All Models are Wrong but Some are Useful: the Use of Predictive Analytics in Direct Marketing

Abstract

This paper discusses the use of predictive analytics techniques in direct marketing (DM) and presents the CRISP-DM process for data mining. It explains that DM models generally perform poorly in terms of model-fitting criteria, but illustrates how they are assessed, using gains analyses and lift charts, and how they are shown to have significant business value.

Key Words:

• Analytical model
• CRISP-DM
• data mining
• direct marketing
• gains analysis
• lift chart
• predictive analytics
• ROC curve.

Additional information

Notes on contributors

Barry Leventhal

Barry Leventhal is a marketing statistician who runs an independent analytics consultancy based in the UK. Previously, Barry was Director of Advanced Analytics for Teradata (UK). Prior to that, he had statistical roles in a customer management consultancy, a market analysis company and a market research agency. He holds a BSc and a Ph.D. from University College London, and a diploma in Computer Science from Cambridge University. He is a fellow of the Royal Statistical Society, Market Research Society and Institute of Direct Marketing. He chairs the Census & Geodemographics Group, which is an MRS advisory board, and serves on the executive board of the IDM journal.