Abstract
The background to this article is a quantitative model for data-driven value creation that conceptually explains a holistic approach to finding optimal data-driven service configurations along the customer lifecycle by modeling both provider and customer value and identifying the optimum on the Pareto front. This model, which provides an artifact for service optimization in an iterative design process, is characterized by different inputs that model the costs and benefits of providers and customers depending on different intensities of data usage for services in the respective phases of the lifecycle. In this article, we analyse empirical industrial service configurations by applying the quantitative model and derive insights for optimizing value creation. The analysis shows that this optimization leads to solutions that are not simply achieved by maximizing the intensity of data usage of individual services, but by specifically optimizing the reconfiguration of operant resources along the lifecycle. This enables an overall optimization in a designoriented, iterative approach that differs significantly from standard models that target ascending levels of intensity and maturity of digitization.
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