Combining machine learning and Service Design to improve customer experience

Niko Reunanen
Hellon, Finland

Zeynep Falay von Flittner
Hellon, Finland

Virpi Roto
Aalto University School of Arts, Design and Architecture, Helsinki, Finland

Kirsikka Vaajakallio
Hellon, Finland

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Ingår i: ServDes.2020 Tensions, Paradoxes and Plurality Conference Proceedings, 2-5th February 2021, Melbourne, Australia

Linköping Electronic Conference Proceedings 173:16, s. 124-133

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Publicerad: 2020-12-22

ISBN: 978-91-7929-779-4

ISSN: 1650-3686 (tryckt), 1650-3740 (online)


Service design is an effective approach for service-based businesses to improve customer experience. However, Double Diamond design process has limitations in identifying the development areas with most business impact. Combining service design process with machine learning presents a new opportunity for alleviating the aforementioned limitation. We present a case from a European service design agency and a Nordic life insurance company to describe the utilization of machine learning in the beginning of the service design process. With this new process we were able to quantify business impact of different customer experience factors and focus the design effort towards the most potential area. Additionally, we increased the buy-in from top management by enhancing the credibility of the qualitative approach with numeric evidence of customer experience data. The work resulted in increased Net Promoter Score for the client organization.


customer experience, machine learning, service design, impact of design, net promoter score, double diamond process


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