Although digital services are supposed to simplify our lives and increase our efficiency, they often frustrate and burden customers, users, and employees. Based on user journeys we will analyse, model, and observe how humans experience digital services, rather than taking the perspective of service providers and service systems. The main source of data comes from two complementary service providers: GrepS, a start-up company offering software services for analysing programming skills, and Telenor, a telecom company offering a wide range of mobile-, broadband- and TV services in the Nordics and in Asia. We will trace data left from users in various systems during repeated interactions with a service over time, on the level of individuals.
In the first phase of the project, we will build tools for automated capture of repeated user interactions in digital channels. Based on the resulting database of user journeys and user models, we will use logic-based techniques and machine learning to expose deviations and predict possible behaviours. The accumulation of user journeys will also enable identification of patterns that optimize the user experience and service quality.
We will extend and use executable modelling languages and their associated analysis tools to describe, predict, and prescribe user journeys as concurrent processes. We will base these languages and analysis tools on formal methods and concurrency theory, which build on the foundation of theoretical computer science.