IDEAS home Printed from https://ideas.repec.org/r/inm/ormksc/v29y2010i6p1086-1108.html
   My bibliography  Save this item

Customer-Base Analysis in a Discrete-Time Noncontractual Setting

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
as


Cited by:

  1. Park, Chang Hee & Park, Young-Hoon & Schweidel, David A., 2018. "The effects of mobile promotions on customer purchase dynamics," International Journal of Research in Marketing, Elsevier, vol. 35(3), pages 453-470.
  2. Aurélie Lemmens & Sunil Gupta, 2020. "Managing Churn to Maximize Profits," Marketing Science, INFORMS, vol. 39(5), pages 956-973, September.
  3. Harris, Shannon L. & May, Jerrold H. & Vargas, Luis G., 2016. "Predictive analytics model for healthcare planning and scheduling," European Journal of Operational Research, Elsevier, vol. 253(1), pages 121-131.
  4. Lamei, Pezhman & Mohammadi Darani, Milad & Wiggins, Jennifer & Mahar, Christopher, 2025. "Can mobile promotion automation decrease overall purchases? The role of promotion pattern predictability in customer habit formation," Journal of Retailing and Consumer Services, Elsevier, vol. 82(C).
  5. Zhang, Shoutong Thomas, 2016. "Firm valuation from customer equity: When does it work and when does it fail?," International Journal of Research in Marketing, Elsevier, vol. 33(4), pages 966-970.
  6. David A. Schweidel & Young-Hoon Park & Zainab Jamal, 2014. "A Multiactivity Latent Attrition Model for Customer Base Analysis," Marketing Science, INFORMS, vol. 33(2), pages 273-286, March.
  7. David A. Schweidel & George Knox, 2013. "Incorporating Direct Marketing Activity into Latent Attrition Models," Marketing Science, INFORMS, vol. 32(3), pages 471-487, May.
  8. Michael Platzer & Thomas Reutterer, 2016. "Ticking Away the Moments: Timing Regularity Helps to Better Predict Customer Activity," Marketing Science, INFORMS, vol. 35(5), pages 779-799, September.
  9. Johannes Habel & Sascha Alavi & Nicolas Heinitz, 2023. "A theory of predictive sales analytics adoption," AMS Review, Springer;Academy of Marketing Science, vol. 13(1), pages 34-54, June.
  10. Kim, Hwang & Rao, Vithala R., 2022. "The role of network embeddedness across multiple social networks: Evidence from mobile social network games," International Journal of Research in Marketing, Elsevier, vol. 39(3), pages 867-887.
  11. Chou, Ping & Chuang, Howard Hao-Chun & Chou, Yen-Chun & Liang, Ting-Peng, 2022. "Predictive analytics for customer repurchase: Interdisciplinary integration of buy till you die modeling and machine learning," European Journal of Operational Research, Elsevier, vol. 296(2), pages 635-651.
  12. Dongyun Nie & Michael Scriney & Xiaoning Liang & Mark Roantree, 2024. "From data acquisition to validation: a complete workflow for predicting individual customer lifetime value," Journal of Marketing Analytics, Palgrave Macmillan, vol. 12(2), pages 321-341, June.
  13. Eric M. Schwartz & Eric T. Bradlow & Peter S. Fader, 2014. "Model Selection Using Database Characteristics: Developing a Classification Tree for Longitudinal Incidence Data," Marketing Science, INFORMS, vol. 33(2), pages 188-205, March.
  14. Eva Ascarza & Bruce G. S. Hardie, 2013. "A Joint Model of Usage and Churn in Contractual Settings," Marketing Science, INFORMS, vol. 32(4), pages 570-590, July.
  15. Ascarza, & Neslin, & Netzer, & Lemmens, Aurélie & Anderson, Zachery & Fader, Peter S. & Gupta, S. & Hardie, B.G.S. & Libai, Barak & Neal, David & Provost, Foster, 2018. "In pursuit of enhanced customer retention management : Review, key issues, and future directions," Other publications TiSEM 28a90d28-6daf-42f1-bd8e-e, Tilburg University, School of Economics and Management.
  16. Arun Gopalakrishnan & Eric T. Bradlow & Peter S. Fader, 2017. "A Cross-Cohort Changepoint Model for Customer-Base Analysis," Marketing Science, INFORMS, vol. 36(2), pages 195-213, March.
  17. Romero, Jaime & van der Lans, Ralf & Wierenga, Berend, 2013. "A Partially Hidden Markov Model of Customer Dynamics for CLV Measurement," Journal of Interactive Marketing, Elsevier, vol. 27(3), pages 185-208.
  18. Patrick Bachmann & Markus Meierer & Jeffrey Näf, 2021. "The Role of Time-Varying Contextual Factors in Latent Attrition Models for Customer Base Analysis," Marketing Science, INFORMS, vol. 40(4), pages 783-809, July.
  19. Yao Zhang & Eric T. Bradlow & Dylan S. Small, 2015. "Predicting Customer Value Using Clumpiness: From RFM to RFMC," Marketing Science, INFORMS, vol. 34(2), pages 195-208, March.
  20. Bobby J. Calder & Edward C. Malthouse & Joe Omatoi, 2025. "Integrating the customer equity and brand equity approaches to the financial value of marketing," Journal of Marketing Analytics, Palgrave Macmillan, vol. 13(1), pages 4-16, March.
  21. Yanwen Wang & Chunhua Wu & Ting Zhu, 2019. "Mobile Hailing Technology and Taxi Driving Behaviors," Marketing Science, INFORMS, vol. 38(5), pages 734-755, September.
  22. Durango-Cohen, Elizabeth J., 2013. "Modeling contribution behavior in fundraising: Segmentation analysis for a public broadcasting station," European Journal of Operational Research, Elsevier, vol. 227(3), pages 538-551.
  23. Myoungjae Choi & Sun-Hi Yoo & Jongtaik Lee & Jeongsub Choi & Byunghoon Kim, 2022. "A modified gamma/Gompertz/NBD model for estimating technology lifetime," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(10), pages 5731-5751, October.
  24. Eva Ascarza & Oded Netzer & Bruce G. S. Hardie, 2018. "Some Customers Would Rather Leave Without Saying Goodbye," Marketing Science, INFORMS, vol. 37(1), pages 54-77, January.
  25. Ivan Lugovoi & Dimitrios A. Andritsos & Claire Senot, 2022. "Novelty and scope of process innovation: The role of related and unrelated manufacturing experience," Production and Operations Management, Production and Operations Management Society, vol. 31(10), pages 3877-3895, October.
  26. Ta-Wei Huang & Eva Ascarza, 2024. "Doing More with Less: Overcoming Ineffective Long-Term Targeting Using Short-Term Signals," Marketing Science, INFORMS, vol. 43(4), pages 863-884, July.
  27. Durango-Cohen, Elizabeth J. & Torres, Ramón L. & Durango-Cohen, Pablo L., 2013. "Donor Segmentation: When Summary Statistics Don't Tell the Whole Story," Journal of Interactive Marketing, Elsevier, vol. 27(3), pages 172-184.
  28. Glady, Nicolas & Lemmens, Aurélie & Croux, Christophe, 2015. "Unveiling the relationship between the transaction timing, spending and dropout behavior of customers," International Journal of Research in Marketing, Elsevier, vol. 32(1), pages 78-93.
  29. repec:tiu:tiutis:52e91e47-4a2d-4e7b-bb23-3926b842ae30 is not listed on IDEAS
  30. Mihai TICHINDELEAN, 2013. "Models Used for Measuring Customer Engagement," Expert Journal of Marketing, Sprint Investify, vol. 1(1), pages 38-49.
  31. Sinha, Shameek & Malik, Sumit & Mahajan, Vijay & ter Hofstede, Frenkel, 2025. "Retain, reactivate or acquire: Can nonprofits reliably use community profiles as an alternative to past donation data?," Journal of Business Research, Elsevier, vol. 186(C).
  32. Park, Chang Hee & Park, Young-Hoon & Schweidel, David A., 2014. "A multi-category customer base analysis," International Journal of Research in Marketing, Elsevier, vol. 31(3), pages 266-279.
  33. Kinshuk Jerath & Peter S. Fader & Bruce G. S. Hardie, 2011. "New Perspectives on Customer "Death" Using a Generalization of the Pareto/NBD Model," Marketing Science, INFORMS, vol. 30(5), pages 866-880, September.
  34. Albert C. Bemmaor & Nicolas Glady, 2012. "Modeling Purchasing Behavior with Sudden "Death": A Flexible Customer Lifetime Model," Management Science, INFORMS, vol. 58(5), pages 1012-1021, May.
  35. Ryan Dew & Asim Ansari, 2018. "Bayesian Nonparametric Customer Base Analysis with Model-Based Visualizations," Marketing Science, INFORMS, vol. 37(2), pages 216-235, March.
  36. Jerath, Kinshuk & Fader, Peter S. & Hardie, Bruce G.S., 2016. "Customer-base analysis using repeated cross-sectional summary (RCSS) data," European Journal of Operational Research, Elsevier, vol. 249(1), pages 340-350.
  37. Reutterer, Thomas & Platzer, Michael & Schröder, Nadine, 2021. "Leveraging purchase regularity for predicting customer behavior the easy way," International Journal of Research in Marketing, Elsevier, vol. 38(1), pages 194-215.
  38. Sam K. Hui, 2017. "Understanding repeat playing behavior in casual games using a Bayesian data augmentation approach," Quantitative Marketing and Economics (QME), Springer, vol. 15(1), pages 29-55, March.
  39. Michael Hahsler & Radoslaw Karpienko, 2017. "Visualizing association rules in hierarchical groups," Journal of Business Economics, Springer, vol. 87(3), pages 317-335, April.
  40. Eva Ascarza & Scott A. Neslin & Oded Netzer & Zachery Anderson & Peter S. Fader & Sunil Gupta & Bruce G. S. Hardie & Aurélie Lemmens & Barak Libai & David Neal & Foster Provost & Rom Schrift, 2018. "In Pursuit of Enhanced Customer Retention Management: Review, Key Issues, and Future Directions," Customer Needs and Solutions, Springer;Institute for Sustainable Innovation and Growth (iSIG), vol. 5(1), pages 65-81, March.
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.