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Bayesian neural network learning for repeat purchase modelling in direct marketing

Citations

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Cited by:

  1. Bilal Zorić, Alisa, 2015. "Case Study in Banking Using Neural Networks," Proceedings of the ENTRENOVA - ENTerprise REsearch InNOVAtion Conference (2015), Kotor, Montengero, in: Proceedings of the ENTRENOVA - ENTerprise REsearch InNOVAtion Conference, Kotor, Montengero, 10-11 September 2015, pages 251-257, IRENET - Society for Advancing Innovation and Research in Economy, Zagreb.
  2. M. Ballings & D. Van Den Poel & E. Verhagen, 2013. "Evaluating the Added Value of Pictorial Data for Customer Churn Prediction," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 13/869, Ghent University, Faculty of Economics and Business Administration.
  3. 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.
  4. Ma, Tiejun & Tang, Leilei & McGroarty, Frank & Sung, Ming-Chien & Johnson, Johnnie E. V, 2016. "Time is money: Costing the impact of duration misperception in market prices," European Journal of Operational Research, Elsevier, vol. 255(2), pages 397-410.
  5. 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.
  6. B Baesens & T Van Gestel & M Stepanova & D Van den Poel & J Vanthienen, 2005. "Neural network survival analysis for personal loan data," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 56(9), pages 1089-1098, September.
  7. Fan, Zhi-Ping & Sun, Minghe, 2015. "Behavior-aware user response modeling in social media: Learning from diverse heterogeneous dataAuthor-Name: Chen, Zhen-Yu," European Journal of Operational Research, Elsevier, vol. 241(2), pages 422-434.
  8. D. F. Benoit & D. Van Den Poel, 2012. "Improving Customer Retention In Financial Services Using Kinship Network Information," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 12/786, Ghent University, Faculty of Economics and Business Administration.
  9. Van den Poel, Dirk & Buckinx, Wouter, 2005. "Predicting online-purchasing behaviour," European Journal of Operational Research, Elsevier, vol. 166(2), pages 557-575, October.
  10. Van den Poel, Dirk & Lariviere, Bart, 2004. "Customer attrition analysis for financial services using proportional hazard models," European Journal of Operational Research, Elsevier, vol. 157(1), pages 196-217, August.
  11. Viaene, Stijn & Dedene, Guido, 2005. "Cost-sensitive learning and decision making revisited," European Journal of Operational Research, Elsevier, vol. 166(1), pages 212-220, October.
  12. Mihai TICHINDELEAN, 2013. "Models Used for Measuring Customer Engagement," Expert Journal of Marketing, Sprint Investify, vol. 1(1), pages 38-49.
  13. 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.
  14. Bose, Indranil & Chen, Xi, 2009. "Quantitative models for direct marketing: A review from systems perspective," European Journal of Operational Research, Elsevier, vol. 195(1), pages 1-16, May.
  15. Geng Cui & Man Leung Wong & Hon-Kwong Lui, 2006. "Machine Learning for Direct Marketing Response Models: Bayesian Networks with Evolutionary Programming," Management Science, INFORMS, vol. 52(4), pages 597-612, April.
  16. Baesens, Bart & Verstraeten, Geert & Van den Poel, Dirk & Egmont-Petersen, Michael & Van Kenhove, Patrick & Vanthienen, Jan, 2004. "Bayesian network classifiers for identifying the slope of the customer lifecycle of long-life customers," European Journal of Operational Research, Elsevier, vol. 156(2), pages 508-523, July.
  17. J. Burez & D. Van Den Poel, 2008. "Handling class imbalance in customer churn prediction," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 08/517, Ghent University, Faculty of Economics and Business Administration.
  18. Baumgartner, Bernhard & Hruschka, Harald, 2005. "Allocation of catalogs to collective customers based on semiparametric response models," European Journal of Operational Research, Elsevier, vol. 162(3), pages 839-849, May.
  19. David Olson & Qing Cao & Ching Gu & Donhee Lee, 2009. "Comparison of customer response models," Service Business, Springer;Pan-Pacific Business Association, vol. 3(2), pages 117-130, June.
  20. Hruschka, Harald, 2006. "Relevance of functional flexibility for heterogeneous sales response models: A comparison of parametric and semi-nonparametric models," European Journal of Operational Research, Elsevier, vol. 174(2), pages 1009-1020, October.
  21. Gubela, Robin M. & Lessmann, Stefan & Jaroszewicz, Szymon, 2020. "Response transformation and profit decomposition for revenue uplift modeling," European Journal of Operational Research, Elsevier, vol. 283(2), pages 647-661.
  22. K. Coussement & D. Van Den Poel, 2008. "Improving Customer Attrition Prediction by Integrating Emotions from Client/Company Interaction Emails and Evaluating Multiple Classifiers," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 08/527, Ghent University, Faculty of Economics and Business Administration.
  23. B Baesens & T Van Gestel & S Viaene & M Stepanova & J Suykens & J Vanthienen, 2003. "Benchmarking state-of-the-art classification algorithms for credit scoring," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 54(6), pages 627-635, June.
  24. Opher Etzion & Amit Fisher & Segev Wasserkrug, 2005. "e-CLV: A Modeling Approach for Customer Lifetime Evaluation in e-Commerce Domains, with an Application and Case Study for Online Auction," Information Systems Frontiers, Springer, vol. 7(4), pages 421-434, December.
  25. Stefan Lessmann & Stefan Voß, 2010. "Customer-Centric Decision Support," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 2(2), pages 79-93, April.
  26. Gitae Kim & Bongsug Chae & David Olson, 2013. "A support vector machine (SVM) approach to imbalanced datasets of customer responses: comparison with other customer response models," Service Business, Springer;Pan-Pacific Business Association, vol. 7(1), pages 167-182, March.
  27. Coussement, Kristof & Buckinx, Wouter, 2011. "A probability-mapping algorithm for calibrating the posterior probabilities: A direct marketing application," European Journal of Operational Research, Elsevier, vol. 214(3), pages 732-738, November.
  28. Nadarajah, Saralees & Kotz, Samuel, 2009. "Models for purchase frequency," European Journal of Operational Research, Elsevier, vol. 192(3), pages 1014-1026, February.
  29. G Verstraeten & D Van den Poel, 2005. "The impact of sample bias on consumer credit scoring performance and profitability," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 56(8), pages 981-992, August.
  30. Aslan Lotfi & Zhengrui Jiang & Ali Lotfi & Dipak C. Jain, 2023. "Estimating Life Cycle Sales of Technology Products with Frequent Repeat Purchases: A Fractional Calculus-Based Approach," Information Systems Research, INFORMS, vol. 34(2), pages 409-422, June.
  31. B. Larivi Re & D. Van Den Poel, 2004. "Predicting Customer Retention and Profitability by Using Random Forests and Regression Forests Techniques," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 04/282, Ghent University, Faculty of Economics and Business Administration.
  32. Buckinx, Wouter & Van den Poel, Dirk, 2005. "Customer base analysis: partial defection of behaviourally loyal clients in a non-contractual FMCG retail setting," European Journal of Operational Research, Elsevier, vol. 164(1), pages 252-268, July.
  33. Crone, Sven F. & Lessmann, Stefan & Stahlbock, Robert, 2006. "The impact of preprocessing on data mining: An evaluation of classifier sensitivity in direct marketing," European Journal of Operational Research, Elsevier, vol. 173(3), pages 781-800, September.
  34. Lessmann, Stefan & Voß, Stefan, 2009. "A reference model for customer-centric data mining with support vector machines," European Journal of Operational Research, Elsevier, vol. 199(2), pages 520-530, December.
  35. M. Ballings & D. Van Den Poel, 2012. "The Relevant Length of Customer Event History for Churn Prediction: How long is long enough?," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 12/804, Ghent University, Faculty of Economics and Business Administration.
  36. Seret, Alex & Verbraken, Thomas & Versailles, Sébastien & Baesens, Bart, 2012. "A new SOM-based method for profile generation: Theory and an application in direct marketing," European Journal of Operational Research, Elsevier, vol. 220(1), pages 199-209.
  37. Bert de Reyck & Zeger Degraeve, 2003. "Broadcast Scheduling for Mobile Advertising," Operations Research, INFORMS, vol. 51(4), pages 509-517, August.
  38. Alisa Bilal Zoric, 2016. "Predicting customer churn in banking industry using neural networks," Interdisciplinary Description of Complex Systems - scientific journal, Croatian Interdisciplinary Society Provider Homepage: http://indecs.eu, vol. 14(2), pages 116-124.
  39. Vera L. Miguéis & Ana S. Camanho & José Borges, 2017. "Predicting direct marketing response in banking: comparison of class imbalance methods," Service Business, Springer;Pan-Pacific Business Association, vol. 11(4), pages 831-849, December.
  40. Luis Castro-Martín & Maria del Mar Rueda & Ramón Ferri-García, 2020. "Inference from Non-Probability Surveys with Statistical Matching and Propensity Score Adjustment Using Modern Prediction Techniques," Mathematics, MDPI, vol. 8(6), pages 1-19, June.
  41. Benjamin Lev, 2007. "Book Reviews," Interfaces, INFORMS, vol. 37(3), pages 300-304, June.
  42. Rocío G. Martínez & Ramon A. Carrasco & Cristina Sanchez-Figueroa & Diana Gavilan, 2021. "An RFM Model Customizable to Product Catalogues and Marketing Criteria Using Fuzzy Linguistic Models: Case Study of a Retail Business," Mathematics, MDPI, vol. 9(16), pages 1-31, August.
  43. W. Buckinx & E. Moons & D. Van Den Poel & G. Wets, 2003. "Customer-Adapted Coupon Targeting Using Feature Selection," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 03/201, Ghent University, Faculty of Economics and Business Administration.
  44. Kamila Migdał-Najman & Krzysztof Najman & Sylwia Badowska, 2020. "The GNG neural network in analyzing consumer behaviour patterns: empirical research on a purchasing behaviour processes realized by the elderly consumers," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 14(4), pages 947-982, December.
  45. Mehdi Neshat & Ali Akbar Pourahmad & Mohammad Reza Hasani, 2016. "Designing an Adaptive Neuro Fuzzy Inference System for Prediction of Customers Satisfaction," Journal of Information & Knowledge Management (JIKM), World Scientific Publishing Co. Pte. Ltd., vol. 15(04), pages 1-21, December.
  46. Brandner, Hubertus & Lessmann, Stefan & Voß, Stefan, 2013. "A memetic approach to construct transductive discrete support vector machines," European Journal of Operational Research, Elsevier, vol. 230(3), pages 581-595.
  47. Jonker, J.-J. & Piersma, N. & Van den Poel, D., 2002. "Joint optimization of customer segmentation and marketing policy to maximize long-term profitability," Econometric Institute Research Papers EI 2002-18, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
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