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Customer journey mapping (CJM) is a popular technique used to increase a company's understanding of their customers. In its simplest form, a CJM shows the main customer paths. When dealing with complex customers' trajectories, these paths are difficult to apprehend, losing the benefit of using a CJM. We present a javascript-based tool that can leverage process mining models, namely process trees, and business owners' knowledge to semi-automatically build a CJM at different levels of granularity. We applied our approach with a dataset describing a complex process, and shows that our technique can abstract it in a meaningful way. By doing so, we contribute by showing how process mining and CJM can be put closer together.

Anticipating the next events of an ongoing series of activities has many compelling applications in various industries. It can be used to improve customer satisfaction, to enhance operational efficiency, and to streamline health-care services, to name a few. In this work, we propose an algorithm that predicts the next events by leveraging business process models obtained using process mining techniques. Because we are using business process models to build the predictions, it allows business analysts to interpret and alter the predictions. We tested our approach with more than 30 synthetic datasets as well as 6 real datasets. The results have superior accuracy compared to using neural networks while being orders of magnitude faster.

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