Optimal Marketing Strategies for Modeling Real Settings: Improving The Multichannel in Banking

Abstract

This paper presents a dynamic model approach to analyze the utility generated by a customer's buying behavior dynamics. The dynamic of the model is represented by a class of controllable finite Markov Decision Process (MDP). The MDP model the trajectory of the vendor across their states (segments) governed by transition probabilities. We show that the system and the trajectory dynamics converge. For representing the properties of the dynamics we introduce optimal policies to maximize the one-step ahead increment of the utility function. At each time period the system state provides all the information necessary for choosing an action. We model the vendor in terms of the customer behavior over time considering the actions proposed by the vendor in the marketing campaigns. Strategies dictate how the customer makes his decisions (choose a strategy). As a result of choosing a strategy the customer moves to a new (possibly different) state whose probability distribution depends on the previous state and the actions chosen. Validity of the proposed method is successfully demonstrated both, theoretically and by a simulated experiment related to multichannel for a bank.