Autonomous CRM Control via CLV Approximation with Deep Reinforcement Learning in Discrete and Continuous Action Space
This addresses the problem of optimizing direct marketing actions for businesses by automating CRM decisions, though it is incremental as it builds on existing RFM and reinforcement learning methods.
The paper tackles autonomous CRM control by using a modified RFM metric system to define client states and applying deep Q-learning to determine optimal marketing actions in discrete and continuous spaces, with experiments on the KDD Cup 1998 dataset showing it enables quick CLV estimation.
The paper outlines a framework for autonomous control of a CRM (customer relationship management) system. First, it explores how a modified version of the widely accepted Recency-Frequency-Monetary Value system of metrics can be used to define the state space of clients or donors. Second, it describes a procedure to determine the optimal direct marketing action in discrete and continuous action space for the given individual, based on his position in the state space. The procedure involves the use of model-free Q-learning to train a deep neural network that relates a client's position in the state space to rewards associated with possible marketing actions. The estimated value function over the client state space can be interpreted as customer lifetime value, and thus allows for a quick plug-in estimation of CLV for a given client. Experimental results are presented, based on KDD Cup 1998 mailing dataset of donation solicitations.