Deep Reinforcement Learning for Sequential Targeting

Deep reinforcement learning (DRL) has opened up many unprecedented opportunities in revolutionizing the digital marketing field. In this study, we designed a DRL-based personalized targeting strategy. We show that it can address four important challenges in this area. 1) Sequential-decisions: accounting for the dynamic sequential behavior of consumers; 2) Forward-looking: balancing between a firm’s current revenue and future revenues; 3) Earning-while-learning: maximizing profits while continuously learning through exploration-exploitation; 4) Scalability: coping with a high-dimensional state and policy space. We illustrate the above through a novel design of a DRL-based agent. To better adapt DRL to complex consumer behavior dimensions, we proposed a quantization-based uncertainty learning heuristic for efficient exploration-exploitation. Our policy evaluation results suggest that the proposed DRL agent generates 26.75% more long-term revenues than can the non-DRL approaches on average, and learns 76.92% faster than the second fastest model among all benchmarks.Further, to better understand the potential underlying mechanisms, we conducted multiple interpretability analyses to explain the patterns of learned optimal policy. Our findings provide important managerial-relevant and theory-consistent insights. For instance, consecutive price promotions at the beginning can capture price-sensitive consumers’ immediate attention, while carefully spaced non-promotional “cool-down” periods between price promotions can allow consumers to adjust their reference points. Besides, consideration of future revenues is necessary from a long-term horizon, but weighing the future too much also dampens revenues. In addition, the optimal promotion sequence highly varies across consumer engagement stages. Overall, our results demonstrate DRL’s potential to optimize these strategies’ combination to maximize long-term revenues