Combining machine learning and Service Design to improve customer experience

Niko Reunanen, Zeynep Falay von Flittner, Virpi Roto, Kirsikka Vaajakallio

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Abstract

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.
Original languageEnglish
Title of host publicationServDes.2020 Tensions, Paradoxes, Plurality
Subtitle of host publicationConference Proceedings
Place of PublicationMelbourne
Number of pages10
Publication statusPublished - Dec 2020
MoE publication typeA4 Article in a conference publication
EventService Design and Service Innovation Conference - RMIT University & online, Melbourne, Australia
Duration: 2 Feb 20215 Feb 2021
https://servdes2020.org/

Conference

ConferenceService Design and Service Innovation Conference
Abbreviated titleServDes
CountryAustralia
CityMelbourne
Period02/02/202105/02/2021
Internet address

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