Valuing customer portfolios with endogenous mass-and-direct-marketing interventions using a Stochastic Dynamic Programming Decomposition

Esteban-Bravo, Mercedes and Vidal-Sanz, Jose M. and Yildirim, Gokhan (2014) Valuing customer portfolios with endogenous mass-and-direct-marketing interventions using a Stochastic Dynamic Programming Decomposition. Marketing Science, 33 (5). pp. 621-640. ISSN 0732-2399

Abstract

The CRM allocation of marketing budget is potentially misleading when it uses individual CLV estimations from historical data. Planned marketing interventions would change the purchasing behavior of different customers and history- based decisions would thus be sub-optimal. To cope with this inherent endogeneity, we model the optimal allocation of the marketing mix by accounting simultaneously for mass interventions and direct marketing interventions on each customer. This is a large stochastic dynamic problem that, in general, is computationally rather intractable due to the “curse of dimensionality”. We present an algorithm to derive the optimal marketing policies (how the firm should allocate its marketing resources), and the expected present value of those decisions which maximize the long-term profitability of firms. This allows the firm to value customers/segments and helps the firm to target the customers/segments that maximize long-term profitability given the optimal marketing resources allocation. We apply the proposed approach in the context of a manufacturer of kitchen appliances. The results identify the most effective marketing policies and the endogenous customer values. It is in this context that we also dynamically identify the most-profitable customer and the short- and long-term effects of marketing activities on each customer.