IDEAS home Printed from https://ideas.repec.org/r/inm/ormksc/v32y2013i4p570-590.html
   My bibliography  Save this item

A Joint Model of Usage and Churn in Contractual Settings

Citations

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


Cited by:

  1. Park, Chang Hee & Yoon, Tae Jung, 2022. "The dark side of up-selling promotions: Evidence from an analysis of cross-brand purchase behavior☆," Journal of Retailing, Elsevier, vol. 98(4), pages 647-666.
  2. Philipp Afèche & Mojtaba Araghi & Opher Baron, 2017. "Customer Acquisition, Retention, and Service Access Quality: Optimal Advertising, Capacity Level, and Capacity Allocation," Manufacturing & Service Operations Management, INFORMS, vol. 19(4), pages 674-691, October.
  3. Jin, Haofeng, 2022. "The effect of overspending on tariff choices and customer churn: Evidence from mobile plan choices," Journal of Retailing and Consumer Services, Elsevier, vol. 66(C).
  4. 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.
  5. Angelovska, Nina, 2021. "Analysis Of Customer Activity, The Importance Of Timing For Effective Marketing Actions: Case Of Group Buying Site, Grouper," UTMS Journal of Economics, University of Tourism and Management, Skopje, Macedonia, vol. 12(2), pages 156-170.
  6. Carlos Fernández-Loría & Maxime C. Cohen & Anindya Ghose, 2023. "Evolution of Referrals over Customers’ Life Cycle: Evidence from a Ride-Sharing Platform," Information Systems Research, INFORMS, vol. 34(2), pages 698-720, June.
  7. Arun Gopalakrishnan & Zhenling Jiang & Yulia Nevskaya & Raphael Thomadsen, 2021. "Can Non-tiered Customer Loyalty Programs Be Profitable?," Marketing Science, INFORMS, vol. 40(3), pages 508-526, May.
  8. Zhuojun Gu & Ravi Bapna & Jason Chan & Alok Gupta, 2022. "Measuring the Impact of Crowdsourcing Features on Mobile App User Engagement and Retention: A Randomized Field Experiment," Management Science, INFORMS, vol. 68(2), pages 1297-1329, February.
  9. Jinping Hu, 2023. "Customer feature selection from high-dimensional bank direct marketing data for uplift modeling," Journal of Marketing Analytics, Palgrave Macmillan, vol. 11(2), pages 160-171, June.
  10. Meire, Matthijs, 2021. "Customer comeback: Empirical insights into the drivers and value of returning customers," Journal of Business Research, Elsevier, vol. 127(C), pages 193-205.
  11. Kappe, Eelco & Stadler Blank, Ashley & DeSarbo, Wayne S., 2018. "A random coefficients mixture hidden Markov model for marketing research," International Journal of Research in Marketing, Elsevier, vol. 35(3), pages 415-431.
  12. Jonathan Z. Zhang & Chun-Wei Chang, 2021. "Consumer dynamics: theories, methods, and emerging directions," Journal of the Academy of Marketing Science, Springer, vol. 49(1), pages 166-196, January.
  13. Michael R. Ward, 2022. "Network engagement from learning friends’ preferences: evidence from a video gaming social network," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(3), pages 1239-1255, September.
  14. Aurélie Lemmens & Sunil Gupta, 2020. "Managing Churn to Maximize Profits," Marketing Science, INFORMS, vol. 39(5), pages 956-973, September.
  15. Yingjie Zhang & Beibei Li & Xueming Luo & Xiaoyi Wang, 2019. "Personalized Mobile Targeting with User Engagement Stages: Combining a Structural Hidden Markov Model and Field Experiment," Information Systems Research, INFORMS, vol. 30(3), pages 787-804, September.
  16. Haupt, Johannes & Lessmann, Stefan, 2022. "Targeting customers under response-dependent costs," European Journal of Operational Research, Elsevier, vol. 297(1), pages 369-379.
  17. Koll, Oliver & Plank, Andreas, 2022. "Do shoppers choose the same brand on the next trip when facing the same context? An empirical investigation in FMCG retailing," Journal of Retailing, Elsevier, vol. 98(4), pages 576-592.
  18. Peter Ebbes & Oded Netzer, 2022. "Using Social Network Activity Data to Identify and Target Job Seekers," Management Science, INFORMS, vol. 68(4), pages 3026-3046, April.
  19. Ibrahim Al-Shourbaji & Na Helian & Yi Sun & Samah Alshathri & Mohamed Abd Elaziz, 2022. "Boosting Ant Colony Optimization with Reptile Search Algorithm for Churn Prediction," Mathematics, MDPI, vol. 10(7), pages 1-21, March.
  20. Haupt, Johannes & Lessmann, Stefan, 2020. "Targeting Cutsomers Under Response-Dependent Costs," IRTG 1792 Discussion Papers 2020-005, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
  21. Holtrop, Niels & Wieringa, Jaap E. & Gijsenberg, Maarten J. & Verhoef, Peter C., 2017. "No future without the past? Predicting churn in the face of customer privacy," International Journal of Research in Marketing, Elsevier, vol. 34(1), pages 154-172.
  22. Gui Liberali & Alina Ferecatu, 2022. "Morphing for Consumer Dynamics: Bandits Meet Hidden Markov Models," Marketing Science, INFORMS, vol. 41(4), pages 769-794, July.
  23. 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.
  24. Lu, Shijie & Xie, Ying & Chen, Xingyu, 2023. "Immediate and enduring effects of digital badges on online content consumption and generation," International Journal of Research in Marketing, Elsevier, vol. 40(1), pages 146-163.
  25. Alina Ferecatu & Arnaud De Bruyn, 2022. "Understanding Managers’ Trade-Offs Between Exploration and Exploitation," Marketing Science, INFORMS, vol. 41(1), pages 139-165, January.
  26. Mora Cortez, Roberto & Johnston, Wesley J. & Gopalakrishna, Srinath, 2022. "Driving participation and investment in B2B trade shows: The organizer view," Journal of Business Research, Elsevier, vol. 142(C), pages 1092-1105.
  27. Micus, Christian & Schramm, Simon & Boehm, Markus & Krcmar, Helmut, 2023. "Methods to analyze customer usage data in a product decision process:A systematic literature review," Operations Research Perspectives, Elsevier, vol. 10(C).
  28. Kocaman, Barış & Gelper, Sarah & Langerak, Fred, 2023. "Till the cloud do us part: Technological disruption and brand retention in the enterprise software industry," International Journal of Research in Marketing, Elsevier, vol. 40(2), pages 316-341.
  29. Johannes Haupt & Stefan Lessmann, 2020. "Targeting customers under response-dependent costs," Papers 2003.06271, arXiv.org, revised Aug 2021.
  30. Antonello Maruotti & Jan Bulla & Tanya Mark, 2019. "Assessing the influence of marketing activities on customer behaviors: a dynamic clustering approach," METRON, Springer;Sapienza Università di Roma, vol. 77(1), pages 19-42, April.
  31. Holtrop, Niels & Wieringa, Jaap E., 2023. "Timing customer reactivation initiatives," International Journal of Research in Marketing, Elsevier, vol. 40(3), pages 570-589.
  32. Li, Yixin & Hou, Bingzhang & Wu, Yue & Zhao, Donglai & Xie, Aoran & Zou, Peng, 2021. "Giant fight: Customer churn prediction in traditional broadcast industry," Journal of Business Research, Elsevier, vol. 131(C), pages 630-639.
  33. Hongshuang (Alice) Li, 2022. "Converting free users to paid subscribers in the SaaS context: The impact of marketing touchpoints, message content, and usage," Production and Operations Management, Production and Operations Management Society, vol. 31(5), pages 2185-2203, May.
  34. 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.
  35. Clarence Lee & Elie Ofek & Thomas J. Steenburgh, 2018. "Personal and Social Usage: The Origins of Active Customers and Ways to Keep Them Engaged," Management Science, INFORMS, vol. 64(6), pages 2473-2495, June.
  36. Christof Naumzik & Stefan Feuerriegel & Markus Weinmann, 2022. "I Will Survive: Predicting Business Failures from Customer Ratings," Marketing Science, INFORMS, vol. 41(1), pages 188-207, January.
  37. Lily (Xuehui) Gao & Evert Haan & Iguácel Melero-Polo & F. Javier Sese, 2023. "Winning your customers’ minds and hearts: Disentangling the effects of lock-in and affective customer experience on retention," Journal of the Academy of Marketing Science, Springer, vol. 51(2), pages 334-371, March.
  38. Mark, Tanya & Bulla, Jan & Niraj, Rakesh & Bulla, Ingo & Schwarzwäller, Wolfgang, 2019. "Catalogue as a tool for reinforcing habits: Empirical evidence from a multichannel retailer," International Journal of Research in Marketing, Elsevier, vol. 36(4), pages 528-541.
  39. Benoit, Dries F. & Tsang, Wai Kit & Coussement, Kristof & Raes, Annelies, 2024. "High-stake student drop-out prediction using hidden Markov models in fully asynchronous subscription-based MOOCs," Technological Forecasting and Social Change, Elsevier, vol. 198(C).
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.