The Customer Journey as a Source of Information

We introduce a probabilistic machine learning model that fuses customer click-stream data and purchase data within and across journeys. This approach addresses the critical business need for leveraging first-party data (1PD), particularly in environments with infrequent purchases, which are characterized by minimal or no prior purchase history. Combining data across journeys poses a challenge because customers' needs might vary across different purchase occasions.

 

Our model accounts for this "context heterogeneity" using a Bayesian non-parametric Pitman-Yor process. By drawing from within-journey, past journeys, and cross-customer behaviors, our model offers a solution to the "cold start problem,'" enabling firms to predict customer preferences even without prior interactions. Notably, the model continuously updates the inferred preferences as customers interact with the firm.

 

We apply this model to data from an online travel platform, revealing significant benefits from consolidating 1PD from both current and previous customer journeys. This integration enhances managers' understanding of customer needs, allowing for more effective personalization of marketing tactics, such as retargeting efforts and product recommendations, to better align with customers' dynamic preferences.