Revenue Maximization of Airbnb Marketplace using Search Results

TL;DR

This paper presents a two-stage pricing model for Airbnb's search results that learns value distributions and optimizes prices, significantly increasing revenue potential and reducing booking regret.

Contribution

It introduces a practical two-stage pricing approach that leverages learned value distributions to optimize prices in online marketplaces like Airbnb.

Findings

Improves revenue potential by over 55% compared to baseline strategies.

Reduces booking regret by at least 20%.

Effective in large-scale real-world Airbnb data.

Abstract

Correctly pricing products or services in an online marketplace presents a challenging problem and one of the critical factors for the success of the business. When users are looking to buy an item they typically search for it. Query relevance models are used at this stage to retrieve and rank the items on the search page from most relevant to least relevant. The presented items are naturally "competing" against each other for user purchases. We provide a practical two-stage model to price this set of retrieved items for which distributions of their values are learned. The initial output of the pricing strategy is a price vector for the top displayed items in one search event. We later aggregate these results over searches to provide the supplier with the optimal price for each item. We applied our solution to large-scale search data obtained from Airbnb Experiences marketplace. Offline evaluation results show that our strategy improves upon baseline pricing strategies on key metrics by at least +20% in terms of booking regret and +55% in terms of revenue potential.