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Authors: Daan Weijs and Emiel Caron

Affiliation: Tilburg University, Department of Management, Warandelaan 2, PO Box 90153, Tilburg, The Netherlands

Keyword(s): Customer Journey, Touchpoints, Customer Journey Analytics, Data Models, Process Mining.

Abstract: The customer journey is becoming more complex due to digitization of business processes, broadening the gap between the proposed journey and the journey that is actually experienced by customers. Customer Journey Analytics (CJA) aims to detect and analyse pain points in the journey in order to improve the customer experience. This study proposes an extended version of the Customer Journey Mapping (CJM) model, to measure the impact of different types of touchpoints along the customer journey on customer experience, and to apply process mining to gain more insight in the gap between proposed and actual journeys. Moreover, this model is used to develop dedicated CJA based on process mining techniques. A case study on e-commerce applies the CJM-model in practice and shows how the combination of process mining techniques can answer the analysis questions that arise in customer journey management.

CC BY-NC-ND 4.0

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Paper citation in several formats:
Weijs, D. and Caron, E. (2022). Customer Journey Analytics: A Model for Creating Diagnostic Insights with Process Mining. In Proceedings of the 17th International Conference on Software Technologies - ICSOFT; ISBN 978-989-758-588-3; ISSN 2184-2833, SciTePress, pages 418-424. DOI: 10.5220/0011263900003266

@conference{icsoft22,
author={Daan Weijs. and Emiel Caron.},
title={Customer Journey Analytics: A Model for Creating Diagnostic Insights with Process Mining},
booktitle={Proceedings of the 17th International Conference on Software Technologies - ICSOFT},
year={2022},
pages={418-424},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011263900003266},
isbn={978-989-758-588-3},
issn={2184-2833},
}

TY - CONF

JO - Proceedings of the 17th International Conference on Software Technologies - ICSOFT
TI - Customer Journey Analytics: A Model for Creating Diagnostic Insights with Process Mining
SN - 978-989-758-588-3
IS - 2184-2833
AU - Weijs, D.
AU - Caron, E.
PY - 2022
SP - 418
EP - 424
DO - 10.5220/0011263900003266
PB - SciTePress

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