Market Segmentation in Online Platforms
Franco Berbeglia, Gerardo Berbeglia, and Pascal Van Hentenryck

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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
