Customer Journey Analytics: A Model for Creating Diagnostic Insights with Process Mining

Daan Weijs, Emiel Caron

2022

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.

Paper Citation

in Harvard Style

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 - Volume 1: ICSOFT, ISBN 978-989-758-588-3, pages 418-424. DOI: 10.5220/0011263900003266

in Bibtex Style

@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 - Volume 1: ICSOFT,},
year={2022},
pages={418-424},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011263900003266},
isbn={978-989-758-588-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Conference on Software Technologies - Volume 1: ICSOFT,
TI - Customer Journey Analytics: A Model for Creating Diagnostic Insights with Process Mining
SN - 978-989-758-588-3
AU - Weijs D.
AU - Caron E.
PY - 2022
SP - 418
EP - 424
DO - 10.5220/0011263900003266