In this paper, we address the problem of making optimal product offers to customers of a retail bank by using techniques including Markov chains, genetic algorithms, mathematical programming, and design of experiments. Our challenges were large problem size, uncertainty about estimates of customer responses to product offers, and practical issues in training and implementation. The solution had an estimated financial impact of around $20 million; it also provided other intangible benefits, including structured decision making, the capability of performing what-if analysis, and portability to other markets and portfolios.

This article proposes a structure for personalized promotions in multiplexes using RFM (Recency, Frequency, and Monetary Value) Analysis, predictive modeling, and optimization at different stages. The stiff competition in the multiplex industry in India makes it essential for the players to have a good loyalty program. One effective way for increasing loyalty would be to introduce personalized promotions for the customers. This would make the promotion more targeted towards the needs of the customers and would keep them involved. One major challenge in implementing any personalized promotion is to have a good and efficient structure for the same. The proposed structure is capable of incorporating business constraints and providing useful business insights to help the multiplex have an effective loyalty program.