Comparison of tree-based models performance in prediction of marketing campaign results using Explainable Artificial Intelligence tools
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References listed on IDEAS
- David J. Hand & Heikki Mannila & Padhraic Smyth, 2001. "Principles of Data Mining," MIT Press Books, The MIT Press, edition 1, volume 1, number 026208290x, December.
- Friedman, Jerome H., 2002. "Stochastic gradient boosting," Computational Statistics & Data Analysis, Elsevier, vol. 38(4), pages 367-378, February.
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- Srikrishna Chintalapati & Shivendra Kumar Pandey, 2025. "Factors driving the adoption of AI-powered marketing in financial services: a practitioner field study," DECISION: Official Journal of the Indian Institute of Management Calcutta, Springer;Indian Institute of Management Calcutta, vol. 52(1), pages 17-36, March.
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Keywords
direct marketing; telemarketing; relationship marketing; data mining; machine learning; random forest; adaboost; gbm; catboost; xgboost; bank marketing; XAI; variable importance; partial dependency profile;All these keywords.
JEL classification:
- C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
- C44 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Operations Research; Statistical Decision Theory
- M31 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising - - - Marketing
NEP fields
This paper has been announced in the following NEP Reports:- NEP-BIG-2020-06-15 (Big Data)
- NEP-CMP-2020-06-15 (Computational Economics)
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