LAUSR

Studies show that merchants are heterogenous in profitability from offering promotions on third-party-online-promotion marketplaces who often charge a single commission rate. Using a data analytics system, a marketplace can classify merchants according to their heterogeneous characteristics and offer merchant-type specific commission rates. In this paper, we construct a game-theoretic model consisting a marketplace with two types of merchants who have heterogeneous proportion of consumers who are informed about their offering. The types are merchants' private information, but the marketplace can invest in data analytics capability to classify merchants as per their types with a probability. We study a signal-based strategy, where the marketplace invests in data analytics capability and offers a specific commission rate to individual merchant based on the merchant-type classification and compare it with a single-rate strategy of offering one Accepted Article This article is protected by copyright. All rights reserved commission rate to all merchants. We show that the relative strength and weakness of the signal-based strategy depend on the merchant type distribution and the investment cost of improving the classification accuracy rate. Interestingly, the marketplace can be better off with the single-rate strategy when a merchant type dominates the market. Moreover, we show that the signal-based strategy, can lead to an increase in profit for merchants and an increase in consumer surplus. This is so because the marketplace's signal-based strategy has a cascade effect on consumers through the merchant's optimal discount rate strategy. We conclude by identifying the conditions for a win-win-win situation wherein investment in data analytics capabilities by the marketplace also benefits merchants and consumers.