A Joint Model of Usage and Churn in Contractual Settings
Published Online:22 May 2013https://doi.org/10.1287/mksc.2013.0786
References
- . Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management (2004) (Wiley Publishing, Indianapolis) Google Scholar
- . When customers are members: Customer retention in paid membership context. J. Marketing (1998) 26(1):31–44Crossref, Google Scholar
- . Customer Equity: Building and Managing Relationships as Valuable Assets (2001) (Harvard Business School Press, Boston) Google Scholar
- . Database Marketing. Analyzing and Managing Customers (2008) (Springer, New York) Crossref, Google Scholar
- . 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):45–65Link, Google Scholar
- . A dynamic model of customers' usage of services: Usage as an antecedent and consequence of satisfaction. J. Marketing Res. (1999) 36(2):171–186Crossref, Google Scholar
- . A first-passage time model for predicting inactivity in a contractual setting. (2010) . Working paper, Australian National University, Canberra, ACT. http://ssrn.com/abstract=997810Crossref, Google Scholar
- . Customer lifetime value measurement. Management Sci. (2008) 54(1):100–112Link, Google Scholar
- . Investigating purchase incidence, brand choice and purchase quantity decisions of households. Marketing Sci. (1993) 12(Spring):184–204Link, Google Scholar
- . The Statistical Analysis of Recurrent Events (2007) (Springer, New York) Google Scholar
- . Optimal pricing of new subscription services: Analysis of a market experiment. Marketing Sci. (2002) 21(2):119–138Link, Google Scholar
- . Informative drop-out in longitudinal data analysis. Appl. Statist. (1994) 43(1):49–93Crossref, Google Scholar
- . Pricing access services. Marketing Sci. (2002) 21(2):139–159Link, Google Scholar
- . Customer Centricity (2012) 2nd ed.(Wharton Digital Press, Philadelphia) Google Scholar
- . Customer-base valuation in a contractual setting: The perils of ignoring heterogeneity. Marketing Sci. (2010) 29(1):85–93Link, Google Scholar
- . A dynamic changepoint model for new product sales forecasting. Marketing Sci. (2004) 23(1):50–65Link, Google Scholar
- . RFM and CLV: Using iso-value curves for customer base analysis. J. Marketing Res. (2005) 42(November):415–430Crossref, Google Scholar
- . Customer-base analysis in a discrete-time noncontractual setting. Marketing Sci. (2010) 29(6):1086–1108Link, Google Scholar
- . The different roles of satisfaction, trust, and commitment in customer relationships. J. Marketing (1999) 63(2):70–87Crossref, Google Scholar
- . Relationship marketing activities, commitment, and membership behaviors in professional associations. J. Marketing (2000) 64(3):34–49Crossref, Google Scholar
- . Discrete/continuous models of consumer demand. Econometrica (1984) 52(3):541–561Crossref, Google Scholar
- . A latent process model for joint modeling of events and marker. Lifetime Data Anal. (2003) 9(4):331–343Crossref, Google Scholar
- . Attrition bias in experimental and panel data: The Gary income maintenance experiment. Econometrica (1979) 47(2):455–473Crossref, Google Scholar
- . Joint modelling of longitudinal measurements and event time data. Biostatistics (2000) 1(4):465–480Crossref, Google Scholar
- . A model of brand choice and purchase quantity price sensitivities. Marketing Sci. (1988) 7(1):1–20Link, Google Scholar
- . The power of CLV: Managing customer lifetime value at IBM. Marketing Sci. (2008) 27(4):585–599Link, Google Scholar
- . Predicting customer retention and profitability by using random forests and regression forests techniques. Expert Systems Appl. (2005) 29(2):472–484Crossref, Google Scholar
- . Bagging and boosting classification trees to predict churn. Marketing Res. (2006) 43(2):276–286Crossref, Google Scholar
- . Dynamic customer relationship management: Incorporating future considerations into the service retention decision. J. Marketing (2002) 66(1):1–14Crossref, Google Scholar
- . 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
- . Capturing evolving visit behavior in clickstream data. J. Interactive Marketing (2004a) 18(1):5–19Crossref, Google Scholar
- . Dynamic conversion behavior at e-commerce sites. Management Sci. (2004b) 50(3):326–335Link, Google Scholar
- . A dynamic allocation of pharmaceutical detailing and sampling for long-term profitability. Marketing Sci. (2010) 29(5):909–924Link, Google Scholar
- . The commitment-trust theory of relationship marketing. J. Marketing (1994) 58(3):20–38Crossref, Google Scholar
- . Predicting subscriber dissatisfaction and improving retention in the wireless telecommunications industry. IEEE Trans. Neural Networks (2000) 11(3):690–696Crossref, Google Scholar
- . Planning media schedules in the presence of dynamic advertising quality. Marketing Sci. (1998) 17(3):214–235Link, Google Scholar
- . The role of self-selection and usage uncertainty in the demand for local telephone service. Quant. Marketing Econom. (2007) 5(1):1–34Crossref, Google Scholar
- . A hidden Markov model of customer relationship dynamics. Marketing Sci. (2008) 27(2):185–204Link, Google Scholar
- . Data Mining Cookbook: Modeling Data for Marketing, Risk, and Customer Relationship Management (2001) (John Wiley & Sons, New York) Google Scholar
- . The impact of customer relationship characteristics on profitable lifetime duration. J. Marketing (2003) 67(1):77–99Crossref, Google Scholar
- . Staying power of churn prediction models. J. Interactive Marketing (2010) 24(3):198–208Crossref, Google Scholar
- . Customer satisfaction, customer retention, and market share. J. Retailing (1993) 69(2):193–215Crossref, Google Scholar
- . Driving Customer Equity: How Customer Lifetime Value Is Reshaping Corporate Strategy (2001) (Free Press, New York) Google Scholar
- . A nonstationary model of binary choice applied to media exposure. Management Sci. (1981) 27(6):637–657Link, Google Scholar
- . Modeling the evolution of customers' service portfolios. (2008a) . Working paper, Emory University, Atlanta. http://ssrn.com/abstract=985639Crossref, Google Scholar
- . Understanding service retention within and across cohorts using limited information. J. Marketing (2008b) 72(1):82–94Crossref, Google Scholar
- . Portfolio dynamics for customers of a multi-service provider. Management Sci. (2011) 57(3):471–486Link, Google Scholar
- . Hidden Markov models for longitudinal comparisons. J. Amer. Statist. Assoc. (2005) 100(470):359–369Crossref, Google Scholar
- . The effects of service quality on usage and termination of a video on demand service. (2012) . Working paper, University of Michigan, Ann ArborGoogle Scholar
- . A customer lifetime value framework for customer selection and resource allocation strategy. J. Marketing (2004) 68(4):106–125Crossref, Google Scholar
- . Understanding the effect of customer relationship management efforts on customer retention and customer share development. J. Marketing (2003) 67(4):30–45Crossref, Google Scholar
- . Econometric Analysis of Cross Section and Panel Data (2002) (MIT Press, Cambridge, MA) Google Scholar
- . Instant customer base analysis: Managerial heuristics often get it right. J. Marketing (2008) 72(3):82–93Crossref, Google Scholar
- . Kalman filter estimation of new product diffusion models. J. Marketing Res. (1997) 34(3):378–393Crossref, Google Scholar
- . Joint analysis of longitudinal data comprising repeated measures and times to events. Appl. Statist. (2001) 50(3):375–387Google Scholar

