Market Segmentation in Online Platforms

TL;DR

This paper analyzes how market segmentation and ranking policies in online trial-offer marketplaces affect long-term sales, considering consumer heterogeneity and social influence, and provides guidelines for when to segment or aggregate.

Contribution

It introduces a complex stochastic model to quantify the benefits of market segmentation in online platforms with social influence and heterogeneous consumers.

Findings

Market segmentation improves long-term sales in highly heterogeneous markets.

Consumer heterogeneity makes the ranking problem NP-hard.

Segmentation benefits are robust to small classification errors.

Abstract

This paper studies ranking policies in a stylized trial-offer marketplace model, in which a single firm offers products and has consumers with heterogeneous preferences. Consumer trials are influenced by past purchases and the ranking of each product. The platform owner needs to devise a ranking policy to display the products to maximize the number of purchases in the long run. The model proposed attempts to understand the impact of market segmentation in a trial-offer market with social influence. In our model, consumer choices are based on a very general choice model known as the mixed MNL. We analyze the long-term dynamics of this highly complex stochastic model and we quantify the expected benefits of market segmentation. When past purchases are displayed, consumer heterogeneity makes buyers try the sub-optimal products, reducing the overall sales rate. We show that consumer heterogeneity makes the ranking problem NP-hard. We then analyze the benefits of market segmentation. We find tight bounds to the expected benefits of offering a distinct ranking to each consumer segment. Finally, we show that the market segmentation strategy always benefits from social influence when the average quality ranking is used. One of the managerial implications is that the firm is better off using an aggregate ranking policy when the variety of consumer preference is limited, but it should perform a market segmentation policy when consumers are highly heterogeneous. We also show that this result is robust to relatively small consumer classification mistakes; when these are large, an aggregate ranking is preferred.