TY  - CHAP
A1  - Wiethölter, Jost
A1  - Salingré, Jan
A1  - Feldmann, Carsten
A1  - Schwanitz, Johannes
A1  - Niessing, Jörg
ED  - Köpke, Julius
ED  - Plattfaut, Ralf
ED  - Gdowska, Katarzyna
ED  - Munoz-Gama, Jorge
ED  - van der Werf, Jan Martijn
ED  - López-Pintado, Orlenys
ED  - Rehse, Jana-Rebecca
ED  - Gonzalez-Lopez, Fernanda
ED  - Smit, Koen
T1  - Exploring Customer Journey Mining and RPA: Prediction of Customers’ Next Touchpoint
T2  - Business Process Management: Blockchain, Robotic Process Automation and Educators Forum
N2  - In-depth analysis of customer journeys to broaden the understanding of customer behaviors and expectations in order to improve the customer experience is considered highly relevant in modern business practices. Recent studies predominantly focus on retrospective analysis of customer data, whereas more forward-directed concepts, namely predictions, are rarely addressed. Additionally, the integration of robotic process automation (RPA) to potentially increase the efficiency of customer journey analysis is not discussed in the current field of research. To fill this research gap, this paper introduces “customer journey mining”. Process mining techniques are applied to leverage digital customer data for accurate prediction of customer movements through individual journeys, creating valuable insights for improving the customer experience. Striving for improved efficiency, the potential interplay of RPA and customer journey mining is examined accordingly. The research methodology followed is based on a design science research process. An initially defined customer journey mining artifact is operationalized through an illustrative case study. This operationalization is achieved by analyzing a log file of an online travel agency functioning as an orientation for researchers and practitioners while also evaluating the initially defined framework. The data is used to train seven distinct prediction models to forecast the touchpoint a customer is most likely to visit next. Gradient-boosted trees yield the highest prediction accuracy with 43.1%. The findings further indicate technical suitability for RPA implementation, while financial viability is unlikely.
KW  - Customer Journey Mining
KW  - Customer Journey Mapping
KW  - Robotic Process Automation
KW  - Process Mining
KW  - Prediction
Y1  - 2023
UR  - https://link.springer.com/chapter/10.1007/978-3-031-43433-4_12#Abs1
SN  - 978-3-031-43432-7
U6  - http://dx.doi.org/https://doi.org/10.1007/978-3-031-43433-4
SN  - 1865-1348
SP  - 181
EP  - 196
PB  - Springer
ER  -