Deep Reinforcement Learning for Sequential Targeting

Wen Wang (), Beibei Li (), Xueming Luo () and Xiaoyi Wang ()
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Wen Wang: Robert H. Smith School of Business, University of Maryland, College Park, Maryland 20742
Beibei Li: Information Systems and Management, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213
Xueming Luo: Fox School of Business, Temple University, Philadelphia, Pennsylvania 19122
Xiaoyi Wang: School of Management, Zhejiang University, Hangzhou 310058, China

Management Science, 2023, vol. 69, issue 9, 5439-5460

Abstract: 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 in a sequential setting. We show that the strategy is able to address three important challenges of sequential targeting: (1) forward looking (balancing between a firm’s current revenue and future revenues), (2) earning while learning (maximizing profits while continuously learning through exploration-exploitation), and (3) scalability (coping with a high-dimensional state and policy space). We illustrate this through a novel design of a DRL-based artificial intelligence (AI) 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 through simulation 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, in order to better understand the potential underlying mechanisms, we conducted multiple interpretability analyses to explain the patterns of learned optimal policy at both the individual and population levels. 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, whereas carefully spaced nonpromotional “cooldown” periods between price promotions can allow consumers to adjust their reference points. Additionally, consideration of future revenues is necessary from a long-term horizon, but weighing the future too much can also dampen revenues. In addition, analyses of heterogeneous treatment effects suggest that the optimal promotion sequence pattern highly varies across the consumer engagement stages. Overall, our study results demonstrate DRL’s potential to optimize these strategies’ combination to maximize long-term revenues.

Keywords: deep reinforcement learning; DRL; sequential targeting; promotions; forward looking; exploration-exploitation; scalability; AI (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:inm:ormnsc:v:69:y:2023:i:9:p:5439-5460

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