Focus area of Data Science

Customer Journey

Informed and responsible analytics to understand and improve the customer journey

It is time to find streams of knowledge in a sea of data!

Customers interact with an organization and its products and services in various ways: online shopping, after-sales, added services, social media, complaints, actual product usage (internet of things), upgrades, etc. To understand and to improve the overall customer journey, it is vital to link the various touch-points. This is an extremely challenging multidisciplinary problem that we analyze from several complementary research perspectives: predictive analytics, data mining, process mining, human computer interaction, user psychology, marketing, and innovation.

During the interaction with an organization, customers leave many traces of their behavior. The interpretation of these traces and the extraction of actionable knowledge requires expertise on data collection and statistical techniques. Actionable knowledge is linked to understanding customers in the way that they behave, collect information and decide. A vast amount of data makes this possible. Data analytics ought to be employed to understand the user population, the organization’s outputs and the interaction between them. It is time to find streams of knowledge in a sea of data!

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Research Challenges

Our research aims at actionable and trustable insights, that can be interpreted by its users. Psychology and marketing insights enrich the data and process mining techniques that we use. We also want to understand how customer behavior changes over time, under evolving circumstances and how it varies between sub-groups. As many customer journeys are long time experiences, we need to adjust our approaches to cope with this long-term time scale. At the same time we strive for real-time predictive models to quickly react to customer behavior. 

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