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  • Feldmann, Carsten (29)
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Investments in 3D printing: Decision support from a financial supply chain perspective with focus on inbound and outbound logistics. 25th Annual International Purchasing and Supply Education and Research Association (IPSERA) Conference: "Purchasing & Supply Management - from efficiency to effectiveness in an integrated Supply Chain Management" (2016)
Feldmann, Carsten ; Pumpe, Andreas
Holistic evaluation of the impacts of additive manufacturing on sustainability, distribution costs, and time in global supply chains (2019)
Dircksen, Michael ; Feldmann, Carsten
Agile Project Management: Not a Silver Bullet (2020)
Burchardt, Martin ; Stuwe, Stefan ; Feldmann, Carsten
Agile versus Waterfall Project Management: Decision Model for Selecting the Appropriate Approach to a Project (2021)
Thesing, Theo ; Feldmann, Carsten ; Burchardt, Martin
Additive manufacturing in community pharmacies: a framework for business model innovation (2021)
Feldmann, Carsten ; Rose, Olaf
Perception of Additive Manufacturing by SME: Empirical Survey via World Cafés (2019)
Fernströning, Sebastian ; Feldmann, Carsten
Digital Transformation of Companies: Experience Gained in the Implementation of an IoT Check (2019)
Tackenberg, Sven ; Jungkind, Wilfried ; Feldmann, Carsten ; Appelfeller, Wieland
Success Factors for Business Process Improvement Projects in Small and Medium Sized Enterprises – Empirical Evidence (2017)
Lückmann, Patrick ; Feldmann, Carsten
Exoskeletons: Productivity and Ergonomics in Logistics – A Systematic Review (2021)
Kaupe, Victor ; Feldmann, Carsten ; Wagner, Heiko
Exploring Customer Journey Mining and RPA: Prediction of Customers’ Next Touchpoint (2023)
Wiethölter, Jost ; Salingré, Jan ; Feldmann, Carsten ; Schwanitz, Johannes ; Niessing, Jörg
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.
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