Using Temporal Discovery and Data-Driven Journey-Maps to Predict Customer Satisfaction

Abstract

Timely identification of potentially dissatisfied customers enables us to take meaningful interventions to improve customer experience. The goal of this work is to create models that can predict customer satisfaction for active insurance claims at any point in time during the claim process. In order to capture relevant temporal information, we introduce the concept of a "journey-map": a data-driven structured timeline where all the relevant events pertinent to the claim process are registered and positioned temporally with respect to each other. We also describe a machine-learning-based framework to extract and discover meaningful information relevant for the task at hand. The result of this work is a deployed system currently used during the claims process.