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.
Keywords
customer experience, machine learning, service design, impact of design, net promoter score, double diamond process
DOI
https://doi.org/10.21606/servdes2020.16
Citation
Reunanen, N., von Flittner, Z., Roto, V.,and Vaajakallio, K.(2021) Combining machine learning and Service Design to improve customer experience, in Akama, Y., Fennessy, L., Harrington, S., & Farago, A. (eds.), ServDes 2020: Tensions, Paradoxes and Plurality, 2–5 February 2021, Melbourne, Australia. https://doi.org/10.21606/servdes2020.16
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Conference Track
Research Papers
Combining machine learning and Service Design to improve customer experience
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.