BCRLSP: An Offline Reinforcement Learning Framework for Sequential Targeted Promotion

TL;DR

This paper introduces BCRLSP, an offline reinforcement learning framework that optimizes sequential targeted promotions under budget constraints, improving customer retention and reducing costs in real-world business settings.

Contribution

The paper presents a novel offline RL framework combined with linear programming to effectively balance promotion costs and customer retention in sequential marketing strategies.

Findings

BCRLSP outperforms baseline methods in customer retention.

It achieves lower promotion costs while maintaining high retention rates.

The framework adapts well to noisy data and flexible budget constraints.

Abstract

We utilize an offline reinforcement learning (RL) model for sequential targeted promotion in the presence of budget constraints in a real-world business environment. In our application, the mobile app aims to boost customer retention by sending cash bonuses to customers and control the costs of such cash bonuses during each time period. To achieve the multi-task goal, we propose the Budget Constrained Reinforcement Learning for Sequential Promotion (BCRLSP) framework to determine the value of cash bonuses to be sent to users. We first find out the target policy and the associated Q-values that maximizes the user retention rate using an RL model. A linear programming (LP) model is then added to satisfy the constraints of promotion costs. We solve the LP problem by maximizing the Q-values of actions learned from the RL model given the budget constraints. During deployment, we combine the offline RL model with the LP model to generate a robust policy under the budget constraints. Using both online and offline experiments, we demonstrate the efficacy of our approach by showing that BCRLSP achieves a higher long-term customer retention rate and a lower cost than various baselines. Taking advantage of the near real-time cost control method, the proposed framework can easily adapt to data with a noisy behavioral policy and/or meet flexible budget constraints.