Pricing strategy in livestream selling with the presence of myopic and strategic consumers

Abstract Following the development of e-commerce, the manufacturer can sell products via direct or livestream modes. To meet market demand, the manufacturer will strategically adjust product prices, and consumers weigh whether to make early- or later-stage purchases based on their needs. In the current market, we assume two types of consumers including myopic and strategic consumers. Meanwhile, the tolerance level of each consumer regarding whether to delay purchase is heterogeneous. In our work, we built a two-stage game model to analyze the impact of consumers’ strategic behavior and their patience to delay purchases on manufacturer’s choice of the stage to adopt the livestream selling strategy under three scenarios including the manufacturer chooses a direct format to sell products in both stages (NN), the manufacturer chooses a direct format in the first stage and a livestream format to sell products in the second stage (NL), the manufacturer chooses a livestream format in the first stage and a direct format to sell products in the second stage (LN). Our main findings show that when strategic consumers dominate the market and consumers are more willing to postpone their purchasing, the manufacturer should adopt a penetration pricing strategy under NL scenario; otherwise, a skimming pricing strategy can be adopted. Under LN scenario, the manufacturer should directly employ a skimming pricing strategy. In addition, when the discount factor and the proportion of myopic consumers are small, and the commission rate is high, the manufacturer is more inclined toward the NL scenario; otherwise, it prefers to the LN scenario. In the extended models, we discuss the influence of different parameters, including the changed popularities of live-streamer, commission rates, and pit fees for the profit of the manufacturer. Moreover, we explore how the manufacturer selects livestream selling in both stages, and compare it with three main scenarios by using numerical studies. The result shows that the LN scenario is optimal in most cases.