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A machine learning framework for customer purchase prediction in the non-contractual setting

Citations

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Cited by:

  1. Narendra Singh & Pushpa Singh & Mukul Gupta, 2020. "An inclusive survey on machine learning for CRM: a paradigm shift," DECISION: Official Journal of the Indian Institute of Management Calcutta, Springer;Indian Institute of Management Calcutta, vol. 47(4), pages 447-457, December.
  2. Bottmer, Lea & Croux, Christophe & Wilms, Ines, 2022. "Sparse regression for large data sets with outliers," European Journal of Operational Research, Elsevier, vol. 297(2), pages 782-794.
  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. Liu, Zhenkun & Zhang, Ying & Abedin, Mohammad Zoynul & Wang, Jianzhou & Yang, Hufang & Gao, Yuyang & Chen, Yinghao, 2024. "Profit-driven fusion framework based on bagging and boosting classifiers for potential purchaser prediction," Journal of Retailing and Consumer Services, Elsevier, vol. 79(C).
  5. He Jiang, 2023. "Robust forecasting in spatial autoregressive model with total variation regularization," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(2), pages 195-211, March.
  6. Abdulrashid, Ismail & Chiang, Wen-Chyuan & Sheu, Jiuh-Biing & Mammadov, Shamkhal, 2025. "An interpretable machine learning framework for enhancing road transportation safety," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 195(C).
  7. Daniel Guhl & Friederike Paetz & Udo Wagner & Michel Wedel, 2024. "Predicting and optimizing marketing performance in dynamic markets," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 46(1), pages 1-27, March.
  8. Sarkar, Mainak & De Bruyn, Arnaud, 2021. "LSTM Response Models for Direct Marketing Analytics: Replacing Feature Engineering with Deep Learning," Journal of Interactive Marketing, Elsevier, vol. 53(C), pages 80-95.
  9. Miikka Blomster & Timo Koivumäki, 2022. "Exploring the resources, competencies, and capabilities needed for successful machine learning projects in digital marketing," Information Systems and e-Business Management, Springer, vol. 20(1), pages 123-169, March.
  10. Fan Zou & Yupeng Li & Jiahuan Huang, 2022. "Group interaction and evolution of customer reviews based on opinion dynamics towards product redesign," Electronic Commerce Research, Springer, vol. 22(4), pages 1131-1151, December.
  11. Hoon S. Choi, 2026. "Apple doesn’t fall far from the tree: Effect of extrinsic factors of online reviews on predicting useless reviews," Electronic Commerce Research, Springer, vol. 26(1), pages 121-146, February.
  12. Leogrande, Angelo, 2024. "From Discounts to Delivery: Decoding Customer Care Interactions in Warehousing," MPRA Paper 122693, University Library of Munich, Germany.
  13. Jeremy K. Nguyen & Adam Karg & Abbas Valadkhani & Heath McDonald, 2022. "Predicting individual event attendance with machine learning: a ‘step-forward’ approach," Applied Economics, Taylor & Francis Journals, vol. 54(27), pages 3138-3153, June.
  14. repec:osf:socarx:qzm5v_v1 is not listed on IDEAS
  15. He, Yang & Luo, Jian & Zheng, Yukai, 2025. "A novel ensemble support vector regression for load forecasting under data attacks," Energy, Elsevier, vol. 333(C).
  16. Ahmed, Abdulaziz & Topuz, Kazim & Moqbel, Murad & Abdulrashid, Ismail, 2024. "What makes accidents severe! explainable analytics framework with parameter optimization," European Journal of Operational Research, Elsevier, vol. 317(2), pages 425-436.
  17. Esmeli, Ramazan & Bader-El-Den, Mohamed & Abdullahi, Hassana, 2022. "An analyses of the effect of using contextual and loyalty features on early purchase prediction of shoppers in e-commerce domain," Journal of Business Research, Elsevier, vol. 147(C), pages 420-434.
  18. Dai, Hongyan & Xiao, Qin & Chen, Songlin & Zhou, Weihua, 2023. "Data-driven demand forecast for O2O operations: An adaptive hierarchical incremental approach," International Journal of Production Economics, Elsevier, vol. 259(C).
  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. Ales Jandera & Tomas Skovranek, 2022. "Customer Behaviour Hidden Markov Model," Mathematics, MDPI, vol. 10(8), pages 1-10, April.
  21. 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.
  22. Minnu F. Pynadath & T. M. Rofin & Sam Thomas, 2023. "Evolution of customer relationship management to data mining-based customer relationship management: a scientometric analysis," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(4), pages 3241-3272, August.
  23. Chen, Song & Qiu, Yongqin & Li, Jingmao & Fang, Kan & Fang, Kuangnan, 2023. "Precision marketing for financial industry using a PU-learning recommendation method," Journal of Business Research, Elsevier, vol. 160(C).
  24. Ramazan Esmeli & Mohamed Bader-El-Den & Hassana Abdullahi, 2021. "Towards early purchase intention prediction in online session based retailing systems," Electronic Markets, Springer;IIM University of St. Gallen, vol. 31(3), pages 697-715, September.
  25. João A. Bastos & Maria Inês Bernardes, 2024. "Understanding online purchases with explainable machine learning," Working Papers REM 2024/0313, ISEG - Lisbon School of Economics and Management, REM, Universidade de Lisboa.
  26. Muth, Manuel & Nufer, Gerd, 2024. "Interdisziplinäre Anwendung des Supervised Machine Learning für nachfragerbezogene Analysen im Marketing," PraxisWissen - German Journal of Marketing, AfM – Arbeitsgemeinschaft für Marketing, vol. 9(01/2024), pages 34-52.
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