Who are the Best Adopters? User Selection Model for Free Trial Item Promotion

Shiqi Wang, Chongming Gao, Min Gao, Junliang Yu, Zongwei Wang, Hongzhi Yin

Abstract: With the increasingly fierce market competition, the free trial has been widely applied as an effective incentive strategy to attract users and promote products. By providing opportunities to experience goods without charge, a free trial offers adopters more direct contact with the products and thus raises their willingness to buy. However, as the key point in the promotion process, how to select proper adopters is rarely explored. Empirically winnowing users by their static demographic attributes is feasible but less effective due to the lack of consideration of personalized demands. In this work, we propose SMILE – a tailored free trial user selection model for finding the best adopters promptly. Based on the reinforcement learning (RL) technique, SMILE can consider long-run profits and rely on user-item interactions to suggest actions. Besides, since selecting adopters from the large user candidates set is time-consuming, we design a balanced tree structure that reformulates the user action space. The experimental analysis on three datasets demonstrates the proposed model's superiority and elucidates why reinforcement learning and tree structure can improve performance. Our study shows technical feasibility of constructing a more robust and intelligent user selection model and guides for investigating more marketing promotion strategies.