Evaluation of prediction models for marketing campaigns

Saharon Rosset · Einat Neuman · Uri Eick · Nurit Vatnik · Izhak Idan

37 citations0/0 files storedACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)

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Customer churn and segmentation Consumer Market Behavior and Pricing Forecasting Techniques and Applications

Abstract

We consider prediction-model evaluation in the context of marketing-campaign planning. In order to evaluate and compare models with specific campaign objectives in mind, we need to concentrate our attention on the appropriate evaluation-criteria. These should portray the model's ability to score accurately and to identify the relevant target population. In this paper we discuss some applicable model-evaluation and selection criteria, their relevance for campaign planning, their robustness under changing population distributions, and their employment when constructing confidence intervals. We illustrate our results with a case study based on our experience from several projects.

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1998Data mining for direct marketing: problems and solutions 1989Direct Marketing Management

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2009Business Intelligence from Voice of Customer 2004Implementing Neural Networks for Decision Support in Direct Marketing 2002Performance-based variable selection for scoring models 2012Uniformly subsampled ensemble (USE) for churn management: Theory and implementation 2007Analytics-driven solutions for customer targeting and sales-force allocation 2008Model selection for direct marketing: performance criteria and validation methods 2008Supervised Classification for Decision Support in Customer Relationship Management 2004A Comparison of Logistic Regression, k-Nearest Neighbor, and Decision Tree Induction for Campaign Management

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