Improving Direct Marketing Activities Effectiveness Using Analytical Models: RFM vs. Logit Model on a Casino Case

Abstract

This research deals with the development and implementation of a large-scale analytics framework for improving segmentation and targeting of a service firm's direct marketing activities. The aim of the framework is to create a direct marketing response model using customers' demographics and other behavioural data (such as past response to direct marketing activities) from a casino (gambling industry). Prior to this research the company was using Recency-Frequency-Monetary (thereafter referred as rfm model). The statistical model used in our research, based on logit regression, significantly improves the accuracy of direct marketing activities as well as provides insight on relevant customers' characteristics that affect choice. We believe the results are a showcase of combining large, disaggregate, individual-level datasets with marketing analytics solutions for improving response to the marketing-communication mix. As per our knowledge in the time of writing this paper no such complete set of demographical and behavioural determinants have been used in direct marketing effectiveness analysis in the casino industry. Findings in this paper can be used by the company to considerably improve fine-tuning of target segments in their direct marketing activities, other industries (currently using rfm for direct marketing activities target group selection) can also benefit.