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Handbook of Marketing Decision Models pp 297–329Cite as

Marketing Models for the Customer-Centric Firm

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Part of the book series: International Series in Operations Research & Management Science ((ISOR,volume 254))

Abstract

A customer-centric firm takes the view that there are three key drivers of (organic) growth and overall profitability: Customer acquisition , customer retention , and customer development (i.e., increasing the value of each existing customer (per unit of time) while they remain a customer). In this chapter we review the key data-based tools and methods that have been developed by marketing scientists (and researchers and practitioners in related fields such as operations research, statistics, and computer science) to assist firms in their understanding and implementing these activities more effectively.

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Notes

  1. 1.

    There are other ways of expressing this basic idea. For example, instead of talking about retention and development, Bolton et al. (2004) talk of the length, depth, and breadth of the relationship between a customer and a service provider, where “the depth of a relationship is reflected in the frequency of service usage over time [... and ...] in customers’ decisions to upgrade and purchase premium (higher margin) products instead of low-cost variants [, ... and ... ] the breadth of a relationship is reflected in cross-buying or ‘add-on’ buying; that is, the number of additional (different) products or services purchased” (p. 273).

  2. 2.

    See Deighton and Johnson (2013) for an examination of the complex network of firms that collect and use data about individuals for marketing purposes.

  3. 3.

    A notable exception is the work of Natter et al. (2015).

  4. 4.

    While a sale may be the “direct response” to the advertisement, it is frequently a referral (i.e., the individual revealing that they are a prospect very interested in the product or service being advertised), which may or may not result in a sale. Calli et al. (2012) and Tellis et al. (2000) are examples of work that model the response to direct response advertising on radio and/or TV, both focusing on referrals and making no distinction between new referrals (i.e., prospects) and repeat customers.

  5. 5.

    Note that the vast majority of the response/predictive/classification models presented in the direct marketing related literature are not acquisition focused. Rather, they consider the response to mailings to existing customers (as opposed to prospects). Any model that includes past purchasing behavior as a covariate obviously falls in this category. We review this work in Sect. 10.3.3.

  6. 6.

    It is also important to consider the impact of acquisition campaigns on the behavior of existing customers. For example, offering new customers better deals than existing customers can potentially result in customer dissatisfaction—“I’ve been a loyal customer for many years and I’m getting a worse deal than new customers!” See Lhoest-Snoeck et al. (2014) for a discussion and examination of these issues.

  7. 7.

    Other work looks at the long-term implications of customer behaviour at the time of acquisition. For example, Fader et al. (2007) compare the repeat-buying behaviour of two groups of customers that differ in terms of the size of their initial purchase, and find that those with a higher initial transaction value have higher repeat-buying rates and lower attrition rates. Padilla and Ascarza (2017) explore how differences in customer behavior at the time of acquisition explain differences in expected value and customers’ sensitivity to marketing actions.

  8. 8.

    This question is partially addressed by research on allocating marketing expenditures between acquisition and retention activities, which we consider in Sect. 10.4.

  9. 9.

    This section draws on material presented in Fader and Hardie (2009, 2015). Readers are referred to these references for a deeper review of this literature.

  10. 10.

    We may also wish to include the acquisition cost in the calculation of the second quantity.

  11. 11.

    A related stream of work uses homogeneous Markov chains to characterize customer behavior (e.g., Deming and Glasser 1968; Pfeifer and Carraway 2000; Soukup 1983). Such work does not account for heterogeneity in the underlying behavioral characteristics, which can lead to misleading interferences about the nature of buying behavior (e.g., Frank 1962). See Ching et al. (2004) for an example of how these simple Markov models of customer behavior can be embedded in broader marketing optimization models.

  12. 12.

    Of course, if it is possible to characterize these time-varying covariates by a separate stochastic process, we could take the expectation of the covariate-dependent process over the distribution of covariate paths. How the resulting estimates of E(CLV) and E(RLV) would differ from those based on models of customer behavior that do no consider time-varying covariates is an open question.

  13. 13.

    David Shepard Associates (1999) use the labels contractual and implied; “an implied relationship is one in which there is no obligation on either party’s part to do anything in the future” (p. 416).

  14. 14.

    This is not the same as Jackson’s (1985) lost-for-good versus alway-a-share classification. Following Fader and Hardie (2014a), we feel that the contractual versus noncontractual classification is a better way of thinking about the nature of a firm’s relationship with its customers, as the notion of latent attrition is missing from the basic always-a-share “model.”

  15. 15.

    What lies behind this death? It could be a change in customer tastes, financial circumstances, and/or geographical location, the outcome of bad customer service experiences, or even physical death, to name but a few possible causes. But given the modeling objectives, why this death occurs is of little interest to the analyst; the primary goal is to ensure that the phenomenon is captured by the model.

  16. 16.

    Churners are typically categorized as voluntary or involuntary. Voluntary churn occurs when the customer decides to terminate their relationship with the firm, whereas involuntary churn occurs when the firm terminates the relationship (e.g., as a result of nonpayment or fraud). Involuntary churners are typically excluded when developing a churn model or modeling survival (cf. Braun and Schweidel 2011).

  17. 17.

    Nitzan and Libai (2011) examine such effects in a duration time (i.e., survival) model.

  18. 18.

    Neslin et al. (2006) and Blattberg et al. (2008) use the abbreviation LTV, which we have replaced with CLV . Strictly speaking, this should be E(RLV), but the distinction raised in (10.1) and (10.2) is ignored in most of the literature, including the work of Blattberg et al. (2008) and Neslin et al. (2006).

  19. 19.

    Of course, a firm will make use of experiments to determine the best mailing package (e.g., Bult et al. 1997).

  20. 20.

    In addition to the frequency of response, a number of researchers have considered the impact of contact history (e.g., frequency of contact) on the customer’s likelihood of responding to the current mailing, including possible irritation effects; see Schröder and Hruschka (2016) for a review. This is especially an issue in today’s permission marketing world where, for example, too much contact could result in the customer opting-out of communications all together (e.g., Drèze and Bonfrer 2008).

  21. 21.

    Whereas the work discussed above implicitly assumes that customers can only place an order (i.e., respond) in a given period if they receive a mailing, Gönül et al. (2000) recognize that customers can place orders from old catalogs, even though they did not receive a catalog in the current period.

  22. 22.

    Note that most of this work has focused on which products to offer to the firm’s customers. Khan et al. (2009) develop a model for determining which promotions to offer, if any, over a finite planning horizon. The promotions they consider are transaction, not product, specific (i.e., free shipping offers, discount coupons, and loyalty program rewards).

  23. 23.

    Once at the firm’s website, a further form of customization matches the “look and feel” of a website to each customer (e.g., Hauser et al. 2009, 2014).

  24. 24.

    At first glance, it may appear that the work that embeds Blattberg and Deighton’s (1996) model in a brand-switching framework (e.g., Tsao et al. 2014; Williams and Williams 2015) would work in a noncontractual setting. However, this is not the case; the fact that someone purchases from a competitive firm between two purchases from the focal firm should not necessarily mean that they churned after the first purchase and were acquired (again) when they made their second purchase. The notions of acquisition and retention implicit in such models are quite different from those implict in most of the literature reviewed in this chapter. (See Fader and Hardie (2014a) for a further discussion of issues related to the treatment of competition in noncontractual settings.).

References

  • Abe, M. 2009. “Counting your customers” one by one: A hierarchical Bayes extension to the Pareto/NBD model. Marketing Science 28 (May–June): 541–553.

    Google Scholar 

  • Abhishek, V., P.S. Fader, and K. Hosanagar. 2015. Media exposure through the funnel: A model of multi-stage attribution. http://ssrn.com/abstract=2158421. Accessed 10 Aug 2016.

  • Adomavicius, G., and A. Tuzhilin. 2005. Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering 17 (June): 734–749.

    Article  Google Scholar 

  • Ainslie, A., and L. Pitt. 1998. UniBank and the analysis of the ExcursionCard customer database: A practical application of statistical techniques in database marketing. Journal of Interactive Marketing 12 (Summer): 57–66.

    Google Scholar 

  • Allenby, G.M., and R.C. Blattberg. 1987. A new theory of direct market testing. Journal of Direct Marketing 1 (Autumn): 24–37.

    Article  Google Scholar 

  • Almquist, E., and G. Wyner. 2001. Boost your marketing ROI with experimental design. Harvard Business Review 79 (October): 135–141.

    Google Scholar 

  • Ansari, A., S. Essegaier, and R. Kohli. 2000. Internet recommendation systems. Journal of Marketing Research 37 (August): 363–375.

    Article  Google Scholar 

  • Ansari, A., and C.F. Mela. 2003. E-customization. Journal of Marketing Research 40 (May): 131–145.

    Article  Google Scholar 

  • Ascarza, E. 2016. Retention futility: Targeting high risk customers might be ineffective. http://ssrn.com/abstract=2759170. Accessed 10 Aug 2016.

  • Ascarza, E., P. Ebbes, O. Netzer, and M. Danielson. 2017. Beyond the target customer: Social effects of CRM campaigns. Journal of Marketing Research, forthcoming.

    Google Scholar 

  • Ascarza, E., and B.G.S. Hardie. 2013. A joint model of usage and churn in contractual settings. Marketing Science, 32 (July–August): 570–590.

    Google Scholar 

  • Ascarza, E., R. Iyengar, and M. Schleicher. 2016. The perils of proactive churn prevention using plan recommendations: Evidence from a field experiment. Journal of Marketing Research 53 (February): 46–60.

    Article  Google Scholar 

  • Ascarza, E., O. Netzer, and B.G.S. Hardie. 2017. Some customers would rather leave without saying. Mark Sci, forthcoming.

    Google Scholar 

  • Ballings, M., and D. Van den Poel. 2012. Customer event history for churn prediction: How long is long enough? Expert Systems with Applications 39 (18): 13517–13522.

    Article  Google Scholar 

  • Ballings, M., and D. Van den Poel. 2015. CRM in social media: Predicting increases in Facebook usage frequency. European Journal of Operational Research 244 (1): 248–260.

    Article  Google Scholar 

  • Baumgartner, B., and H. Hruschka. 2005. Allocation of catalogs to collective customers based on semiparametric response models. European Journal of Operational Research 162 (3): 839–849.

    Article  Google Scholar 

  • Bell, G.H., J. Ledolter, and A.J. Swersey. 2006. Experimental design on the front lines of marketing: Testing new ideas to increase direct mail sales. International Journal of Research in Marketing 23 (September): 309–319.

    Article  Google Scholar 

  • Bemmaor, A.C., and N. Glady. 2012. Modeling purchasing behavior with sudden “death”: A flexible customer lifetime model. Management Science 58 (May): 1012–1021.

    Article  Google Scholar 

  • Berger, P.D., and N.N. Bechwati. 2001. The allocation of promotion budget to maximize customer equity. Omega 29 (February): 49–61.

    Article  Google Scholar 

  • Berger, P.D., and D. Bernstein. 2002. The optimal trade-off between acquisition and retention promotion—An application to the diagnostic self-testing market. International Quarterly Journal of Marketing, 2 (January–December): 47–54.

    Google Scholar 

  • Bitran, G.R., and S.V. Mondschein. 1996. Mailing decisions in the catalog sales industry. Management Science 42 (September): 1364–1381.

    Article  Google Scholar 

  • Blattberg, R.C., and J. Deighton. 1996. Manage marketing by the customer equity test. Harvard Business Review, 74 (July–August): 136–144.

    Google Scholar 

  • Blattberg, R.C., G. Getz, and J.S. Thomas. 2001. Customer equity: Building and managing relationships as valuable assets. Boston, MA: Harvard Business School Press.

    Google Scholar 

  • Blattberg, R.C., B.-D. Kim, and S.A. Neslin. 2008. Database marketing: Analyzing and managing customers. New York, NY: Springer.

    Book  Google Scholar 

  • Bodapati, A.V. 2008. Recommendation systems with purchase data. Journal of Marketing Research 45 (February): 77–93.

    Article  Google Scholar 

  • Bolton, R.N. 1998. A dynamic model of the duration of the customer’s relationship with a continuous service provider: The role of satisfaction. Marketing Science 17 (February): 45–65.

    Google Scholar 

  • Bolton, R.N., K.N. Lemon, and P.C. Verhoef. 2004. The theoretical underpinnings of customer asset management: A framework and propositions for future research. Journal of the Academy of Marketing Science 32 (July): 271–292.

    Google Scholar 

  • Braun, M., and D.A. Schweidel. 2011. Modeling customer lifetimes with multiple causes of churn. Marketing Science, 30 (September–October): 881–902.

    Google Scholar 

  • Braun, M., D.A. Schweidel, and E.M. Stein. 2015. Transaction attributes and customer valuation. Journal of Marketing Research 52 (December): 848–864.

    Article  Google Scholar 

  • Buchanan, B., and D.G. Morrison. 1988. A stochastic model of list falloff with implications for repeat mailings. Journal of Direct Marketing 2 (Summer): 7–15.

    Article  Google Scholar 

  • Bult, J.R. 1993. Semiparametric versus parametric classification models: An application to direct marketing. Journal of Marketing Research 30 (August): 380–390.

    Article  Google Scholar 

  • Bult, J.R., H. van der Scheer, and T. Wansbeek. 1997. Interaction between target and mailing characteristics in direct marketing, with an application to health care fund raising. International Journal of Research in Marketing 14 (October): 301–308.

    Article  Google Scholar 

  • Bult, J.R., and T. Wansbeek. 1995. Optimal selection for direct mail. Marketing Science 14 (November): 378–394.

    Article  Google Scholar 

  • Calciu, M. 2008. Numeric decision support to find optimal balance between customer acquisition and retention spending. Journal of Targeting, Measurement and Analysis for Marketing 16 (3): 214–227.

    Article  Google Scholar 

  • Calli, M.K., M. Weverbergh, and P.H. Franses. 2012. The effectiveness of high-frequency direct-response commercials. International Journal of Research in Marketing 29 (March): 98–109.

    Article  Google Scholar 

  • Campbell, D., R. Erdahl, D. Johnson, E. Bibelnieks, M. Haydock, M. Bullock, and H. Crowder. 2001. Optimizing customer mail streams at Fingerhut. Interfaces, 31 (January–February): 77–90.

    Google Scholar 

  • Carroll, B.J. 2006. Lead generation for the complex sale. New York, NY: McGraw-Hill.

    Google Scholar 

  • Chan, T.Y., C. Wu, and Y. Xie. 2011. Measuring the lifetime value of customers acquired from Google search advertising. Marketing Science 30 (September–October): 837–850.

    Google Scholar 

  • Chang, C.W., and J.Z. Zhang. 2016. The effects of channel experiences and direct marketing on customer retention in multichannel settings. Journal of Interactive Marketing 36 (November): 77–90.

    Article  Google Scholar 

  • Chen, Y., and J.H. Steckel. 2012. Modeling credit card share of wallet: Solving the incomplete information problem. Journal of Marketing Research 49 (October): 655–669.

    Article  Google Scholar 

  • Ching, W.K., M.K. Ng, K.-K. Wong, and E. Altman. 2004. Customer lifetime value: Stochastic optimization approach. Journal of the Operational Research Society 55 (August): 860–868.

    Google Scholar 

  • Chung, T.S., M. Wedel, and R.T. Rust. 2016. Adaptive personalization using social networks. Journal of the Academy of Marketing Science 44 (January): 66–87.

    Google Scholar 

  • Cohen, M.-D. 2004. Exploiting response models–Optimizing cross-sell and up-sell opportunities in banking. Information Systems 29 (June): 327–341.

    Article  Google Scholar 

  • Colombo, R., and W. Jiang. 1999. A stochastic RFM model. Journal of Interactive Marketing 13 (Summer): 2–12.

    Article  Google Scholar 

  • Coussement, K., and D. Van den Poel. 2008. Churn prediction in subscription services: An application of support vector machines while comparing two parameter-selection techniques. Expert Systems with Applications 34 (1): 313–327.

    Article  Google Scholar 

  • Cui, G., M.L. Wong, and H.-K. Lui. 2006. Machine learning for direct marketing response models: Bayesian networks with evolutionary programming. Management Science 52 (April): 597–612.

    Article  Google Scholar 

  • Dasgupta, K., R. Singh, B. Viswanathan, D. Chakraborty, S. Mukherjea, A.A. Nanavati, and A. Joshi. 2008. Social ties and their relevance to churn in mobile telecom networks. Proceedings of the 11th international conference on extending database technology: Advances in database technology, 668–677. New York, NY: Association for Computing Machinery.

    Google Scholar 

  • Datta, H., B. Foubert, and H.J. van Heerde. 2015. The challenge of retaining customers acquired with free trials. Journal of Marketing Research 52 (April): 217–234.

    Article  Google Scholar 

  • David Shepard Associates. 1999. The new direct marketing, 3rd ed. New York: McGraw-Hill.

    Google Scholar 

  • De Bock, K.W., and D. Van den Poel. 2011. An empirical evaluation of rotation-based ensemble classifiers for customer churn prediction. Expert Systems with Applications 38 (10): 12293–12301.

    Article  Google Scholar 

  • Deighton, J., & Johnson, P.A. 2013. The value of data: Consequences for insight, innovation, and efficiency in the U.S. economy. New York, NY: The Direct Marketing Association.

    Google Scholar 

  • Delanote, S., R. Leus, and F.T. Nobibon. 2013. Optimization of the annual planning of targeted offers in direct marketing. Journal of the Operational Research Society 64 (December): 1770–1779.

    Article  Google Scholar 

  • Deming, W.E., & Glasser, G.J. 1968. A Markovian analysis of the life of newspaper subscriptions. Management Science, 14 (February): B-283–B-293.

    Google Scholar 

  • Drèze, X., and A. Bonfrer. 2008. An empirical investigation of the impact of communication timing on customer equity. Journal of Interactive Marketing 22 (Winter): 36–50.

    Article  Google Scholar 

  • Dong, W., S.D. Swain, and P.D. Berger. 2007. The role of channel quality in customer equity management. Journal of Business Research 60 (December): 1243–1252.

    Article  Google Scholar 

  • Donkers, B., R. Paap, J.-J. Jonker, and P.H. Franses. 2006. Deriving target selection rules from endogenously selected samples. Journal of Applied Econometrics 21 (July/August): 549–562.

    Google Scholar 

  • Du, R.Y., W.A. Kamakura, and C.F. Mela. 2007. Size and share of customer wallet. Journal of Marketing 71 (April): 94–113.

    Article  Google Scholar 

  • Ehrman, C.M. 1990. Correcting for “regression to the mean” in list selection decisions. Journal of Direct Marketing 4 (Spring): 21–30.

    Article  Google Scholar 

  • Ehrman, C.M., and G.M. Funk. 1997. Insights on “Selecting, evaluating, and updating prospects in direct mail marketing,” by Vithala Rao and Joel Steckel. Journal of Interactive Marketing 11 (Summer): 8–13.

    Google Scholar 

  • Ehrman, C.M., and K.J. Miescke. 1989. Structured decision rules for ranking and selecting mailing lists and creative packages for direct marketing. Journal of Direct Marketing 3 (Winter): 47–59.

    Article  Google Scholar 

  • Elsner, R., M. Krafft, and A. Huchzermeier. 2003. Optimizing Rhenania’s mail-order business through dynamic multilevel modeling (DMLM). Interfaces, 33 (January–February): 50–66.

    Google Scholar 

  • Elsner, R., M. Krafft, and A. Huchzermeier. 2004. Optimizing Rhenania’s direct marketing business through dynamic multilevel modeling (DMLM) in a multicatalog-brand environment. Marketing Science 23 (Spring): 192–206.

    Article  Google Scholar 

  • Fader, P. 2012. Customer centricity: Focus on the right customers for strategic advantage, 2nd ed. Philadelphia, PA: Wharton Digital Press.

    Google Scholar 

  • Fader, P.S., B.G.S. Hardie, and K. Jerath. 2007. Estimating CLV using aggregated data: The Tuscan Lifestyles case revisited. Journal of Interactive Marketing 21 (Summer): 55–71.

    Google Scholar 

  • Fader, P.S., and B.G.S. Hardie. 2007a. How to project customer retention. Journal of Interactive Marketing 21 (Winter): 76–90.

    Article  Google Scholar 

  • Fader, P.S., and B.G.S. Hardie. 2007b. Incorporating time-invariant covariates into the Pareto/NBD and BG/NBD models. http://www.brucehardie.com/notes/019/. Accessed 10 Aug 2016.

  • Fader, P.S., and B.G.S. Hardie. 2009. Probability models for customer-base analysis. Journal of Interactive Marketing 23 (January): 61–69.

    Article  Google Scholar 

  • Fader, P.S., & Hardie, B.G.S. 2010. Customer-base valuation in a contractual setting: The perils of ignoring heterogeneity. Marketing Science, 29 (January–February), 85–93.

    Google Scholar 

  • Fader, P.S., and B.G.S. Hardie. 2012. Reconciling and clarifying CLV formulas. http://www.brucehardie.com/notes/024/. Accessed 10 Aug 2016.

  • Fader, P.S., and B.G.S. Hardie. 2014a. The Pareto/NBD is not a lost-for-good model. http://www.brucehardie.com/notes/031/. Accessed 10 Aug 2016.

  • Fader, P.S., and B.G.S. Hardie. 2014b. A spreadsheet-literate non-statistician’s guide to the beta-geometric model. http://www.brucehardie.com/notes/032/. Accessed 10 Aug 2016.

  • Fader, P.S., and B.G.S. Hardie. 2014c. What’s wrong with this CLV formula? http://www.brucehardie.com/notes/033/. Accessed 10 Aug 2016.

  • Fader, P.S., and B.G.S. Hardie. 2015. Simple probability models for computing CLV and CE. In The handbook of customer equity, ed. V. Kumar and Denish Shah, 77–100. Cheltenham, UK: Edward Elgar Publishers.

    Google Scholar 

  • Fader, P.S., B.G.S. Hardie, and K.L. Lee. 2005a. “Counting your customers” the easy way: An alternative to the Pareto/NBD model. Marketing Science 24 (Spring): 275–284.

    Article  Google Scholar 

  • Fader, P.S., B.G.S. Hardie, and K.L. Lee. 2005b. RFM and CLV: Using iso-value curves for customer base analysis. Journal of Marketing Research 42 (November): 415–430.

    Article  Google Scholar 

  • Fader, P.S., B.G.S. Hardie, and S. Sen. 2014. Stochastic models of buyer behavior. In The history of marketing science, ed. Russell S. Winer, and Scott A. Neslin, 165–205. Singapore: World Scientific Publishing.

    Google Scholar 

  • Fader, P.S., B.G.S. Hardie, and J. Shang. 2010. Customer-base analysis in a discrete-time noncontractual setting. Marketing Science, 29 (November–December): 1086–1108.

    Google Scholar 

  • Fan, S.S., and P.D. Berger. 2001. The optimal allocation between acquisition and retention spending over multiple time periods. International Quarterly Journal of Marketing, 1 (April–December): 199–210.

    Google Scholar 

  • Frank, R.E. 1962. Brand choice as a probability process. The Journal of Business 35 (January): 43–56.

    Google Scholar 

  • Fruchter, G.E., and Z.J. Zhang. 2004. Dynamic targeted promotions: A customer retention and acquisition perspective. Journal of Service Research 7 (August): 3–19.

    Article  Google Scholar 

  • Galbraith, J.R. 2005. Designing the customer-centric organization. San Francisco, CA: Jossey-Bass.

    Google Scholar 

  • George, M., V. Kumar, and D. Grewal. 2013. Maximizing profits for a multi-category catalog retailer. Journal of Retailing 89 (December): 374–396.

    Article  Google Scholar 

  • Gerpott, T.J., and N. Ahmadi. 2015. Regaining drifting mobile communication customers: Predicting the odds of success of winback efforts with competing risks regression. Expert Systems with Applications 42 (21): 7917–7928.

    Article  Google Scholar 

  • Gönül, F.F., B.-D. Kim, and M. Shi. 2000. Mailing smarter to catalog customers. Journal of Interactive Marketing 14 (Spring): 2–16.

    Article  Google Scholar 

  • Gönül, F.F., and F. Ter Hofstede. 2006. How to compute optimal catalog mailing decisions. Marketing Science, 25 (January–February): 65–74.

    Google Scholar 

  • Griffin, J., and M.W. Lowenstein. 2001. Customer winback: How to recapture lost customers–And keep them loyal. San Francisco, CA: Jossey-Bass.

    Google Scholar 

  • Gupta, S. 2014. Marketing reading: Customer management (Core curriculum readings series). Boston, MA: Harvard Business Publishing.

    Google Scholar 

  • Gupta, S., and D.R. Lehmann. 2005. Managing customers as investments. Upper Saddle River, NJ: Wharton School Publishing.

    Google Scholar 

  • Gupta, S., D.R. Lehmann, and J.A. Stuart. 2004. Valuing customers. Journal of Marketing Research 41 (February): 7–18.

    Google Scholar 

  • Haenlein, M. 2013. Social interactions in customer churn decisions: The impact of relationship directionality. International Journal of Research in Marketing 30 (September): 236–248.

    Article  Google Scholar 

  • Hansen, K. 2015. Comment on the “Predictive modelling for churner/non-churner” conversation in the “Advanced Business Analytics, Data Mining and Predictive Modeling” LinkedIn group. https://www.linkedin.com/groups/35222/35222-5949032187344023556. Accessed 10 Aug 2016.

  • Hansotia, B.J., and P. Wang. 1997. Analytical challenges in customer acquisition. Journal of Direct Marketing 11 (Spring): 7–19.

    Article  Google Scholar 

  • Hauser, J.R., G. Liberali, and G.L. Urban. 2014. Website morphing 2.0: Switching costs, partial exposure, random exit, and when to morph. Management Science 60 (June): 1594–1616.

    Article  Google Scholar 

  • Hauser, J.R., G.L. Urban, G. Liberali, and M. Braun. 2009. Website morphing. Marketing Science, 28 (March–April): 202–223.

    Google Scholar 

  • Heitz, C., M. Dettling, and A. Ruckstuhl. 2011. Modelling customer lifetime value in contractual settings. International Journal of Services Technology and Management 16 (2): 172–190.

    Article  Google Scholar 

  • Hinz, O., B. Skiera, C. Barrot, and J.U. Becker. 2011. Seeding strategies for viral marketing: An empirical comparison. Journal of Marketing 75 (November): 55–71.

    Article  Google Scholar 

  • Hoekstra, J.C., P.S.H. Leeflang, and D.R. Wittink. 1999. The customer concept: The basis for a new marketing paradigm. Journal of Market Focused Management 4 (1): 43–76.

    Article  Google Scholar 

  • Holtrop, N., J.E. Wieringa, M.J. Gijsenberg, and P.C. Verhoef. 2017. No future without the past? Predicting churn in the face of customer privacy. International Journal of Research in Marketing, 34 (March): 154–172.

    Google Scholar 

  • Hopmann, J., and A. Thede. 2005. Applicability of customer churn forecasts in a non-contractual setting. In Innovations in classification, data science, and information systems (Proceedings of the 27th annual conference of the gesellschaft fr klassifikation e.V., Brandenburg University of Technology, Cottbus, March 12–14, 2003), ed. Daniel Baier and Klaus-Dieter Wernecke, 330–337. Berlin: Springer.

    Google Scholar 

  • Howard, R.A. 1978. Comments on the origin and application of Markov decision processes. In Dynamic Programming and its Applications, ed. M.L. Puterman, 201–205. New York: Academic Press.

    Chapter  Google Scholar 

  • Hruschka, H. 2010. Considering endogeneity for optimal catalog allocation in direct marketing. European Journal of Operational Research 206 (1): 239–247.

    Article  Google Scholar 

  • Huang, C.-Y. 2012. To model, or not to model: Forecasting for customer prioritization. International Journal of Forecasting 28 (2): 497–506.

    Article  Google Scholar 

  • Hughes, A.M. 1996. The complete database marketer, revised ed. Chicago, IL: Irwin.

    Google Scholar 

  • Imhoff, C., L. Loftis, and J.G. Geiger. 2001. Building the customer-centric enterprise. New York, NY: Wiley.

    Google Scholar 

  • Jackson, B.B. 1985. Winning and keeping industrial customers. New York, NY: Lexington Books.

    Google Scholar 

  • Jamal, Z., and R.E. Bucklin. 2006. Improving the diagnosis and prediction of customer churn: A heterogeneous hazard modeling approach. Journal of Interactive Marketing, 20 (Summer/Autumn): 16–29.

    Google Scholar 

  • Jerath, K., P.S. Fader, B.G.S. Hardie 2011. New perspectives on customer ‘death’ using a generalization of the Pareto/NBD model. Marketing Science, 30 (September–October): 866–880.

    Google Scholar 

  • Jerath, K., P.S. Fader, and B.G.S. Hardie. 2016. Customer-base analysis using repeated cross-sectional summary (RCSS) data. European Journal of Operational Research 249 (1): 340–350.

    Article  Google Scholar 

  • Jonker, J.-J., N. Piersma, and R. Potharst. 2006. A decision support system for direct mailing decisions. Decision Support Systems 42 (November): 915–925.

    Article  Google Scholar 

  • Kamakura, W.A., S.N. Ramaswami, and R.K. Srivastava. 1991. Applying latent trait analysis in the evaluation of prospects for cross-selling of financial services. International Journal of Research in Marketing 8 (November): 329–349.

    Article  Google Scholar 

  • Kannan, P.K., W. Reinartz, and P.C. Verhoef. 2016. The path to purchase and attribution modeling: Introduction to special section. International Journal of Research in Marketing 33 (September): 449–456.

    Google Scholar 

  • Katz, E., and P.F. Lazarsfeld. 1955. Personal influence: The part played by people in the flow of mass communications. Glencoe, IL: The Free Press.

    Google Scholar 

  • Kestnbaum, R.D., K.T. Kestnbaum, and P.W. Ames. 1998. Building a longitudinal contact strategy\(^{\text{ TM }}\). Journal of Interactive Marketing 12 (Winter): 56–62.

    Article  Google Scholar 

  • Khan, R., M. Lewis, and V. Singh. 2009. Dynamic customer management and the value of one-to-one marketing. Marketing Science 28 (November–December): 1063–1079.

    Google Scholar 

  • Kim, B.-D., and S.-O. Kim. 1999. Measuring upselling potential of life insurance customers: Application of a stochastic frontier model. Journal of Interactive Marketing 13 (Autumn): 2–9.

    Article  Google Scholar 

  • Koning, R., P. Spring, T. Wansbeek. 2002. Joint modeling of primary and secondary action in database marketing. Working paper, Department of Economics, University of Groningen.

    Google Scholar 

  • Knott, A., A. Hayes, and S.A. Neslin. 2002. Next-product-to-buy models for cross-selling applications. Journal of Interactive Marketing 16 (Summer): 59–75.

    Article  Google Scholar 

  • Knox, G., and R. Van Oest. 2014. Customer complaints and recovery effectiveness: A customer base approach. Journal of Marketing 78 (September): 42–57.

    Article  Google Scholar 

  • Kumar, V., Y. Bhagwat, and X. Zhang. 2015. Regaining “lost” customers: The predictive power of first-lifetime behavior, the reason for defection, and the nature of the win-back offer. Journal of Marketing 79 (July): 34–55.

    Article  Google Scholar 

  • Kumar, V., J.A. Petersen, and R.P. Leone. 2010. Driving profitability by encouraging customer referrals: Who, when, and how. Journal of Marketing 74 (September): 1–17.

    Article  Google Scholar 

  • Kumar, V., and W. Reinartz. 2012. Customer relationship management, 2nd ed. Heidelberg: Springer.

    Book  Google Scholar 

  • Kumar, V., and D. Shah. 2015. The handbook of customer equity. Cheltenham, UK: Edward Elgar Publishers.

    Book  Google Scholar 

  • Kumar, V., R. Venkatesan, T. Bohling, and D. Beckmann. 2008. The power of CLV: Managing customer lifetime value at IBM. Marketing Science 27 (July–August): 585–599.

    Google Scholar 

  • Lamberti, L. 2013. Customer centricity: The construct and the operational antecedents. Journal of Strategic Marketing 21 (7): 588–612.

    Article  Google Scholar 

  • Lambrecht, A., and C. Tucker. 2013. When does retargeting work? Information specificity in online advertising. Journal of Marketing Research 50 (October): 561–576.

    Article  Google Scholar 

  • Larivière, B., and D. Van den Poel. 2005. Predicting customer retention and profitability by using random forests and regression forests techniques. Expert Systems with Applications 29 (2): 472–484.

    Article  Google Scholar 

  • Lemmens, A., and C. Croux. 2006. Bagging and boosting classification trees to predict churn. Journal of Marketing Research 43 (May): 276–286.

    Article  Google Scholar 

  • Lemmens, A., and S. Gupta. 2013. Managing churn to maximize profits. http://ssrn.com/abstract=2964906. Accessed 10 May 2017.

  • Levin, N., and J. Zahavi. 1998. Continuous predictive modeling–A comparative analysis. Journal of Interactive Marketing 12 (Spring): 5–22.

    Article  Google Scholar 

  • Lewis, M. 2006. Customer acquisition promotions and customer asset value. Journal of Marketing Research 43 (May): 195–203.

    Article  Google Scholar 

  • Lhoest-Snoeck, S., E. van Nierop, and P.C. Verhoef. 2014. For new customers only: A study on the effect of acquisition campaigns on a service company’s existing customers’ CLV. Journal of Interactive Marketing 28 (August): 210–224.

    Article  Google Scholar 

  • Li, H., and P.K. Kannan. 2014. Attributing conversions in a multichannel online marketing environment: An empirical model and a field experiment. Journal of Marketing Research 51 (February): 40–56.

    Google Scholar 

  • Li, S., B. Sun, and A.L. Montgomery. 2011. Cross-selling the right product to the right customer at the right time. Journal of Marketing Research 48 (August): 683–700.

    Article  Google Scholar 

  • Li, S., B. Sun, and R.T. Wilcox. 2005. Cross-selling sequentially ordered products: An application to consumer banking services. Journal of Marketing Research 42 (May): 233–239.

    Article  Google Scholar 

  • Lianos, G., & Sloev, I. 2013. Customer acquisition and customer retention in a monopolistically competitive industry. http://ssrn.com/abstract=2386586. Accessed 10 Aug 2016.

  • Libai, B., E. Muller, and R. Peres. 2013. Decomposing the value of word-of-mouth seeding programs: Acceleration versus expansion. Journal of Marketing Research 50 (April): 161–176.

    Article  Google Scholar 

  • Liu, H., J. Pancras, and M. Houtz. 2015. Managing customer acquisition risk using co-operative databases. Journal of Interactive Marketing 29 (February): 39–56.

    Article  Google Scholar 

  • Lu, T.L., and C. Boutilier. 2014. Dynamic segmentation for large-scale marketing optimization. ICML-2014 workshop on customer life-time value optimization in digital marketing, 31st international conference on machine learning (ICML 2014), Beijing, June 21–26.

    Google Scholar 

  • Ma, S., and J. Büschken. 2011. Counting your customers from an “always a share” perspective. Marketing Letters 22 (3): 243–257.

    Article  Google Scholar 

  • Ma, S., & Liu, J.-L. 2007. The MCMC approach for solving the Pareto/NBD model and possible extensions. Third international conference on natural computation (ICNC 2007), 505–512.

    Google Scholar 

  • Malthouse, E.C., and R. Elsner. 2006. Customisation with crossed-basis sub-segmentation. Journal of Database Marketing & Customer Strategy Management 14 (1): 40–50.

    Article  Google Scholar 

  • Mark, T., K.N. Lemon, M. Vandenbosch, J. Bulla, and A. Maruotti. 2013. Capturing the evolution of customer-firm relationships: How customers become more (or less) valuable over time. Journal of Retailing 89 (September): 231–245.

    Google Scholar 

  • Mark, T., K.N. Lemon, and M. Vandenbosch. 2014. Customer migration patterns: Evidence from a North American retailer. Journal of Marketing Theory and Practice 22 (3): 251–269.

    Article  Google Scholar 

  • McCarthy, D., P.S. Fader, and B.G.S. Hardie. 2017. Valuing subscription-based businesses using publicly disclosed customer data. Journal of Marketing 81 (January): 17–35.

    Google Scholar 

  • Moffett, T. 2014. Use cross-channel attribution to understand marketing effectiveness. Cambridge, MA: Forrester Research Inc.

    Google Scholar 

  • Montoya, R., O. Netzer, and K. Jedidi. 2010. Dynamic allocation of pharmaceutical detailing and sampling for long-term profitability. Marketing Science 29 (September–October): 909–924.

    Google Scholar 

  • Moon, S., and G.J. Russell. 2008. Predicting product purchase from inferred customer similarity: An autologistic model approach. Management Science 54 (January): 71–82.

    Article  Google Scholar 

  • Morwitz, V.G., and D.C. Schmittlein. 1998. Testing new direct marketing offerings: The interplay of management judgment and statistical models. Management Science 44 (May): 610–628.

    Article  Google Scholar 

  • Mozer, M.C., R. Wolniewicz, D.B. Grimes, E. Johnson, and H. Kaushansky. 2000. Predicting subscriber dissatisfaction and improving retention in the wireless telecommunications industry. IEEE Transactions on Neural Networks 11 (3): 690–696.

    Article  Google Scholar 

  • Musalem, A., and Y.V. Joshi. 2009. How much should you invest in each customer relationship? A competitive strategic approach. Marketing Science, 28 (May–June): 555–565.

    Google Scholar 

  • Muus, L., H. van der Scheer, and T. Wansbeek. 2002. A decision theoretic framework for profit maximization in direct marketing. In Econometric models in marketing advances in econometrics, vol. 16, ed. P.H. Franses, and A.L. Montgomery, 119–140. Oxford: Elsevier Science.

    Google Scholar 

  • Natter, M., A.-M. Ozimec, and J.-Y. Kim. 2015. ECO: Entega’s profitable new customer acquisition on online price comparison sites. Marketing Science, 34 (November–December): 789–803.

    Google Scholar 

  • Neslin, S.A., S. Gupta, W. Kamakura, J. Lu, and C.H. Mason. 2006. Defection detection: Measuring and understanding the predictive accuracy of customer churn models. Journal of Marketing Research 43 (May): 204–211.

    Article  Google Scholar 

  • Neslin, S.A., G.A. Taylor, K.D. Grantham, and K.R. McNeil. 2013. Overcoming the “recency trap” in customer relationship management. Journal of the Academy of Marketing Science 41 (May): 320–337.

    Google Scholar 

  • Netzer, O., J.M. Lattin, V. Srinivasan. 2008. A hidden Markov model of customer relationship dynamics. Marketing Science, 27 (March–April): 185–204.

    Google Scholar 

  • Nitzan, I., and B. Libai. 2011. Social effects on customer retention. Journal of Marketing 75 (November): 24–38.

    Article  Google Scholar 

  • Nobibon, F.T., R. Leus, and F.C.R. Spieksma. 2011. Optimization models for targeted offers in direct marketing: Exact and heuristic algorithms. European Journal of Operational Research 210 (3): 670–683.

    Article  Google Scholar 

  • Otter, P.W., H. van der Scheer, and T.J. Wansbeek. 2000. Optimal selection of households for direct marketing by joint modeling of the probability and quantity of response. CCSO Working paper 2006/06, CCSO Centre for Economic Research, University of Groningen.

    Google Scholar 

  • Ovchinnikov, A., B. Boulu-Reshef, and P.E. Pfeifer. 2014. Balancing acquisition and retention spending for firms with limited capacity. Management Science 60 (August): 2002–2019.

    Article  Google Scholar 

  • Paas, L.J. 1998. Mokken scaling characteristic sets and acquisition patterns of durable and financial products. Journal of Economic Psychology 19 (June): 353–376.

    Article  Google Scholar 

  • Padilla, N., and E. Ascarza. 2017. First impressions count: Leveraging acquisition data for customer management. http://ssrn.com/abstract=2933291. Accessed 5 May 2017.

  • Park, C.H., Y.-H. Park, and D.A. Schweidel. 2014. A multi-category customer base analysis. International Journal of Research in Marketing 31 (September): 266–279.

    Article  Google Scholar 

  • Passant, P. 1995. From the practitioners: Retention marketing needs a new vision. Journal of Direct Marketing 9 (Spring): 2–4.

    Google Scholar 

  • Peres, R., E. Muller, and V. Mahajan. 2010. Innovation diffusion and new product growth models: A critical review and research directions. International Journal of Research in Marketing 27 (June): 91–106.

    Article  Google Scholar 

  • Petrison, L.A., R.C. Blattberg, and P. Wang. 1997. Database marketing: Past, present, and future. Journal of Direct Marketing 11 (Fall): 109–125.

    Article  Google Scholar 

  • Pfeifer, P.E. 1998. On using the beta-logistic model to update response probabilities given nonresponse. Journal of Interactive Marketing 12 (Spring): 23–32.

    Article  Google Scholar 

  • Pfeifer, P.E. 2005. The optimal ratio of acquisition and retention costs. Journal of Targeting, Measurement and Analysis for Marketing 13 (2): 179–188.

    Article  Google Scholar 

  • Pfeifer, P.E., and R.L. Carraway. 2000. Modeling customer relationships as Markov chains. Journal of Interactive Marketing 14 (Spring): 43–55.

    Google Scholar 

  • Pfeifer, P.E., M.E. Haskins, and R.M. Conroy. 2005. Customer lifetime value, customer profitability, and the treatment of acquisition spending. Journal of Managerial Issues 17 (Spring): 11–25.

    Google Scholar 

  • Pfeifer, P.E., and A. Ovchinnikov. 2011. A note on willingness to spend and customer lifetime value for firms with limited capacity. Journal of Interactive Marketing 25 (August): 178–189.

    Article  Google Scholar 

  • Piatetsky-Shapiro, G., and B. Masand. 1999. Estimating campaign benefits and modeling lift. Proceedings of the fifth ACM SIGKDD international conference on knowledge discovery and data mining, 185–193. Association for Computing Machinery: New York, NY.

    Google Scholar 

  • Pick, D., J.S. Thomas, S. Tillmanns, and M. Krafft. 2016. Customer win-back: The role of attributions and perceptions in customers’ willingness to return. Journal of the Academy of Marketing Science 44 (March): 218–240.

    Google Scholar 

  • Piersma, N., and J.-J. Jonker. 2004. Determining the optimal direct mailing frequency. European Journal of Operational Research 158 (1): 173–182.

    Article  Google Scholar 

  • Platzer, M. (2008). Stochastic models of noncontractual consumer relationships. Unpublished Master’s thesis, Vienna University of Economics and Business Administration.

    Google Scholar 

  • Potter, R.G., and M.P. Parker. 1964. Predicting the time required to conceive. Population Studies 18 (1): 99–116.

    Article  Google Scholar 

  • Prinzie, A., and D. Van den Poel. 2006. Investigating purchasing-sequence patterns for financial services using Markov, MTD and MTDg models. European Journal of Operational Research 170 (3): 710–734.

    Article  Google Scholar 

  • Prinzie, A., and D. Van den Poel. 2007. Predicting home-appliance acquisition sequences: Markov/Markov for discrimination and survival analysis for modeling sequential information in NPTB models. Decision Support Systems 44 (November): 28–45.

    Article  Google Scholar 

  • Rao, V.R., and J.H. Steckel. 1995. Selecting, evaluating, and updating prospects in direct mail marketing. Journal of Direct Marketing 9 (Spring): 20–31.

    Article  Google Scholar 

  • Ravi, R., and B. Sun. 2016. Customer-centric marketing: A pragmatic framework. Cambridge, MA: The MIT Press.

    Google Scholar 

  • Reinartz, W., and V. Kumar. 2000. On the profitability of long-life customers in a noncontractual setting: An empirical investigation and implications for marketing. Journal of Marketing 64 (October): 17–35.

    Article  Google Scholar 

  • Reinartz, W., and V. Kumar. 2003. The impact of customer relationship characteristics on profitable lifetime duration. Journal of Marketing 67 (January): 77–99.

    Article  Google Scholar 

  • Reinartz, W., J.S. Thomas, and V. Kumar. 2005. Balancing acquisition and retention resources to maximize customer profitability. Journal of Marketing 69 (January): 63–79.

    Article  Google Scholar 

  • Rhee, S., and S. McIntyre. 2008. Including the effects of prior and recent contact effort in a customer scoring model for database marketing. Journal of the Academy of Marketing Science 36 (December): 538–551.

    Article  Google Scholar 

  • Rhee, E., and S. McIntyre. 2009. How current targeting can hinder targeting in the future and what to do about it. Journal of Database Marketing & Customer Strategy Management 16 (1): 15–28.

    Article  Google Scholar 

  • Rhee, E., and G.J. Russell. 2009. Forecasting household response in database marketing: A latent trait approach. In Advances in business and management forecasting, vol. 6, ed. K.D. Lawrence, and R.K. Klimberg, 109–131. Bingley, UK: JAI Press/Emerald Group Publishing.

    Chapter  Google Scholar 

  • Ricci, F., L. Rokach, and B. Shapira. 2015. Recommender systems handbook, 2nd ed. New York, NY: Springer.

    Book  Google Scholar 

  • Risselada, H., P.C. Verhoef, and T.H.A. Bijmolt. 2010. Staying power of churn prediction models. Journal of Interactive Marketing 24 (August): 198–208.

    Article  Google Scholar 

  • Romero, J., R. Van der Lans, and B. Wierenga. 2013. A partially hidden Markov model of customer dynamics for CLV measurement. Journal of Interactive Marketing 27 (August): 185–208.

    Article  Google Scholar 

  • Rosenwald, P.J. 2004. Accountable marketing: The economics of data-driven marketing. New York, NY: Texere.

    Google Scholar 

  • Rosset, S., E. Neumann, U. Eick, and N. Vatnik. 2003. Customer lifetime value models for decision support. Data Mining and Knowledge Discovery 7 (July): 321–339.

    Article  Google Scholar 

  • Rust, R.T., K.N. Lemon, and V.A. Zeithaml. 2004. Return on marketing: Using customer equity to focus marketing strategy. Journal of Marketing 68 (January): 109–127.

    Article  Google Scholar 

  • Schmitt, P., B. Skiera, and C. Van den Bulte. 2011. Referral programs and customer value. Journal of Marketing 75 (January): 46–59.

    Article  Google Scholar 

  • Schmittlein, D.C., D.G. Morrison, and R. Colombo. 1987. Counting your customers: Who they are and what will they do next? Management Science 33 (January): 1–24.

    Article  Google Scholar 

  • Schmittlein, D.C., and R.A. Peterson. 1994. Customer base analysis: An industrial purchase process application. Marketing Science 13 (Winter): 41–67.

    Article  Google Scholar 

  • Schröder, N., and H. Hruschka. 2016. Investigating the effects of mailing variables and endogeneity on mailing decisions. European Journal of Operational Research 250 (2): 579–589.

    Article  Google Scholar 

  • Schulze, C., B. Skiera, and T. Wiesel. 2012. Linking customer and financial metrics to shareholder value: The leverage effect in customer-based valuation. Journal of Marketing 76 (March): 17–32.

    Article  Google Scholar 

  • Schwartz, E.M., E.T. Bradlow, P.S. Fader. 2014. Model selection using database characteristics: Developing a classification tree for longitudinal incidence data. Marketing Science, 33 (March–April): 188–205.

    Google Scholar 

  • Schwartz, E.M., E. Bradlow, P. Fader. 2016. Customer acquisition via display advertising using multi-armed bandit experiments. Marketing Science, forthcoming.

    Google Scholar 

  • Schweidel, D.A., E.T. Bradlow, and P.S. Fader. 2011. Portfolio dynamics for customers of a multiservice provider. Management Science 57 (March): 471–486.

    Article  Google Scholar 

  • Schweidel, D.A., P.S. Fader, and E.T. Bradlow. 2008a. Understanding service retention within and across cohorts using limited information. Journal of Marketing 72 (January): 82–94.

    Article  Google Scholar 

  • Schweidel, D.A., P.S. Fader, E.T. Bradlow. 2008b. A bivariate timing model of customer acquisition and retention. Marketing Science, 27 (September–October): 829–843.

    Google Scholar 

  • Schweidel, D.A., and G. Knox. 2013. Incorporating direct marketing activity into latent attrition models. Marketing Science, 32 (May–June): 471–487.

    Google Scholar 

  • Schweidel, D.A., Y.-H. Park, Z. Jamal. 2014. A multiactivity latent attrition model for customer base analysis. Marketing Science, 33 (March–April): 273–286.

    Google Scholar 

  • Seybold, P.B., R.T. Marshak, and J.M. Lewis. 2001. The customer revolution. New York, NY: Random House.

    Google Scholar 

  • Shao, X., and L. Li. 2011. Data-driven multi-touch attribution models. Proceedings of the 17th ACM SIGKDD international conference on knowledge discovery and data mining, 258–264. Association for Computing Machinery: New York, NY.

    Google Scholar 

  • Simester, D.I., P. Sun, and J.N. Tsitsiklis. 2006. Dynamic catalog mailing policies. Management Science 52 (May): 683–696.

    Article  Google Scholar 

  • Simon, J.L. 1967. Expenditure policy for mail-order advertisers. Journal of Marketing Research 4 (February): 59–61.

    Google Scholar 

  • Simon, J.L. 1993. How to start and operate a mail-order business, 5th ed. New York, NY: McGraw-Hill.

    Google Scholar 

  • Singh, S.S., S. Borle, and D.C. Jain. 2009. A generalized framework for estimating customer lifetime value when customer lifetimes are not observed. Quantitative Marketing and Economics 7 (2): 181–205.

    Article  Google Scholar 

  • Soukup, D.J. 1983. A Markov analysis of fund-raising alternatives. Journal of Marketing Research 20 (August): 314–319.

    Google Scholar 

  • Stanford, R.E., W.S. Martin, and G.C. Myers. 1996. Fundraising vs. contributor prospecting tradeoffs in direct mail response rate management: A linear programming analysis. Journal of Direct 10 (Autumn): 8–18.

    Google Scholar 

  • Stauss, B., and C. Friege. 1999. Regaining service customers: Costs and benefits of regain management. Journal of Service Research 1 (4): 347–361.

    Article  Google Scholar 

  • Steffes, E.M., B.P.S. Murthi, and R.C. Rao. 2011. Why are some modes of acquisition more profitable? A study of the credit card industry. Journal of Financial Services Marketing 16 (2): 90–100.

    Article  Google Scholar 

  • Swain, S.D., P.D. Berger, and B.D. Weinberg. 2014. The customer equity implications of using incentives in acquisition channels: A nonprofit application. Journal of Marketing Analytics 2 (1): 1–17.

    Article  Google Scholar 

  • Tapp, A., I. Whitten, and M. Housden. 2014. Principles of direct, database, and digital marketing, 5th ed. Harlow, UK: Pearson Education Ltd.

    Google Scholar 

  • Tellis, G.J., R.K. Chandy, and P. Thaivanich. 2000. Which ad works, when, where, and how often? Modeling the effects of direct television advertising. Journal of Marketing Research 37 (February): 32–46.

    Article  Google Scholar 

  • Thomas, J.S. 2001. A methodology for linking customer acquisition to customer retention. Journal of Marketing Research 38 (May): 262–268.

    Article  Google Scholar 

  • Thomas, J.S., R.C. Blattberg, and E.J. Fox. 2004. Recapturing lost customers. Journal of Marketing Research 41 (February): 31–45.

    Article  Google Scholar 

  • Thomas, S.A., S. Feng, and T.V. Krishnan. 2015. To retain? To upgrade? The effects of direct mail on regular donation behavior. International Journal of Research in Marketing 32 (March): 48–63.

    Article  Google Scholar 

  • Trusov, M., R.E. Bucklin, and K. Pauwels. 2009. Effects of word-of-mouth versus traditional marketing: Findings from an internet social networking site. Journal of Marketing 73 (September): 90–102.

    Article  Google Scholar 

  • Tsao, H.Y., C. Campbell, J. Ma, and L. Pitt. 2014. Budget allocation to grow market share and maximize customer equity: The effect of inertial segment size. Journal of Marketing Analytics 2 (4): 205–217.

    Article  Google Scholar 

  • Vaidya, R., and N. Cassidy. 1999. Prioritizing leads using response probabilities and expected purchase amount. In Advanced Research Techniques Forum, Santa Fe, NM, June 13–16.

    Google Scholar 

  • van Diepen, M., B. Donkers, and P.H. Franses. 2009. Dynamic and competitive effects of direct mailings: A charitable giving application. Journal of Marketing Research 46 (February): 120–133.

    Article  Google Scholar 

  • Van den Bulte, C., E. Bayer, B. Skiera, P. Schmitt. 2015. How customer referral programs turn social capital into economic capital. Marketing Science Institute Working Paper Series, Report No. 15-102.

    Google Scholar 

  • Van der Lans, R., G. van Bruggen, J. Eliashberg, B. Wierenga. 2010. A viral branching model for predicting the spread of electronic word of mouth. Marketing Science, 29 (March–April): 348–365.

    Google Scholar 

  • Venkatesan, R., and V. Kumar. 2004. A customer lifetime value framework for customer selection and resource allocation strategy. Journal of Marketing 68 (October): 106–125.

    Article  Google Scholar 

  • Venkatesan, R., V. Kumar, and T. Bohling. 2007. Optimal customer relationship management using Bayesian decision theory: An application for customer selection. Journal of Marketing Research 44 (November): 579–594.

    Article  Google Scholar 

  • Verbeke, W., K. Dejaeger, D. Martens, J. Hur, and B. Baesens. 2012. New insights into churn prediction in the telecommunication sector: A profit driven data mining approach. European Journal of Operational Research 218 (1): 211–229.

    Article  Google Scholar 

  • Verbeke, W., D. Martens, B. Baesens. 2014. Social network analysis for customer churn prediction. Applied Soft Computing 14 (Part C): 431–446.

    Google Scholar 

  • Verbeke, W., D. Martens, C. Mues, and B. Baesens. 2011. Building comprehensible customer churn prediction models with advanced rule induction techniques. Expert Systems with Applications 38 (3): 2354–2364.

    Article  Google Scholar 

  • Verbraken, T., W. Verbeke, and B. Baesens. 2013. A novel profit maximizing metric for measuring classification performance of customer churn prediction models. IEEE Transactions on Knowledge and Data Engineering 25 (May): 961–973.

    Article  Google Scholar 

  • Verhoef, P.C., and B. Donkers. 2001. Predicting customer potential value an application in the insurance industry. Decision Support Systems 32 (December): 189–199.

    Article  Google Scholar 

  • Verhoef, P.C., and B. Donkers. 2005. The effect of acquisition channels on customer loyalty and cross-buying. Journal of Interactive Marketing 19 (Spring): 31–43.

    Article  Google Scholar 

  • Whyte, W.H., Jr. (1954). The web of word of mouth. Fortune, 50 (November): 140–143, 204, 206, 208, 210, 212.

    Google Scholar 

  • Williams, C., and R. Williams. 2015. Optimizing acquisition and retention spending to maximize market share. Journal of Marketing Analytics 3 (3): 159–170.

    Article  Google Scholar 

  • Wübben, M., and F. von Wangenheim. 2008. Instant customer base analysis: Managerial heuristics often ‘get it right’. Journal of Marketing 72 (May): 82–93.

    Google Scholar 

  • Xu, L., J.A. Duan, and A. Whinston. 2014. Path to purchase: A mutually exciting point process model for online advertising and conversion. Management Science 60 (June): 1392–1412.

    Google Scholar 

  • Ying, Y., F. Feinberg, and M. Wedel. 2006. Leveraging missing ratings to improve online recommendation systems. Journal of Marketing Research 43 (August): 355–365.

    Google Scholar 

  • Zhang, X., J. Zhu, S. Xu, and Y. Wan. 2012. Predicting customer churn through interpersonal influence. Knowledge-Based Systems 28 (April): 97–104.

    Article  Google Scholar 

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Authors and Affiliations

  1. Columbia Business School, Uris Hall, 3022 Broadway, New York, NY, 10027-6902, USA

    Eva Ascarza

  2. The Wharton School, University of Pennsylvania, 3730 Walnut Street, Philadelphia, PA, 19104-6340, USA

    Peter S. Fader

  3. London Business School, Regent’s Park, London, NW1 4SA, UK

    Bruce G. S. Hardie

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  1. Rotterdam School of Management, Erasmus University, Rotterdam, South Holland, The Netherlands

    Berend Wierenga

  2. Department of Marketing, Hong Kong University of Science and Technology, Kowloon, Hong Kong

    Ralf van der Lans

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Ascarza, E., Fader, P.S., Hardie, B.G.S. (2017). Marketing Models for the Customer-Centric Firm. In: Wierenga, B., van der Lans, R. (eds) Handbook of Marketing Decision Models. International Series in Operations Research & Management Science, vol 254. Springer, Cham. https://doi.org/10.1007/978-3-319-56941-3_10

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