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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 …