Combining machine learning and Service Design to improve customer experience

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

Combining machine learning and Service Design to improve customer experience

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

Hellon Oy

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

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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 language	English
Title of host publication	ServDes.2020 Tensions, Paradoxes, Plurality
Subtitle of host publication	2021 Conference Proceedings
Editors	Yoko Akama, Liam Fennessy, Sara Harrington, Anna Farago
Place of Publication	Linköping
Publisher	Linköping University Electronic Press
Pages	124-133
Number of pages	10
ISBN (Print)	978-91-7929-779-4
Publication status	Published - Feb 2021
MoE publication type	A4 Conference publication
Event	Service Design and Service Innovation Conference - RMIT University & online, Melbourne, Australia Duration: 2 Feb 2021 → 5 Feb 2021 https://servdes2020.org/

Publication series

Name	Linköping Electronic Conference Proceedings
Publisher	Linköping University Electronic Press
Number	173
ISSN (Print)	1650-3686
ISSN (Electronic)	1650-3740

Conference

Conference	Service Design and Service Innovation Conference
Abbreviated title	ServDes
Country/Territory	Australia
City	Melbourne
Period	02/02/2021 → 05/02/2021
Internet address	https://servdes2020.org/

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https://servdes2020.s3.amazonaws.com/uploads/event/paper/45/59_Reunanen_FvF_Roto_Vaajakallio_SP.pdfLicence: CC BY-NC-ND

Cite this

Reunanen, N., Falay von Flittner, Z., Roto, V., & Vaajakallio, K. (2021). Combining machine learning and Service Design to improve customer experience. In Y. Akama, L. Fennessy, S. Harrington, & A. Farago (Eds.), ServDes.2020 Tensions, Paradoxes, Plurality: 2021 Conference Proceedings (pp. 124-133). (Linköping Electronic Conference Proceedings; No. 173). Linköping University Electronic Press. https://servdes2020.s3.amazonaws.com/uploads/event/paper/45/59_Reunanen_FvF_Roto_Vaajakallio_SP.pdf

Reunanen, Niko ; Falay von Flittner, Zeynep ; Roto, Virpi et al. / Combining machine learning and Service Design to improve customer experience. ServDes.2020 Tensions, Paradoxes, Plurality: 2021 Conference Proceedings. editor / Yoko Akama ; Liam Fennessy ; Sara Harrington ; Anna Farago. Linköping : Linköping University Electronic Press, 2021. pp. 124-133 (Linköping Electronic Conference Proceedings; 173).

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title = "Combining machine learning and Service Design to improve customer experience",

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. ",

author = "Niko Reunanen and {Falay von Flittner}, Zeynep and Virpi Roto and Kirsikka Vaajakallio",

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editor = "Yoko Akama and Liam Fennessy and Sara Harrington and Anna Farago",

booktitle = "ServDes.2020 Tensions, Paradoxes, Plurality",

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Reunanen, N, Falay von Flittner, Z, Roto, V & Vaajakallio, K 2021, Combining machine learning and Service Design to improve customer experience. in Y Akama, L Fennessy, S Harrington & A Farago (eds), ServDes.2020 Tensions, Paradoxes, Plurality: 2021 Conference Proceedings. Linköping Electronic Conference Proceedings, no. 173, Linköping University Electronic Press, Linköping, pp. 124-133, Service Design and Service Innovation Conference, Melbourne, Australia, 02/02/2021. <https://servdes2020.s3.amazonaws.com/uploads/event/paper/45/59_Reunanen_FvF_Roto_Vaajakallio_SP.pdf>

Combining machine learning and Service Design to improve customer experience. / Reunanen, Niko; Falay von Flittner, Zeynep; Roto, Virpi et al.
ServDes.2020 Tensions, Paradoxes, Plurality: 2021 Conference Proceedings. ed. / Yoko Akama; Liam Fennessy; Sara Harrington; Anna Farago. Linköping: Linköping University Electronic Press, 2021. p. 124-133 (Linköping Electronic Conference Proceedings; No. 173).

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

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AU - Falay von Flittner, Zeynep

AU - Roto, Virpi

AU - Vaajakallio, Kirsikka

PY - 2021/2

Y1 - 2021/2

N2 - 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.

AB - 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.

UR - https://servdes2020.org/events/45-combining-machine-learning-and-service-design-to-improve-customer-experience

M3 - Conference article in proceedings

SN - 978-91-7929-779-4

T3 - Linköping Electronic Conference Proceedings

SP - 124

EP - 133

BT - ServDes.2020 Tensions, Paradoxes, Plurality

A2 - Akama, Yoko

A2 - Fennessy, Liam

A2 - Harrington, Sara

A2 - Farago, Anna

PB - Linköping University Electronic Press

CY - Linköping

T2 - Service Design and Service Innovation Conference

Y2 - 2 February 2021 through 5 February 2021

ER -

Reunanen N, Falay von Flittner Z, Roto V, Vaajakallio K. Combining machine learning and Service Design to improve customer experience. In Akama Y, Fennessy L, Harrington S, Farago A, editors, ServDes.2020 Tensions, Paradoxes, Plurality: 2021 Conference Proceedings. Linköping: Linköping University Electronic Press. 2021. p. 124-133. (Linköping Electronic Conference Proceedings; 173).

Combining machine learning and Service Design to improve customer experience

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Conference

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Combining machine learning and Service Design to improve customer experience

Abstract

Publication series

Conference

Access to Document

Other files and links

Fingerprint

Projects

-: Sea4Value A80325

Cite this