Randomized price-matching: How the instant retail platform competes with the community group-buying store

Community group-buying, leveraging the “pre-sale + self-pickup” mode, has surged in recent years, offering competitive prices due to operational efficiency and cost-effectiveness, appealing to liberal customers. Nonetheless, conservative customers still favor instant retail platforms, prompting them to frequently cut prices amid emerging competition. Therefore, this study proposes a randomized price-matching (PM) strategy for instant retail platforms to increase their revenue. Under this strategy, the platform randomly decides whether to match the low prices offered by community group-buying stores in each sales period. Therefore, this study constructs a game-theoretical model to explore not only the impact of instant retail platforms’ randomized PM strategies on the pricing and profits of the two retailers but also how strategic customer behavior affects retailers’ pricing strategies. We find that the randomized PM strategy is not always optimal for the instant retail platform, especially when the utility loss from delayed purchases or customer acceptance of group-buying channels is small. Furthermore, the randomized PM strategy often leads the platform and group-buying store to increase their prices. However, it can result in a decrease in the group-buying store’s price when the proportion of liberal customers is small. Interestingly, this strategy has the potential to not only boost the platform’s profits but also benefit the community group-buying store, thus achieving win-win outcome. Furthermore, customer composition and characteristics affect pricing decisions. Specifically, under the randomized PM strategy, as customers’ acceptance of group-buying channels increases, the platform will raise the PM probability.