate Question: Driving Good Profits and True Growth” (Boston: Harvard Business School Press, 2006).
11. T.L. Keiningham, B. Cooil, T.W. Andreassen and L. Aksoy, “A Longitudinal Examination of Net Promoter on Firm Revenue Growth,” Journal of Marketing 71, no. 3 (July 2007): 39-51; and T.L. Keiningham, B. Cooil, L. Aksoy, T.W. Andreassen and J. Weiner, “The Value of Different Customer Satisfaction and Loyalty Metrics in Predicting Customer Retention, Recommendation and Share-of-Wallet,” Managing Service Quality 17, no. 4 (2007): 361-384.
12. In examining the linkage to customer behavior (retention, share of wallet and recommendations), we examined the following variables: share of wallet, recommend intention, repurchase intention, overall satisfaction, worth what paid, expectations, brand preference, trend in total spend/savings in category and trend in spending/savings with individual company. In examining the linkage to company growth, we examined satisfaction, repurchase intention, recommend intention, the Norwegian Customer Satisfaction Barometer and the American Customer Satisfaction Index.
13. Keiningham, Cooil, Aksoy, Andreassen and Weiner, “Value.”
14. We looked at banking, gas stations, home furnishings retailers, security systems and transportation.
15. This research is reported in Keiningham, Cooil, Andreassen and Aksoy, “Longitudinal Examination.”
16. In the Harvard Business Review article, Reichheld wrote: “Our research indicates that satisfaction lacks a consistently demonstrable connection to actual customer behavior and growth. This finding is borne out by the short shrift that investors give to such reports as the American Customer Satisfaction Index. The ACSI, published quarterly in the Wall Street Journal, reflects customer satisfaction ratings of some 200 U.S. companies. In general, it is difficult to discern a strong correlation between high customer satisfaction scores and outstanding sales growth.” Furthermore, in a Web-based presentation (http://resultsbrief.bain.com/videos/0402/main.html), Reichheld states that a “Bain team looked at the correlation between growth and customer satisfaction, and found there is none.”
17. Reichheld, “Ultimate Question.”
18. In statistics, the R-square (coefficient of determination) is the proportion of variability in a data set that is accounted for by a statistical model. In layman’s terms, it is a measure of how well a model fits the data, and can range between 0 (no fit) and 1 (perfect fit). All things being equal, higher R-square values indicate better fitting models.
19. Aksoy, Cooil, Groening, Keiningham and Yalçin, “Long Term Stock Market”; Anderson, Fornell and Mazvancheryl, “Customer Satisfaction”; Fornell, Mithas, Morgeson and Krishnan, “Customer Satisfaction”; and Gruca and Rego, “Customer Satisfaction.”