Community-Based Live Selling: A Strategic Model

Abstract

Community-based live selling—where entrepreneurs act as both influencers and direct-to-consumer sellers—offers small businesses new ways to reach consumers through real-time social commerce. We develop a game-theoretic model of how streamers allocate scarce time during live sessions between two competing activities: educating followers about product categories (which builds informed consumers) versus describing specific products (which enables precise product-consumer matching). This trade-off shapes market outcomes by interacting with two key mechanisms: social learning, where uninformed buyers mimic informed ones, and urgency, which strengthens the social signals generated by quick purchase decisions. Our analysis yields four insights. First, social learners benefit streamers only when product quality is moderate; with high-quality products, focusing on informed buyers is optimal. Second, social learners can raise consumer surplus across the community, particularly when they are highly responsive to social signals. Third, educating followers allows streamers to charge premiums for moderately good products but may reduce overall consumer surplus. Finally, expanding the community is not always optimal—even if costless—though effective education makes expansion more attractive. These results highlight how live commerce generates distinctive forms of imprecise matching and reveal counterintuitive effects: bigger communities and higher quality are not always better. The findings carry implications for seller strategy, platform design, and consumer protection in this rapidly growing retail channel.