A Dynamic Pricing Model Provides High-Value Insights to Multi-National Travel Company

The client, a provider of luxury vacation experiences, manages hundreds of properties across the globe as short-term rentals for their club members. The pricing team is responsible for setting the price across all properties and arrival dates. They were interested in identifying when arrival dates were experiencing higher-than-expected demand to better align their pricing to available rental capacity and customer demand. This resulted in a need for a demand-enhanced dynamic pricing model to drive revenue uplift and margin expansion opportunities.

The Aimpoint Digital team, in conjunction with the client data team, developed a framework to estimate the propensity a given arrival date would be booked as of the day the prediction was being made. Our team started by conducting stakeholder interviews to identify pain points, then analyzed millions of reservation and pricing data points to develop a robust modeling framework. The framework consists of three core components, a booking probability model, a model experimentation tool, and a price optimization tool. Additionally, the framework provided analytical tools covering model diagnostics, price elasticity of demand estimates, and a calendar visualization to quickly identify highly demanded destinations.