Digital Library

of the European Council for Modelling and Simulation



Predicting Next Touch Point In A Customer Journey: A Use Case In Telecommunication


Marwan Hassani, Stefan Habets

Published in:



(2021). ECMS 2021, 35th Proceedings
Edited by: Khalid Al-Begain, Mauro Iacono, Lelio Campanile, Andrzej Bargiela, European Council for Modelling and Simulation.



ISSN: 2522-2422 (ONLINE)

ISSN: 2522-2414 (PRINT)

ISSN: 2522-2430 (CD-ROM)


ISBN: 978-3-937436-72-2
ISBN: 978-3-937436-73-9(CD)


Communications of the ECMS , Volume 35, Issue 1, June 2021,

United Kingdom


Citation format:

Marwan Hassani, Stefan Habets (2021). Predicting Next Touch Point In A Customer Journey: A Use Case In Telecommunication, ECMS 2021 Proceedings Edited By: Khalid Al-Begain, Mauro Iacono, Lelio Campanile, Andrzej Bargiela European Council for Modeling and Simulation. doi: 10.7148/2021-0048



Customer journey analysis is rapidly increasing in popularity, as it is essential for companies to understand how their customers think and behave. Recent studies investigate how customers traverse their journeys and how they can be improved for the future. However, those researches only focus on improving the process for future customers by analyzing the historical data. This research focuses on helping the current customer immediately, by analyzing if it is possible to predict what the customer will do next and accordingly take proactive steps. We propose a model to predict the customer's next contact type (touch point). At first we will analyze the customer journey data by applying process mining techniques. We will use these insights then together with the historical data of accumulated customer journeys to train several classifiers. The winning of those classifiers, namely XGBoost, is used to perform a prediction on a customer's journey while the journey is still active. We show on three different real datasets coming from interactions between a telecommunication company and its customers that we always beat a baseline classifier thanks to our thorough pre-processing of the data.

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