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With the increasingly fierce market competition, offering a free trial hasbecome a potent stimuli strategy to promote products and attract users. Byproviding users with opportunities to experience goods without charge, a freetrial makes adopters know more about products and thus encourages theirwillingness to buy. However, as the critical point in the promotion process,finding the proper adopters is rarely explored. Empirically winnowing users bytheir static demographic attributes is feasible but less effective, neglectingtheir personalized preferences. To dynamically match the products with the bestadopters, in this work, we propose a novel free trial user selection modelnamed SMILE, which is based on reinforcement learning (RL) where an agentactively selects specific adopters aiming to maximize the profit after freetrials. Specifically, we design a tree structure to reformulate the actionspace, which allows us to select adopters from massive user space efficiently.The experimental analysis on three datasets demonstrates the proposed model'ssuperiority and elucidates why reinforcement learning and tree structure canimprove performance. Our study demonstrates technical feasibility forconstructing a more robust and intelligent user selection model and guides forinvestigating more marketing promotion strategies.

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