Wirtschaft (MSB)
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- 2023 (19) (remove)
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This research case study presents a novel way to study the development and growth of a multi-sided disruptive platform built on digital technologies. The corresponding business model unfolds industry-changing dynamics eventually changing competition logic in established markets. Despite the appeal of those models, developing and managing such a multi-sided disruptive platform is challenging because multiple platform sides need to be strategically aligned to develop along a disruptive path. Hence, scholars and practitioners are increasingly debating about the dynamics arising in the development and growth of such platforms. The focal case study discusses a research project which contributes to those debates:
This case study discusses how we used topic modeling and qualitative content analysis to make sense of a large amount of historical data from and about multiple platform sides to understand the strategic management and alignment mechanisms that unfolded over time. We discuss how we studied an entrant that was spun off from an established catalog retailer and is steering a multi-sided disruptive platform in the German fashion retail industry. We present how we faced the challenges of collecting data from multiple platform sides and how we used topic modeling to overcome data asphyxiation (i.e. difficulties in making sense of an overwhelming amount of qualitative data). Readers of this case study are equipped with practical insights about a) studying the development of multi-sided platforms over time, and b) using topic modeling and qualitative content analysis as complementing methodological approaches.
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.