Expedia Group’s Customer Lifetime Value Prediction Model
Customer Lifetime Value (CLV) represents the customer’s future cash flows over a long-term horizon, such as one year and beyond. Having the ability to estimate the future value of each customer enables businesses to make better decisions about customer acquisition, retention, incentives, marketing communications, long-term investments, and growth.
In this blog, we describe development, implementation and deployment of the CLV prediction models on the Unified Machine Learning Platform at Expedia Group™ (EG). The models are re-trained monthly and future value predictions for hundreds of millions of customers are updated daily and are consumed by business units and teams within EG.
Many different approaches exist to calculate CLV, including Cohort Analysis, RFM (Recency, Frequency, Monetary Value) framework, and statistical Buy-Till-You-Die models. However, these approaches suffer from at least one of the following limitations:
Calculate CLV only for predefined segments of customers, and as a result, the calculations should be redone to calculate CLV for any new segments.
They are based solely on the purchase history and, therefore, fail to account for non-monetary inputs that drive CLV, such as customer engagement and satisfaction.
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