Incorporating Direct Marketing Activity into Latent Attrition Models

Published Online:https://doi.org/10.1287/mksc.2013.0781

References

  • Abe M. “Counting your customers” one by one: A hierarchical Bayes extension to the Pareto/NBD model. Marketing Sci. (2009) 28(3):541–553LinkGoogle Scholar
  • Anderson ET, Simester DI. Long-run effects of promotion depth on new versus established customers: Three field studies. Marketing Sci. (2004) 23(1):4–20LinkGoogle Scholar
  • Blattberg RC, Kim B-D, Neslin SA. Database Marketing: Analyzing and Managing Customers (2008) (Springer, New York) International Series in Quantitative MarketingCrossrefGoogle Scholar
  • Borle S, Singh SS, Jain DC. Customer lifetime value measurement. Management Sci. (2008) 54(1):100–112LinkGoogle Scholar
  • Braun M, Schweidel DA. Modeling customer lifetimes with multiple causes of churn. Marketing Sci. (2011) 30(5):881–902LinkGoogle Scholar
  • Danaher PJ, Smith MS. Modeling multivariate distributions using copulas: Applications in marketing. Marketing Sci. (2011) 30(1):4–21LinkGoogle Scholar
  • Danaher PJ, Hardie BGS, Putsis WP. Marketing-mix variables and the diffusion of successive generations of a technological innovation. J. Marketing Res. (2001) 38(4):501–514CrossrefGoogle Scholar
  • Donkers B, Paap R, Jonker J-J, Franses PH. Deriving target selection rules from endogenously selected samples. J. Appl. Econometrics (2006) 21(5):549–562CrossrefGoogle Scholar
  • Fader PS, Hardie BGS, Lee KL. “Counting your customers” the easy way: An alternative to the Pareto/NBD model. Marketing Sci. (2005a) 24(2):275–284LinkGoogle Scholar
  • Fader PS, Hardie BGS, Lee KL. RFM and CLV: Using iso-value curves for customer base analysis. J. Marketing Res. (2005b) 42(4):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
  • Franses PH, Paap R. Quantitative Models in Marketing Research (2001) (Cambridge University Press, Cambridge, UK) CrossrefGoogle Scholar
  • Fournier S, Dobscha S, Mick DG. Preventing the premature death of relationship marketing. Harvard Bus. Rev. (1997) 75(1):42–49Google Scholar
  • Gönül F, Shi MZ. Optimal mailings of catalogs: A new methodology using estimable structural dynamic programming models. Management Sci. (1998) 44(9):1249–1262LinkGoogle Scholar
  • Gupta S, Park S. Handling endogenous regressors by joint estimation using copulas. Marketing Sci. (2012) 31(4):567–586LinkGoogle Scholar
  • Jen L, Chou C-H, Allenby GM. The importance of modeling temporal dependence of timing and quantity in direct marketing. J. Marketing Res. (2009) 46(4):482–493CrossrefGoogle Scholar
  • Kumar V, Reinartz WJ. Customer Relationship Management: A Database Approach (2006) (John Wiley & Sons, New York) Google Scholar
  • Li S, Sun B, Montgomery A. Cross-selling the right product to the right customer at the right time. J. Marketing Res. (2011) 48(4):683–700CrossrefGoogle Scholar
  • MacDonald IL, Zucchini W. Hidden Markov and Other Models for Discrete-Valued Time Series (1997) (Chapman & Hall, London) Google Scholar
  • Malthouse EC. Ridge regression and direct marketing scoring models. J. Interactive Marketing (1999) 13(4):10–23CrossrefGoogle Scholar
  • Manchanda P, Rossi PE, Chintagunta PK. Response modeling with nonrandom marketing-mix variables. J. Marketing Res. (2004) 41(4):467–478CrossrefGoogle Scholar
  • Moe WW, Fader PS. Dynamic conversion behavior at e-commerce sites. Management Sci. (2004) 50(3):326–335LinkGoogle Scholar
  • Moe WW, Schweidel DA. Online product opinions: Incidence, evaluation, and evolution. Marketing Sci. (2012) 31(3):372–386LinkGoogle Scholar
  • Moe WW, Trusov M. Measuring the value of social dynamics in online product forums. J. Marketing Res. (2011) 48(3):444–456CrossrefGoogle Scholar
  • Montoya R, Netzer O, Jedidi K. Dynamic marketing resource allocation for long-term profitability: A pharmaceutical application. Marketing Sci. (2010) 29(5):909–924LinkGoogle Scholar
  • Neslin SA, Henderson C, Quelch J. Consumer promotions and the acceleration of product purchases. Marketing Sci. (1985) 4(2):147–165LinkGoogle Scholar
  • Neslin SA, Rhoads EE, Wolfson P. A model and empirical analysis of patient compliance and persistence in pharmaceuticals. (2009) . Working paper, Dartmouth College, Hanover, NHGoogle Scholar
  • Netzer O, Lattin JM, Srinivasan V. A hidden Markov model of customer relationship dynamics. Marketing Sci. (2008) 27(2):185–204LinkGoogle Scholar
  • Reinartz WJ, Kumar V. On the profitability of long-life customers in a noncontractual setting: An empirical investigation and implications for marketing. J. Marketing (2000) 64(4):17–35CrossrefGoogle Scholar
  • Reinartz WJ, Kumar V. The impact of customer relationship characteristics on profitable lifetime duration. J. Marketing (2003) 67(1):77–99CrossrefGoogle Scholar
  • Reinartz WJ, Thomas JS, Kumar V. Balancing acquisition and retention resources to maximize customer profitability. J. Marketing (2005) 69(1):63–79CrossrefGoogle Scholar
  • Schmittlein DC, Peterson RA. Customer base analysis: An industrial purchase process application. Marketing Sci. (1994) 13(1):41–67LinkGoogle Scholar
  • Schmittlein DC, Morrison DG, Colombo R. Counting your customers: Who they are and what will they do next? Management Sci. (1987) 33(1):1–24LinkGoogle Scholar
  • Schweidel DA, Fader PS. Revisiting dynamic changepoints: An evolving process model of new product sales. Internat. J. Res. Marketing (2009) 26(2):119–124CrossrefGoogle Scholar
  • Schweidel DA, Fader PS, Bradlow ET. A bivariate timing model of customer acquisition and retention. Marketing Sci. (2008a) 27(5):829–843LinkGoogle Scholar
  • Schweidel DA, Fader PS, Bradlow ET. Understanding subscriber retention behavior across and within cohorts using limited information. J. Marketing (2008b) 72(1):82–94CrossrefGoogle Scholar
  • Schweidel DA, Bradlow ET, Fader PS. Portfolio dynamics for customers of a multiservice provider. Management Sci. (2011) 57(3):471–486LinkGoogle Scholar
  • Singh S, Borle S, Jain D. A generalized framework for estimating customer lifetime value when customer lifetimes are not observed. Quant. Marketing Econom. (2009) 7(2):181–205CrossrefGoogle Scholar
  • Sun B, Li S. Learning and acting on customer information: A simulation-based demonstration on service allocations with offshore centers. J. Marketing Res. (2011) 48(1):72–86CrossrefGoogle Scholar
  • Van Diepen M, Donkers B, Franses PH. Dynamic and competitive effects of direct mailings: A charitable giving application. J. Marketing Res. (2009) 46(1):120–133CrossrefGoogle 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
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.