# Survey data and Bayesian analysis: a cost-efficient way to estimate customer equity

## Abstract

We present a Bayesian framework for estimating the customer lifetime value (CLV) and the customer equity (CE) based on the purchasing behavior deducible from the market surveys on customer purchasing behavior. The proposed framework systematically addresses the challenges faced when the future value of customers is estimated based on survey data. The scarcity of the survey data and the sampling variance are countered by utilizing the prior information and quantifying the uncertainty of the CE and CLV estimates by posterior distributions. Furthermore, information on the purchase behavior of the customers of competitors available in the survey data is integrated to the framework. The introduced approach is directly applicable in the domains where a customer relationship can be thought to be monogamous. As an example on the use of the framework, we analyze a consumer survey on mobile phones carried out in Finland in February 2013. The survey data contains consumer given information on the current and previous brand of the phone and the times of the last two purchases.

## Keywords

Bayesian estimation Brand switching Customer equity Customer lifetime value Survey## JEL Classifications

M31 C11 C81 C34 C83## References

- Abe, M. (2009). Counting your customers one by one: A hierarchical Bayes extension to the Pareto/NBD model.
*Marketing Science*,*28*(3), 541–553.CrossRefGoogle Scholar - Allison, P.D. (1985). Survival analysis of backward recurrence times.
*Journal of the American Statistical Association*,*80*(390), 315–322.CrossRefGoogle Scholar - Bauer, H., Hammerschmidt, M., Braehler, M. (2003). Customer lifetime value concept and its contribution to corporate valuation.
*Yearbook of Marketing and Consumer Research*,*1*(1), 49–67.Google Scholar - Bejou, D., Keiningham, T., Aksoy, L. (Eds.) (2006).
*Customer lifetime value – reshaping the way we manage to maximize profit*: Haworth Press.Google Scholar - Blattberg, R.C., Byung-Do, K., Neslin, S.A. (Eds.) (2008).
*Database marketing: analyzing and managing customers*: Springer.Google Scholar - Borle, S., Singh, S.S., Jain, D.C. (2008). Customer lifetime value measurement.
*Management science*,*54*(1), 100–112.CrossRefGoogle Scholar - Brooks, S.P., & Gelman, A. (1998). General methods for monitoring convergence of iterative simulations.
*Journal of Computational and Graphical Statistics*,*7*, 434–455.MathSciNetGoogle Scholar - Fader, P., & Hardie, B. (2010). Customer-base valuation in a contractual setting: The perils of ignoring heterogeneity.
*Marketing Science*,*29*(1).Google Scholar - Fader, P., Hardie, B., Lee, K.L. (2005a). Counting Your Customers the easy way: An alternative to the Pareto/NBD model.
*Marketing Science*,*24*(2), 275–284.CrossRefGoogle Scholar - Fader, P., Hardie, B., Lee, K.L. (2005b). RFM and CLV: Using iso-value curves for customer base analysis.
*Journal of Marketing Research XLII(November)*, 415–430.Google Scholar - Finnish Communications Regulatory Authority (FICORA) (2013). Toimialakatsaus 2012 (In Finnish). https://www.viestintavirasto.fi/attachments/Toimialakatsaus2012.pdf.
- Gelman, A. (2006). Prior distributions for variance parameters in hierarchical models.
*Bayesian Analysis*,*1*(3), 515–533.MathSciNetGoogle Scholar - Gelman, A., Carlin, J.B., Stern, H.S., Dunson, D.B., Vehtari, A., Rubin, D.B. (2013).
*Bayesian data analysis*, 3rd edn. Boca Raton, FL: Chapman & Hall/CRC.Google Scholar - Gupta, S., & Lehmann, D. (2005).
*Managing customers as investments*: the strategic value of customers in the long run. Upper Saddle River, NJ: Wharton School Publishing.Google Scholar - Herniter, J. (1971). A probablistic market model of purchase timing and brand selection.
*Management Science*,*18*(4-Part-II), P–102.CrossRefGoogle Scholar - Hubbard, D.W. (2010).
*How to measure anything: finding the value of intangibles in business*, 2nd edn. Hoboken, NJ: Wiley.Google Scholar - Jen, L., Chou, C.-H., Allenby, G.M. (2009). The importance of modeling temporal dependence of timing and quantity in direct marketing.
*Journal of Marketing Research*,*46*(4), 482–493.CrossRefGoogle Scholar - Kumar, V., & George, M. (2007). Measuring and maximizing customer equity: a critical analysis.
*Journal of the Academy of Marketing Science*,*35*(4), 157–171.CrossRefGoogle Scholar - Kumar, V., & Petersen, J.A. (2005). Using a customer-level marketing strategy to enhance firm’s performance.
*Journal of the Academy of Marketing Science*,*33*(4), 505–519.CrossRefGoogle Scholar - Kumar, V., Venkatesan, R., Bohling, T., Beckmann, D. (2008). The power of CLV: Managing customer lifetime value at IBM.
*Marketing Science*,*27*(4), 585–599.CrossRefGoogle Scholar - Lunn, D., Spiegelhalter, D., Thomas, A., Best, N. (2009). The BUGS project: Evolution, critique and future directions (with discussion).
*Statistics in Medicine*,*28*, 3049–3082.MathSciNetCrossRefPubMedGoogle Scholar - Nagano, S., Ichikawa, Y., Takaya, N., Uchiyama, T., Abe, M. (2013). Nonparametric hierarchal bayesian modeling in non-contractual heterogeneous survival data. In:
*Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining*(pp. 668–676). ACM.Google Scholar - Pfeifer, P. (2011). On estimating current-customer equity using company summary data.
*Journal of Interactive Marketing*,*25*(1), 1–14.CrossRefGoogle Scholar - R Core Team (2012). R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. ISBN 3-900051-07-0.Google Scholar
- Rossi, P.E., Allenby, G.M., McCulloch, R. (2005).
*Bayesian statistics and marketing*. Hoboken, NJ: Wiley.Google Scholar - Rust, R.T., Zeithaml, V.A., Lemon, K.N. (2001).
*Driving customer equity: how customer lifetime value is reshaping corporate strategy*. New York: Simon and Schuster.Google Scholar - Schmittlein, D., Morrison, D., Colombo, R. (1987). Counting your customers: Who they are and what will they do next
*Management Science*,*33*(1), 1–24.CrossRefGoogle Scholar - Schmittlein, D.C., Bemmaor, A.C., Morrison, D.G. (1985). Technical note – why does the NBD model work? Robustness in representing product purchases, brand purchases and imperfectly recorded purchases.
*Marketing Science*,*4*(3), 255–266.CrossRefGoogle Scholar - Singh, S.S., Borle, S., Jain, D.C. (2009). A generalized framework for estimating customer lifetime value when customer lifetimes are not observed.
*Quantitative Marketing and Economics*,*7*(2), 181–205.CrossRefGoogle Scholar - Statistics Finland (2012). Statistical Yearbook of Finland 2012.Google Scholar
- Sturtz, S., Ligges, U., Gelman, A. (2005). R2WinBUGS: A package for running WinBUGS from R.
*Journal of Statistical Software*,*12*(3), 1–16.CrossRefGoogle Scholar - Venkatesan, R., & Kumar, V. (2004). A customer lifetime value framework for customer selection and resource allocation strategy.
*Journal of Marketing*,*68*(10), 106–125.CrossRefGoogle Scholar - Vilcassim, N.J., & Jain, D.C. (1991). Modeling purchase-timing and brand-switching behavior incorporating explanatory variables and unobserved heterogeneity.
*Journal of Marketing Research*, 29–41.Google Scholar