Abstract
AbstractIndustry is shifting towards service-based business models, for which user satisfaction is crucial. User satisfaction can be analyzed with user journeys, which model services from the user’s perspective. Today, these models are created manually and lack both formalization and tool-supported analysis. This limits their applicability to complex services with many users. Our goal is to overcome these limitations by automated model generation and formal analyses, enabling the analysis of user journeys for complex services and thousands of users. In this paper, we use stochastic games to model and analyze user journeys. Stochastic games can be automatically constructed from event logs and model checked to, e.g., identify interactions that most effectively help users reach their goal. Since the learned models may get large, we use property-preserving model reduction to visualize users’ pain points to convey information to business stakeholders. The applicability of the proposed method is here demonstrated on two complementary case studies.
Publisher
Springer Nature Switzerland
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