Some Customers Would Rather Leave Without Saying Goodbye

References

• Ascarza E (2018) Retention futility: Targeting high risk customers might be ineffective. J. Marketing Res. 55(1):80–98.Crossref, Google Scholar
• Ascarza E, Hardie BGS (2013) A joint model of usage and churn in contractual settings. Marketing Sci. 32(4):570–590.Link, Google Scholar
• Ascarza E, Iyengar R, Schleicher M (2016) The perils of proactive churn prevention using plan recommendations: Evidence from a field experiment. J. Marketing Res. 53(1):46–60.Crossref, Google Scholar
• Blattberg RC, Kim BD, Neslin SA (2008) Database Marketing: Analyzing and Managing Customers (Springer, New York).Crossref, Google Scholar
• Bonfrer A, Drèze X (2009) Real-time evaluation of e-mail campaign performance. Marketing Sci. 28(2):251–263.Link, Google Scholar
• Borle S, Singh SS, Jain DC (2008) Customer lifetime value measurement. Management Sci. 54(1):100–112.Link, Google Scholar
• Braun M, Schweidel DA (2011) Modeling customer lifetimes with multiple causes of churn. Marketing Sci. 30(5):881–902.Link, Google Scholar
• Braun M, Schweidel DA, Stein E (2015) Transaction attributes and customer valuation. J. Marketing Res. 52(6):848–864.Crossref, Google Scholar
• Buckinx W, Verstraeten G, Van den Poel D (2007) Predicting customer loyalty using the internal transactional database. Expert Systems Appl. 32(1):125–134.Crossref, Google Scholar
• Chen K, Hu YH, Hsieh YC (2015) Predicting customer churn from valuable B2B customers in the logistics industry: A case study. Inform. Systems e-Bus. Management 13(3):475–494.Crossref, Google Scholar
• Coussement K, De Bock KW (2013) Customer churn prediction in the online gambling industry: The beneficial effect of ensemble learning. J. Bus. Res. 66(9):1629–1636.Crossref, Google Scholar
• Coussement K, Van den Poel D (2008) Churn prediction in subscription services: An application of support vector machines while comparing two parameter-selection techniques. Expert Systems Appl. 34(1):313–327.Crossref, Google Scholar
• Danaher PJ, Bonfrer A, Dhar S (2008) The effect of competitive advertising interference on sales for packaged goods. J. Marketing Res. 45(2):211–225.Crossref, Google Scholar
• Deming WE, Glasser GJ (1968) A Markovian analysis of the life of newspaper subscriptions. Management Sci. 14(6):B283–B293.Link, Google Scholar
• Drèze X, Bonfrer A (2008) An empirical investigation of the impact of communication timing on customer equity. J. Interactive Marketing 22(1):36–50.Crossref, Google Scholar
• Dwyer FR (1989) Customer lifetime valuation to support marketing decision making. J. Direct Marketing 3(4):8–15.Crossref, Google Scholar
• Fader PS, Hardie BGS (2007) How to project customer retention. J. Interactive Marketing 21(1):76–90.Crossref, Google Scholar
• Fader PS, Hardie BGS, Lee KL (2005) “Counting your customers” the easy way: An alternative to the Pareto/NBD model. Marketing Sci. 24(2):275–284.Link, Google Scholar
• Fader PS, Hardie BGS, Sen S (2014) Stochastic models of buyer behavior. Winer RS, Neslin SA, eds. The History of Marketing Science (World Scientific Publishing, Singapore), 165–205.Crossref, Google Scholar
• Fader PS, Hardie BGS, Shang J (2010) Customer-base analysis in a discrete-time noncontractual setting. Marketing Sci. 29(6):1086–1108.Link, Google Scholar
• Gelman A, Carlin JB, Stern HA, Dunson DB, Vehtari A, Rubin DB (2013) Bayesian Data Analysis, 3rd ed. (Chapman & Hall/CRC, London).Crossref, Google Scholar
• Knox G, Van Oest R (2014) Customer complaints and recovery effectiveness: A customer base approach. J. Marketing 78(5):42–57.Crossref, Google Scholar
• Kumar V, Zhang X, Luo A (2014) Modeling customer opt-in and opt-out in a permission-based marketing context. J. Marketing Res. 51(4):403–419.Crossref, Google Scholar
• Lemmens A, Croux C (2006) Bagging and boosting classification trees to predict churn. J. Marketing Res. 43(2):276–286.Crossref, Google Scholar
• Mason CH (2003) Tuscan lifestyles: Assessing customer lifetime value. J. Interactive Marketing 17(4):54–60.Crossref, Google Scholar
• Montoya R, Netzer O, Jedidi K (2010) Dynamic allocation of pharmaceutical detailing and sampling for long-term profitability. Marketing Sci. 29(5):909–924.Link, Google Scholar
• Neslin SA, Gupta S, Kamakura W, Lu J, Mason CH (2006) Defection detection: Measuring and understanding the predictive accuracy of customer churn models. J. Marketing Res. 43(2):204–211.Crossref, Google Scholar
• Netzer O, Lattin JM, Srinivasan V (2008) A hidden Markov model of customer relationship dynamics. Marketing Sci. 27(2):185–204.Link, Google Scholar
• Pfeifer PE, Carraway RL (2000) Modeling customer relationships as Markov chains. J. Interactive Marketing 14(2):43–55.Crossref, Google Scholar
• Reinartz WJ (1999) Customer lifetime value analysis: An integrated empirical framework for measurement and explanation. Unpublished doctoral dissertation, College of Business Administration, University of Houston, Houston.Google Scholar
• Return Path (2014) Benchmark report: 2014 sender score. Accessed May 13, 2015, http://returnpath.com/research/email-marketing-sender-score-benchmark-report-2014/.Google Scholar
• Schmittlein DC, Morrison DG, Colombo R (1987) Counting your customers: Who are they and what will they do next? Management Sci. 33(1):1–24.Link, Google Scholar
• Schwartz EM, Bradlow ET, Fader PS (2014) Model selection using database characteristics: Developing a classification tree for longitudinal incidence data. Marketing Sci. 33(2):188–205.Link, Google Scholar
• Schweidel DA, Knox G (2013) Incorporating direct marketing activity into latent attrition models. Marketing Sci. 32(3):471–487.Link, Google Scholar
• Schweidel DA, Bradlow ET, Fader PS (2011) Portfolio dynamics for customers of a multi-service provider. Management Sci. 57(3):471–486.Link, Google Scholar
• Schweidel DA, Fader PS, Bradlow ET (2008) Understanding service retention within and across cohorts using limited information. J. Marketing 72(1):82–94.Crossref, Google Scholar
• Schweidel DA, Park YH, Jamal Z (2014) A multiactivity latent attrition model for customer base analysis. Marketing Sci. 33(2):273–286.Link, Google Scholar
• Silverpop (2013) 2013 email marketing metrics benchmark study: An analysis of messages sent Q1–Q4, 2012. Accessed May 13, 2015, http://www.silverpop.com/marketing-resources/white-papers/all/2013/2013-EM-Benchmark/.Google Scholar
• Zhang JZ, Netzer O, Ansari A (2014) Dynamic targeted pricing in B2B relationships. Marketing Sci. 33(3):317–337.Link, Google Scholar
• Zucchini W, MacDonald IL (2009) Hidden Markov Models for Time Series: An Introduction Using R (CRC Press, Boca Raton, FL).Crossref, Google Scholar