Publikation
Data-based Customer-Retention-as-a-Service: Induktive Entwicklung eines datenbasierten Geschäftsmodells auf Basis einer Fallstudie der Automobilbranche
Henrik Kortum-Landwehr; Jonas Rebstadt; Laura Gravemeier; Oliver Thomas
In: HMD - Praxis der Wirtschaftsinformatik (HMD), Vol. 58, Pages 1-15, Springer, 2021.
Zusammenfassung
Many companies are already successfully using artificial intelligence (AI) to process large volumes of data for the purpose of customer retention. Large companies create individualized customer experiences and analyze massive amounts of data to achieve customer loyalty through intelligent recommendations, for example. However, companies with traditional value creation, as of yet often fail to sufficiently address this topic. Therefore, this contribution tackles the implementation of an exemplary use case for data-driven customer retention in a car repair shop. In particular, the aim was to optimize the timing of customer communication based on forecasts of the customers’ daily driving behavior. The basis for this analysis was a data set provided by a car repair shop and the subsequent development of a machine learning model. Based on this case study, a business model is developed that enables companies with traditional value creation and little AI-know-how to use data-driven technologies in customer retention. The underlying platform concept is conceptualized as an open innovation model and supports the interaction of data consumers, data providers and data enablers. In this way, the target is not only to develop own services, but also to establish a data ecosystem for customer loyalty.
Projekte
MUMIS - Multimedia Indexing and Searching Environment,
DBWS - Datenbasierte Wertschöpfungssysteme für den digitalen Mittelstand
DBWS - Datenbasierte Wertschöpfungssysteme für den digitalen Mittelstand