Analyzing customer journey with process mining: from discovery to recommendations

Customer journey analysis is a hot topic in marketing. Understanding how the customers behave is crucial and is considered as one of the key drivers of business success. To the best of our knowledge, a data-driven approach to analyze the customer journey is still missing. For instance, web analytics tools like Google Analytics provide an oversimplified version of the user behavior, focusing more on the frequency of page visits rather than discovering the process of the visit itself. On the other hand, customer journey maps have shown their usefulness, but they need to be created manually by domain experts. This paper contributes a novel approach for applying process mining techniques to web log customer journey analysis. Through process mining we are able to (i) discover the process that better describes the user behavior, (ii) find useful insights, (iii) compare the processes of different clusters of users, and then (iv) use this analysis to improve the journey by optimizing some KPIs (Key Performance Indicators) via personalized recommendations based on the user behavior. We show through a real-life case study a proof of the correctness of the introduced concept by improving the recommender accuracy when incorporating additional context information about the journey as extracted from the process model