506 / 2024-04-25 17:33:01

Partial Persuasive Marketing on Service Operations Platforms

service operations, partial customers, persuasive marketing

This study investigates the strategic use of partial persuasive marketing on an online service platform under uncertain service quality. We model the partial persuasive marketing with “targeting technology”, assuming that the platform strategically communicates service quality to a subset of customers from the perspective of Bayesian persuasion. A queuing game model is developed to determine the platform’s optimal information strategy, aiming to tune the online service recommendation algorithm’s accuracy. The algorithm commits to binary signal strategies based on service quality, influencing informed customers’ beliefs. All customers then decide whether to join the platform. Interestingly, at a moderate market size, we discover that adjusting the beliefs of partial customers with targeting technology can achieve the same effective arrival rate as adjusting the beliefs of all customers. We show that the platform can maximize throughput by fine-tuning positive signal accuracy or to enhance social welfare by obfuscating negative signals. We demonstrate that a unique targeting strategy that maximizes platform throughput can be identified, and partial persuasive marketing is better in a large market size since it is more difficult to adjust the beliefs of all customers than to adjust the beliefs of a subset of customers. To maximize social welfare, we demonstrate that the optimal targeting strategy and information disclosure level can significantly enhance collective payoffs, particularly when market sizes are unconstrained. This insight challenges the intuition that strategically communicating with all potential customers is invariably the preferred strategy, highlighting the benefits of well-designed and targeted information campaigns.