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Quantitative models for direct marketing: A review from systems perspective

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  1. Schröder, Nadine & Hruschka, Harald, 2016. "Investigating the effects of mailing variables and endogeneity on mailing decisions," European Journal of Operational Research, Elsevier, vol. 250(2), pages 579-589.
  2. 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.
  3. Geuens, Stijn & Coussement, Kristof & De Bock, Koen W., 2018. "A framework for configuring collaborative filtering-based recommendations derived from purchase data," European Journal of Operational Research, Elsevier, vol. 265(1), pages 208-218.
  4. Chun, Young H., 2012. "Monte Carlo analysis of estimation methods for the prediction of customer response patterns in direct marketing," European Journal of Operational Research, Elsevier, vol. 217(3), pages 673-678.
  5. 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.
  6. Mariia I. Okuneva & Dmitriy B. Potapov, 2015. "The Effectiveness of Individual Targeting Through Smartphone Application in Retail: Evidence from Field Experiment," HSE Working papers WP BRP 47/MAN/2015, National Research University Higher School of Economics.
  7. Somayeh Moazeni & Boris Defourny & Monika J. Wilczak, 2020. "Sequential Learning in Designing Marketing Campaigns for Market Entry," Management Science, INFORMS, vol. 66(9), pages 4226-4245, September.
  8. Shokouhyar, Sajjad & Shokoohyar, Sina & Safari, Sepehr, 2020. "Research on the influence of after-sales service quality factors on customer satisfaction," Journal of Retailing and Consumer Services, Elsevier, vol. 56(C).
  9. Mercedes Esteban-Bravo & Jose M. Vidal-Sanz & Gökhan Yildirim, 2014. "Valuing Customer Portfolios with Endogenous Mass and Direct Marketing Interventions Using a Stochastic Dynamic Programming Decomposition," Marketing Science, INFORMS, vol. 33(5), pages 621-640, September.
  10. Gattermann-Itschert, Theresa & Thonemann, Ulrich W., 2021. "How training on multiple time slices improves performance in churn prediction," European Journal of Operational Research, Elsevier, vol. 295(2), pages 664-674.
  11. Tang, Leilei & Thomas, Lyn & Fletcher, Mary & Pan, Jiazhu & Marshall, Andrew, 2014. "Assessing the impact of derived behavior information on customer attrition in the financial service industry," European Journal of Operational Research, Elsevier, vol. 236(2), pages 624-633.
  12. Noureddine Boustani & Ali Emrouznejad & Roya Gholami & Ozren Despic & Athina Ioannou, 2024. "Improving the predictive accuracy of the cross-selling of consumer loans using deep learning networks," Annals of Operations Research, Springer, vol. 339(1), pages 613-630, August.
  13. 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.
  14. 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.
  15. Steven Debaere & Floris Devriendt & Johanna Brunneder & Wouter Verbeke & Tom de Ruyck & Kristof Coussement, 2019. "Reducing inferior member community participation using uplift modeling: Evidence from a field experiment," Post-Print hal-02990787, HAL.
  16. 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.
  17. Ding‐Wen Tan & William Yeoh & Yee Ling Boo & Soung‐Yue Liew, 2013. "The Impact Of Feature Selection: A Data‐Mining Application In Direct Marketing," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 20(1), pages 23-38, January.
  18. Soopramanien, Didier & Hong Juan, Liu, 2010. "The importance of understanding the exchange context when developing a decision support tool to target prospective customers of business insurance," Journal of Retailing and Consumer Services, Elsevier, vol. 17(4), pages 306-312.
  19. Meyer, Anne & Glock, Katharina & Radaschewski, Frank, 2021. "Planning profitable tours for field sales forces: A unified view on sales analytics and mathematical optimization," Omega, Elsevier, vol. 105(C).
  20. 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.
  21. Todor Krastevich, 2013. "Using Predictive Modeling to Improve Direct Marketing Performance," Economic Studies journal, Bulgarian Academy of Sciences - Economic Research Institute, issue 3, pages 25-55.
  22. Kamaal Allil, 2024. "Integrating AI-driven marketing analytics techniques into the classroom: pedagogical strategies for enhancing student engagement and future business success," Journal of Marketing Analytics, Palgrave Macmillan, vol. 12(2), pages 142-168, June.
  23. Hossein Hassani & Xu Huang & Emmanuel Silva & Mansi Ghodsi, 2020. "Deep Learning and Implementations in Banking," Annals of Data Science, Springer, vol. 7(3), pages 433-446, September.
  24. Coussement, Kristof & De Bock, Koen W., 2013. "Customer churn prediction in the online gambling industry: The beneficial effect of ensemble learning," Journal of Business Research, Elsevier, vol. 66(9), pages 1629-1636.
  25. Talla Nobibon, Fabrice & Leus, Roel & Spieksma, Frits C.R., 2011. "Optimization models for targeted offers in direct marketing: Exact and heuristic algorithms," European Journal of Operational Research, Elsevier, vol. 210(3), pages 670-683, May.
  26. J. D’Haen & D. Van Den Poel, 2013. "Model-supported business-to-business prospect prediction based on an iterative customer acquisition framework," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 13/863, Ghent University, Faculty of Economics and Business Administration.
  27. Chongwatpol, Jongsawas, 2015. "Integration of RFID and business analytics for trade show exhibitors," European Journal of Operational Research, Elsevier, vol. 244(2), pages 662-673.
  28. Katerina Shapoval & Thomas Setzer, 2018. "Next-Purchase Prediction Using Projections of Discounted Purchasing Sequences," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 60(2), pages 151-166, April.
  29. Niknamian, Sorush, 2019. "The Use of Customer value changing trends in business analysis," OSF Preprints mk38c, Center for Open Science.
  30. Cui, Geng & Wong, Man Leung & Wan, Xiang, 2015. "Targeting High Value Customers While Under Resource Constraint: Partial Order Constrained Optimization with Genetic Algorithm," Journal of Interactive Marketing, Elsevier, vol. 29(C), pages 27-37.
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