A Joint Model of Usage and Churn in Contractual Settings

Published Online:https://doi.org/10.1287/mksc.2013.0786

References

  • Berry MJA, Linoff GS. Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management (2004) (Wiley Publishing, Indianapolis) Google Scholar
  • Bhattacharya CB. When customers are members: Customer retention in paid membership context. J. Marketing (1998) 26(1):31–44CrossrefGoogle Scholar
  • Blattberg RC, Getz G, Thomas JS. Customer Equity: Building and Managing Relationships as Valuable Assets (2001) (Harvard Business School Press, Boston) Google Scholar
  • Blattberg RC, Kim B-D, Neslin SA. Database Marketing. Analyzing and Managing Customers (2008) (Springer, New York) CrossrefGoogle Scholar
  • Bolton RN. A dynamic model of the duration of the customer's relationship with a continuous service provider: The role of satisfaction. Marketing Sci. (1998) 17(1):45–65LinkGoogle Scholar
  • Bolton RN, Lemon KN. A dynamic model of customers' usage of services: Usage as an antecedent and consequence of satisfaction. J. Marketing Res. (1999) 36(2):171–186CrossrefGoogle Scholar
  • Bonfrer A, Knox G, Eliashberg J, Chiang J. A first-passage time model for predicting inactivity in a contractual setting. (2010) . Working paper, Australian National University, Canberra, ACT. http://ssrn.com/abstract=997810CrossrefGoogle Scholar
  • Borle S, Singh S S, Jain D C. Customer lifetime value measurement. Management Sci. (2008) 54(1):100–112LinkGoogle Scholar
  • Chintagunta PK. Investigating purchase incidence, brand choice and purchase quantity decisions of households. Marketing Sci. (1993) 12(Spring):184–204LinkGoogle Scholar
  • Cook RJ, Lawless JF. The Statistical Analysis of Recurrent Events (2007) (Springer, New York) Google Scholar
  • Danaher PJ. Optimal pricing of new subscription services: Analysis of a market experiment. Marketing Sci. (2002) 21(2):119–138LinkGoogle Scholar
  • Diggle P, Kenward MG. Informative drop-out in longitudinal data analysis. Appl. Statist. (1994) 43(1):49–93CrossrefGoogle Scholar
  • Essegaier S, Gupta S, Zhang ZJ. Pricing access services. Marketing Sci. (2002) 21(2):139–159LinkGoogle Scholar
  • Fader P. Customer Centricity (2012) 2nd ed.(Wharton Digital Press, Philadelphia) Google Scholar
  • Fader PS, Hardie BGS. Customer-base valuation in a contractual setting: The perils of ignoring heterogeneity. Marketing Sci. (2010) 29(1):85–93LinkGoogle Scholar
  • Fader PS, Hardie BGS, Huang C-Y. A dynamic changepoint model for new product sales forecasting. Marketing Sci. (2004) 23(1):50–65LinkGoogle Scholar
  • Fader PS, Hardie BGS, Lee KL. RFM and CLV: Using iso-value curves for customer base analysis. J. Marketing Res. (2005) 42(November):415–430CrossrefGoogle Scholar
  • Fader PS, Hardie BGS, Shang J. Customer-base analysis in a discrete-time noncontractual setting. Marketing Sci. (2010) 29(6):1086–1108LinkGoogle Scholar
  • Garbarino E, Johnson MS. The different roles of satisfaction, trust, and commitment in customer relationships. J. Marketing (1999) 63(2):70–87CrossrefGoogle Scholar
  • Gruen TW, Summers JO, Acito F. Relationship marketing activities, commitment, and membership behaviors in professional associations. J. Marketing (2000) 64(3):34–49CrossrefGoogle Scholar
  • Hanemann WM. Discrete/continuous models of consumer demand. Econometrica (1984) 52(3):541–561CrossrefGoogle Scholar
  • Hashemi R, Janqmin-Dagga H, Commenges D. A latent process model for joint modeling of events and marker. Lifetime Data Anal. (2003) 9(4):331–343CrossrefGoogle Scholar
  • Hausman J, Wise DA. Attrition bias in experimental and panel data: The Gary income maintenance experiment. Econometrica (1979) 47(2):455–473CrossrefGoogle Scholar
  • Henderson R, Diggle P, Dobson A. Joint modelling of longitudinal measurements and event time data. Biostatistics (2000) 1(4):465–480CrossrefGoogle Scholar
  • Krishnamurthi L, Raj SP. A model of brand choice and purchase quantity price sensitivities. Marketing Sci. (1988) 7(1):1–20LinkGoogle Scholar
  • Kumar V, Venkatesan R, Bohling T, Beckmann D. The power of CLV: Managing customer lifetime value at IBM. Marketing Sci. (2008) 27(4):585–599LinkGoogle Scholar
  • Larivière B, Van de n Poel D. Predicting customer retention and profitability by using random forests and regression forests techniques. Expert Systems Appl. (2005) 29(2):472–484CrossrefGoogle Scholar
  • Lemmens A, Croux C. Bagging and boosting classification trees to predict churn. Marketing Res. (2006) 43(2):276–286CrossrefGoogle Scholar
  • Lemon KN, White TB, Winer RS. Dynamic customer relationship management: Incorporating future considerations into the service retention decision. J. Marketing (2002) 66(1):1–14CrossrefGoogle Scholar
  • Lu J. Predicting customer churn in the telecommunications industry—An application of survival analysis modeling using SAS®. SAS User Group Internat. (SUGI27) Online Proc. (2002) (SAS Institute, Cary, NC) . Paper 114Google Scholar
  • Moe WW, Fader PS. Capturing evolving visit behavior in clickstream data. J. Interactive Marketing (2004a) 18(1):5–19CrossrefGoogle Scholar
  • Moe WW, Fader PS. Dynamic conversion behavior at e-commerce sites. Management Sci. (2004b) 50(3):326–335LinkGoogle Scholar
  • Montoya R, Netzer O, Jedidi K. A dynamic allocation of pharmaceutical detailing and sampling for long-term profitability. Marketing Sci. (2010) 29(5):909–924LinkGoogle Scholar
  • Morgan RYH, Hunt SD. The commitment-trust theory of relationship marketing. J. Marketing (1994) 58(3):20–38CrossrefGoogle Scholar
  • Mozer MC, Wolniewicz R, Grimes DB, Johnson E, Kaushansky H. Predicting subscriber dissatisfaction and improving retention in the wireless telecommunications industry. IEEE Trans. Neural Networks (2000) 11(3):690–696CrossrefGoogle Scholar
  • Naik PA, Mantrala MK, Sawyer AG. Planning media schedules in the presence of dynamic advertising quality. Marketing Sci. (1998) 17(3):214–235LinkGoogle Scholar
  • Narayanan S, Chintagunta PK, Miravete EJ. The role of self-selection and usage uncertainty in the demand for local telephone service. Quant. Marketing Econom. (2007) 5(1):1–34CrossrefGoogle Scholar
  • Netzer O, Lattin JM, Srinivasan V. A hidden Markov model of customer relationship dynamics. Marketing Sci. (2008) 27(2):185–204LinkGoogle Scholar
  • Parr Rud O. Data Mining Cookbook: Modeling Data for Marketing, Risk, and Customer Relationship Management (2001) (John Wiley & Sons, New York) Google Scholar
  • Reinartz W, Kumar V. The impact of customer relationship characteristics on profitable lifetime duration. J. Marketing (2003) 67(1):77–99CrossrefGoogle Scholar
  • Risselada H, Verhoef PC, Bijmolt THA. Staying power of churn prediction models. J. Interactive Marketing (2010) 24(3):198–208CrossrefGoogle Scholar
  • Rust RT, Zahorik AJ. Customer satisfaction, customer retention, and market share. J. Retailing (1993) 69(2):193–215CrossrefGoogle Scholar
  • Rust RT, Zeithaml VA, Lemon KN. Driving Customer Equity: How Customer Lifetime Value Is Reshaping Corporate Strategy (2001) (Free Press, New York) Google Scholar
  • Sabavala DJ, Morrison DG. A nonstationary model of binary choice applied to media exposure. Management Sci. (1981) 27(6):637–657LinkGoogle Scholar
  • Schweidel DA, Bradlow ET, Fader PS. Modeling the evolution of customers' service portfolios. (2008a) . Working paper, Emory University, Atlanta. http://ssrn.com/abstract=985639CrossrefGoogle Scholar
  • Schweidel DA, Fader PS, Bradlow ET. Understanding service retention within and across cohorts using limited information. J. Marketing (2008b) 72(1):82–94CrossrefGoogle Scholar
  • Schweidel DA, Bradlow ET, Fader PS. Portfolio dynamics for customers of a multi-service provider. Management Sci. (2011) 57(3):471–486LinkGoogle Scholar
  • Scott SL, James GM, Sugar CA. Hidden Markov models for longitudinal comparisons. J. Amer. Statist. Assoc. (2005) 100(470):359–369CrossrefGoogle Scholar
  • Sriram S, Chintagunta PK, Manchanda P. The effects of service quality on usage and termination of a video on demand service. (2012) . Working paper, University of Michigan, Ann ArborGoogle Scholar
  • Venkatesan R, Kumar V. A customer lifetime value framework for customer selection and resource allocation strategy. J. Marketing (2004) 68(4):106–125CrossrefGoogle Scholar
  • Verhoef PC. Understanding the effect of customer relationship management efforts on customer retention and customer share development. J. Marketing (2003) 67(4):30–45CrossrefGoogle Scholar
  • Wooldridge JM. Econometric Analysis of Cross Section and Panel Data (2002) (MIT Press, Cambridge, MA) Google Scholar
  • Wübben M, Wangenheim F. Instant customer base analysis: Managerial heuristics often get it right. J. Marketing (2008) 72(3):82–93CrossrefGoogle Scholar
  • Xie JX, Song M, Sirbu M, Wang Q. Kalman filter estimation of new product diffusion models. J. Marketing Res. (1997) 34(3):378–393CrossrefGoogle Scholar
  • Xu J, Zeger SL. Joint analysis of longitudinal data comprising repeated measures and times to events. Appl. Statist. (2001) 50(3):375–387Google Scholar
INFORMS site uses cookies to store information on your computer. Some are essential to make our site work; Others help us improve the user experience. By using this site, you consent to the placement of these cookies. Please read our Privacy Statement to learn more.