Mailing decisions in the catalog sales industry
Management Science
Machine Learning
Principles of Corporate Finance with Cdrom
Principles of Corporate Finance with Cdrom
Self-Organizing Maps
Sequential cost-sensitive decision making with reinforcement learning
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Customer Lifetime Value Models for Decision Support
Data Mining and Knowledge Discovery
Brand Loyalty Programs: Are They Shams?
Marketing Science
Optimizing the Marketing Interventions Mix in Intermediate-Term CRM
Marketing Science
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Many companies have no reliable way to determine whether their marketing money has been spent effectively, and their return on investment is often not evaluated in a systematic manner. Thus, a compelling need exists for computational tools that help companies to optimize their marketing strategies. For this purpose, we have developed computational models of customer buying behavior in order to determine and leverage the value generated by a customer within a given time frame. The term "customer value" refers to the revenue generated from a customer's buying behavior in relation to the costs of marketing campaigns. We describe a new tool, the IBM Customer Equity Lifetime Management Solution (CELM), that helps to determine long-term customer value by means of dynamic programming algorithms in order to identify which marketing actions are the most effective in improving customer loyalty and hence increasing revenue. Simulation of marketing scenarios may be performed in order to assess budget requirements and the expected impact of marketing policies. We present a case study of a pilot program with a leading European airline, and we show how this company optimized its frequent flyer program to reduce its marketing budget and increase customer value.