Abstract
Customer value management rests on the idea of allocating resources differently according to the characteristics of different customers. The basis of this differential resource allocation is the economic value of the respective customer to the firm. Thus, before one can start to manage customers, one must have developed a thorough understanding of how to compute the value contribution each customer makes to a firm. Various economic concepts and procedures have been developed that help us to achieve this. Chapter 5 proceeds to conceptualize strategic metrics of customer value and introduces popular customer selection strategies and techniques to evaluate these strategies.
Notes
- 1.
All numerical figures mentioned in the discussions below are hypothetical data created for instructional purposes only. However, due care has been exercised to ensure these data are fairly close to real life experiences of many firms.
- 2.
- 3.
- 4.
Further illustrations and a practical application of the concept of CRV can be found in Kumar et al. (2007)
- 5.
If Xi is not binary one can find an optimal (in the sense that it best separates Y on the basis of classification of Xi) cutoff point to divide the domain of Xi in two parts and thus reduce Xi to a binary variable. For a further discussion see Hastie, Tibshirani, and Friedman (2009)
- 6.
A discussion of further optimal splitting rules can be found in Blattberg, Kim, and Neslin (2008).
References
Aaker, D. A., Kumar, V., & Day, G. S. (2003). Marketing research (8th ed.). New York: John Wiley & Sons.
Berger, P. D., & Nasr, N. (1998). Customer lifetime value: Marketing models and applications. Journal of Interactive Marketing, 12(1), 17–30.
Blattberg, R., Kim, B. D., & Neslin, S. A. (2008). Database marketing – Analyzing and managing customers. New York: Springer. 427ff.
Hastie, T., Tibshirani, R., & Friedman, J. (2009). Elements of statistical learning – Data mining, inference, and prediction (2nd ed.). Stanford: Springer.
Jackson, D. R. (1992). In quest of the grail: Breaking the barriers to customer valuation. Direct Marketing, 54(11), 44–48.
Jain, D., & Singh, S. (2002). Customer lifetime value research in marketing: A review and future directions. Journal of Interactive Marketing, 16(2), 34–46.
Kiss, C., & Bichler, M. (2008). Identification of influencer – Measuring influence in customer networks. Decision Support Systems, 46(1), 233–253.
Kumar, V., Petersen, J. A., & Leone, R. P. (2007). How valuable is word of mouth. Harvard Business Review, 85(10), 139–146.
Lee, S. H., Catte, J., & Noseworthy, T. J. (2010). The role of network centrality in the flow of consumer influence. Journal of Consumer Psychology, 20(1), 66–77.
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Appendices
Appendix I
1.1 Notation Key
Notation | Explanation |
---|---|
a | Coefficient of acquisition |
AC | Acquisition costs |
ACS | Acquisition costs savings |
Ar | Acquisition rate |
c | Category |
CE | Customer equity |
Dr | Defection rate |
GC | Gross contribution |
i | Individual customer |
I | Total number of buyers with a focal firm |
j | Firm |
J | Total number of firms in a market |
LTV | Lifetime value |
MC | Marketing costs |
n | Customer in cohort |
N | Cohort size |
r | Coefficient of retention |
Rr | Retention rate |
Rr c | Retention rate ceiling |
S | Sales (value) |
Sr | Survival rate |
t | Time period |
T | Length of time horizon |
V | Sales (volume) |
δ | Applicable discount rate |
Appendix II
2.1 Regression Scoring Models
Scoring models is the process of evaluating potential customer behavior on the basis of test results. Typically, a test is conducted in a limited market or in an experimental set up on a small subset of customers. This subset of customers is exposed to a marketing campaign and a product offering. The purpose of this test is to assign to each of the remaining customers a value which is extrapolated from the results of this test. These values typically reflect the prospective customer’s likelihood of purchasing the test marketed product. The process of regression scoring can be represented in the following steps:
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1.
Draw a random sample from the overall population of prospective customers.
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2.
Obtain data from the sample that profiles individual consumer characteristics. The R, F, and M scores are variables which profile behavioral characteristics of a customer and are typically used in this procedure, along with other relevant variables.
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3.
Initiate a marketing campaign directed at the random sample, and record the individuals who become customers.
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4.
With that information, develop a regression scoring model to obtain a series of weighted variables that either predict which prospects are more likely to become customers or the value of profits that each customer is likely to provide, based on their individual characteristics.
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5.
By applying these weights to individual characteristics of prospective customers, we can arrive at a value for each customer which indicates how likely it is that the customer will purchase a product, or how much profit the customer will generate, if exposed to the tested marketing campaign.
Appendix III
Cell # | RFM codes | Cost per mail ($) | Net profit per sale ($) | Breakeven (%) | Actual response (%) | Breakeven index |
---|---|---|---|---|---|---|
1 | 111 | 1 | 45 | 2.22 | 17.55 | 690 |
2 | 112 | 1 | 45 | 2.22 | 17.45 | 685 |
3 | 113 | 1 | 45 | 2.22 | 17.35 | 681 |
4 | 114 | 1 | 45 | 2.22 | 17.25 | 676 |
5 | 115 | 1 | 45 | 2.22 | 17.15 | 572 |
6 | 121 | 1 | 45 | 2.22 | 17.05 | 667 |
… | ||||||
52 | 312 | 1 | 45 | 2.22 | 12.91 | 481 |
53 | 313 | 1 | 45 | 2.22 | 0.98 | −56 |
54 | 314 | 1 | 45 | 2.22 | 0.94 | −58 |
55 | 375 | 1 | 45 | 2.22 | 0.90 | −60 |
56 | 321 | 1 | 45 | 2.22 | 0.136 | −61 |
57 | 322 | 1 | 45 | 2.22 | 0.82 | −63 |
58 | 323 | 1 | 45 | 2.22 | 0.78 | −65 |
… | ||||||
75 | 355 | 1 | 45 | 2.22 | −0.15 | −107 |
76 | 411 | 1 | 45 | 2.22 | 11.25 | −107 |
77 | 412 | 1 | 45 | 2.22 | 11.22 | 406 |
78 | 413 | 1 | 45 | 2.22 | 0.55 | 405 |
79 | 414 | 1 | 45 | 2.22 | 0.52 | −75 |
80 | 415 | 1 | 45 | 2.22 | 0.49 | −77 |
… | ||||||
100 | 455 | 1 | 45 | 2.22 | −0.11 | −105 |
101 | 511 | 1 | 45 | 2.22 | 10.88 | 390 |
102 | 512 | 1 | 45 | 2.22 | 10.85 | 388 |
103 | 513 | 1 | 45 | 2.22 | 0.78 | −65 |
104 | 514 | 1 | 45 | 2.22 | 0.73 | −67 |
105 | 515 | 1 | 45 | 2.22 | 0.70 | −69 |
106 | 521 | 1 | 45 | 2.22 | 0.67 | −70 |
… | ||||||
120 | 545 | 1 | 45 | 2.22 | 0.25 | −89 |
121 | 551 | 1 | 45 | 2.22 | 0.22 | −90 |
122 | 552 | 1 | 45 | 2.22 | 0.19 | −91 |
123 | 553 | 1 | 45 | 2.22 | 0.10 | −96 |
124 | 554 | 1 | 45 | 2.22 | 0.01 | −100 |
125 | 555 | 1 | 45 | 2.22 | −0.08 | −104 |
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Kumar, V., Reinartz, W. (2012). Customer Analytics Part II. In: Customer Relationship Management. Springer Texts in Business and Economics. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20110-3_6
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