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Machine Learning for Direct Marketing Response Models: Bayesian Networks with Evolutionary Programming

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Cited by:

  1. 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.
  2. Laura Toschi & Elisa Ughetto & Andrea Fronzetti Colladon, 2023. "The identity of social impact venture capitalists: exploring social linguistic positioning and linguistic distinctiveness through text mining," Small Business Economics, Springer, vol. 60(3), pages 1249-1280, March.
  3. Rehman, Obaid Ur & Zhou, Zihan & Wu, Kai & Li, Wen, 2024. "From courtrooms to corporations: The effect of bankruptcy court establishment on firm acquisitions," Finance Research Letters, Elsevier, vol. 61(C).
  4. Pelau Corina & Barbul Maria, 2021. "Consumers’ perception on the use of cognitive computing," Proceedings of the International Conference on Business Excellence, Sciendo, vol. 15(1), pages 639-649, December.
  5. Ghaddar, Bissan & Naoum-Sawaya, Joe, 2018. "High dimensional data classification and feature selection using support vector machines," European Journal of Operational Research, Elsevier, vol. 265(3), pages 993-1004.
  6. Ransome Epie Bawack & Samuel Fosso Wamba & Kevin Daniel André Carillo & Shahriar Akter, 2022. "Artificial intelligence in E-Commerce: a bibliometric study and literature review," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(1), pages 297-338, March.
  7. Garvey, Myles D. & Carnovale, Steven & Yeniyurt, Sengun, 2015. "An analytical framework for supply network risk propagation: A Bayesian network approach," European Journal of Operational Research, Elsevier, vol. 243(2), pages 618-627.
  8. 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.
  9. Pawel Rymarczyk & Piotr Golabek & Sylwia Skrzypek - Ahmed & Magdalena Rzemieniak, 2021. "Profiling and Segmenting Clients with the Use of Machine Learning Algorithms," European Research Studies Journal, European Research Studies Journal, vol. 0(Special 1), pages 513-522.
  10. David Dilts & James Moore, 2009. "Do Arbitrators Use Just Cause Standards in Deciding Discharge and Discipline Cases? A Test," Journal of Labor Research, Springer, vol. 30(3), pages 245-261, September.
  11. Shah, Denish & Murthi, B.P.S., 2021. "Marketing in a data-driven digital world: Implications for the role and scope of marketing," Journal of Business Research, Elsevier, vol. 125(C), pages 772-779.
  12. 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.
  13. Lessmann, Stefan & Coussement, Kristof & De Bock, Koen W. & Haupt, Johannes, 2018. "Targeting customers for profit: An ensemble learning framework to support marketing decision making," IRTG 1792 Discussion Papers 2018-012, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
  14. Tobias Cagala & Ulrich Glogowsky & Johannes Rincke & Anthony Strittmatter, 2021. "Optimal Targeting in Fundraising: A Machine-Learning Approach," Economics working papers 2021-08, Department of Economics, Johannes Kepler University Linz, Austria.
  15. Gang Chen & Shuaiyong Xiao & Chenghong Zhang & Huimin Zhao, 2023. "A Theory-Driven Deep Learning Method for Voice Chat–Based Customer Response Prediction," Information Systems Research, INFORMS, vol. 34(4), pages 1513-1532, December.
  16. Fredström, Ashkan & Parida, Vinit & Wincent, Joakim & Sjödin, David & Oghazi, Pejvak, 2022. "What is the Market Value of Artificial Intelligence and Machine Learning? The Role of Innovativeness and Collaboration for Performance," Technological Forecasting and Social Change, Elsevier, vol. 180(C).
  17. Santiago Carbo-Valverde & Pedro Cuadros-Solas & Francisco Rodríguez-Fernández, 2020. "A machine learning approach to the digitalization of bank customers: Evidence from random and causal forests," PLOS ONE, Public Library of Science, vol. 15(10), pages 1-39, October.
  18. William Rand & Roland T. Rust & Min Kim, 2018. "Complex systems: marketing’s new frontier," AMS Review, Springer;Academy of Marketing Science, vol. 8(3), pages 111-127, December.
  19. Hanyao Gao & Gang Kou & Haiming Liang & Hengjie Zhang & Xiangrui Chao & Cong-Cong Li & Yucheng Dong, 2024. "Machine learning in business and finance: a literature review and research opportunities," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 10(1), pages 1-35, December.
  20. J. Piet Hausberg & Kirsten Liere-Netheler & Sven Packmohr & Stefanie Pakura & Kristin Vogelsang, 2019. "Research streams on digital transformation from a holistic business perspective: a systematic literature review and citation network analysis," Journal of Business Economics, Springer, vol. 89(8), pages 931-963, December.
  21. Campbell, Colin & Sands, Sean & Ferraro, Carla & Tsao, Hsiu-Yuan (Jody) & Mavrommatis, Alexis, 2020. "From data to action: How marketers can leverage AI," Business Horizons, Elsevier, vol. 63(2), pages 227-243.
  22. Coussement, Kristof & Van den Bossche, Filip A.M. & De Bock, Koen W., 2014. "Data accuracy's impact on segmentation performance: Benchmarking RFM analysis, logistic regression, and decision trees," Journal of Business Research, Elsevier, vol. 67(1), pages 2751-2758.
  23. Madhukar Chhimwal & Saurabh Agrawal & Girish Kumar, 2021. "Measuring Circular Supply Chain Risk: A Bayesian Network Methodology," Sustainability, MDPI, vol. 13(15), pages 1-22, July.
  24. Hossein Etemadi & Ahmad Ahmadpour & Seyed Moshashaei, 2015. "Earnings Per Share Forecast Using Extracted Rules from Trained Neural Network by Genetic Algorithm," Computational Economics, Springer;Society for Computational Economics, vol. 46(1), pages 55-63, June.
  25. Park, Hyun Jung & Kim, Sang-Hoon, 2013. "A Bayesian network approach to examining key success factors of mobile games," Journal of Business Research, Elsevier, vol. 66(9), pages 1353-1359.
  26. V Kumar & Amalesh Sharma & Shaphali Gupta, 2017. "Accessing the influence of strategic marketing research on generating impact: moderating roles of models, journals, and estimation approaches," Journal of the Academy of Marketing Science, Springer, vol. 45(2), pages 164-185, March.
  27. Asim Zia & Katherine Lacasse & Nina H. Fefferman & Louis J. Gross & Brian Beckage, 2024. "Machine Learning a Probabilistic Structural Equation Model to Explain the Impact of Climate Risk Perceptions on Policy Support," Sustainability, MDPI, vol. 16(23), pages 1-25, November.
  28. Tobias Cagala & Ulrich Glogowsky & Johannes Rincke & Anthony Strittmatter, 2021. "Optimal Targeting in Fundraising: A Causal Machine-Learning Approach," Papers 2103.10251, arXiv.org, revised Sep 2021.
  29. 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.
  30. Tsan‐Ming Choi & Subodha Kumar & Xiaohang Yue & Hau‐Ling Chan, 2022. "Disruptive Technologies and Operations Management in the Industry 4.0 Era and Beyond," Production and Operations Management, Production and Operations Management Society, vol. 31(1), pages 9-31, January.
  31. Ngai, Eric W.T. & Wu, Yuanyuan, 2022. "Machine learning in marketing: A literature review, conceptual framework, and research agenda," Journal of Business Research, Elsevier, vol. 145(C), pages 35-48.
  32. Wei Li & Wolfgang Karl Hardle & Stefan Lessmann, 2022. "A Data-driven Case-based Reasoning in Bankruptcy Prediction," Papers 2211.00921, arXiv.org.
  33. Venkatesh Shankar & Sohil Parsana, 2022. "An overview and empirical comparison of natural language processing (NLP) models and an introduction to and empirical application of autoencoder models in marketing," Journal of the Academy of Marketing Science, Springer, vol. 50(6), pages 1324-1350, November.
  34. Mustak, Mekhail & Salminen, Joni & Plé, Loïc & Wirtz, Jochen, 2021. "Artificial intelligence in marketing: Topic modeling, scientometric analysis, and research agenda," Journal of Business Research, Elsevier, vol. 124(C), pages 389-404.
  35. Kurt DeMaagd & Johannes M. Bauer, 2011. "Modeling the dynamic interactions of agents in the provision of network infrastructure," Information Systems Frontiers, Springer, vol. 13(5), pages 669-680, November.
  36. Rodgers, Waymond & Hudson, Robert & Economou, Fotini, 2023. "Modelling credit and investment decisions based on AI algorithmic behavioral pathways," Technological Forecasting and Social Change, Elsevier, vol. 191(C).
  37. repec:ers:journl:v:xxiv:y:2021:i:special2:p:513-522 is not listed on IDEAS
  38. 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.
  39. Liu, Hongju & Pancras, Joseph & Houtz, Malcolm, 2015. "Managing Customer Acquisition Risk Using Co-operative Databases," Journal of Interactive Marketing, Elsevier, vol. 29(C), pages 39-56.
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