Terragni, A., & Hassani, M. (2019). Optimizing customer journey using process mining and sequence-aware recommendation. In SAC ’19 Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing (pp. 57-65). New York: Association for Computing Machinery, Inc. DOI: 10.1145/3297280.3297288
Customer journey analysis aims at understanding customer behavior both in the traditional offline setting and through the online website visits. Particularly for the latter, web analytics tools like Google Analytics and customer journey maps have shown their usefulness, by being widely used by web companies. Nevertheless, they provide an oversimplified version of the user behavior in addition to other limitations related to the narrow scope over the cases. This paper contributes a novel approach to overcome these limitations by applying process mining and recommender systems techniques to web log customer journey analysis. Through our novel approach we are able to (i) discover the process that better describes the user behavior, (ii) discover and compare the processes of different behavioral clusters of users, and then (iii) use this analysis to improve the journey by optimizing some KPIs (Key Performance Indicators) via personalized recommendations based on the user behavior. In particular, with process mining it is possible to identify specific customer journey paths that can be enforced to optimize some KPIs. Then, with our novel, sequence-aware recommender system, it is possible to recommend to users particular actions that will optimize the selected KPIs, using the customer journey as an implicit feedback. The proof of the correctness of the introduced concepts is demonstrated through a real-life case study of 10 million events representing the online journeys in 1 month of 2 million users. We show and evaluate the discovered process models from this real web log, then use the extracted information from the process models to select and optimize a KPI via personalized recommendations.