Supplementary files for "Optimizing promotional campaigns to maximize customer lifetime value: A dynamic learning approach"

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Supplementary files for article "Optimizing promotional campaigns to maximize customer lifetime value: A dynamic learning approach" Developing responsive dynamic marketing strategies can be challenging in the absence of complete customer information, such as share of wallet, limiting the ability of the firm to target promotions and other marketing efforts with a view to optimizing customer lifetime value (CLV). Furthermore, much of the existing research on CLV treats customers as receivers, rather than co-creators, of services. We address these two key challenges by developing a reinforcement learning (RL)-based promotion optimization model to determine which promotion strategies are most suitable for targeting different customer groups. Specifically, using feedback derived from customers’ real-time transactional responses to promotional campaigns, we present an RL algorithm that (a) continually refines the estimated effectiveness of promotions, aligning them to customers’ preferences to maximize their CLV, and that (b) supports value co-creation by involving customers as active participants to enhance their service experience. We demonstrate the effectiveness of the model through simulation scenarios within the context of a ferry travel agency, providing evidence of its real-world potential. © The Author(s), CC BY 4.0

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