Data Science for Service Design
You-et / May 1, 2021
Bringing design and data science together with a research project:
An exploration of the opportunities, challenges and methods for data science to support the service design process.
This project contributes to the diversity of the designers’ methodology toolkit with a set of methods. The methods improve user research and help designers in their creative process. Together these results encourage organisations to mature with data science resources for design projects so that their services benefit from more informed designers.
Introduction
At the Design Research Society (DRS) Conference of 2020, Youet presented the research on Data Science for Service Design. The video of this presentation explains this project in a high over summery.
From definitions to methods
This project answers the following research question: When and how can data science be applied to support service designers? The project starts with defining the research areas, understanding designers and data scientists and creating a mental framework. The iterative research process results in a guide to methods for service designers. By analysing these concepts, this project shows the diverse ways data science can support the service design process.
What is Data Science?
Simply put, data science is the study of data and what this means depends on the perspective (Cao, 2017). In the Data Science of Service Design project, data science is defined as the development of techniques that deliver discoveries, predictions, recommendations, and insights based on data (the 'data product' perspective from Cao, 2017). For example, data science technologies, such as Data Mining, support the extraction of meaningful knowledge from large data sets (van der Aalst, 2014).
What is Service Design?
Service design offers a unique perspective on service innovation and the development of the value co-creating between the company and the customers (Costa et al., 2018; Patrício et al., 2018). Therefore, service designers model the holistic experience and inclusive environment of services (Zomerdijk & Voss, 2010). Service designers work typically with user-centred methods, where users and all stakeholders are involved as much as possible (Stickdorn, Schneider, Andrews, & Lawrence, 2011).
Research process
The iterative research process involved both academic and practical designers in user-centred activities. The process alternated between converging and diverging activities and is divided into three main phases: exploration, ideation and evaluation. Throughout these phases, the research explored the methods by emerging, splitting, merging, terminating and changing.
The first phase, namely exploration, focused on understanding designers and data scientists with activities such as shadowing, interviewing, and literature review. The ideation phase resulted in a guide to methods that apply data science and collaboration. At this point, case studies and speculative cases helped to refine the ideas with the help of design practitioners. Main activities in this phase consist of brainstorm methods and feedback sessions with designers. The evaluation phase aimed at testing the usability and desirability of the methods and gathering overall findings from the design research.
Results
This practice-based design research resulted in the capture and selection of the most relevant data science technologies for service design. The collection of methods serves as an introductory guide to data science for service designers. In the thesis, the methods are accompanied by theory, (hypothetical) cases and feedback from design practitioners. The project also addresses how integrating data science techniques requires organisational maturity, which influences the possibilities and challenges of combined projects.
One of the methods
For example, physiological condition mining (Bio translations) provides designers a wealth of subconscious information about humans that can be extracted and analyzed with the help of physiological measurements. It will enable the designer to understand the journey of customers and measure quantitative emotions. In the evaluation workshop, the practitioners discussed the speculative examples of video and smart divices analyses with physiological condition mining. The designers valued especially the quantification of the emotions in journeys & user tests and analysis of multiple user tests. Mining based on tracking with GPS (e.g. smart devices) could lack empathy with the users. It's recommendated that the anaylsis also includes abstract layers such as mental states, behaviours, emotions and context.
Two examples of fictive video analyses, where data science is applied in the context of service design.
Methods in the design process
The service design process distinguishes different phases that are research-heavy and analyse users and systems (e.g. understand and test). It makes sense that data science fit these phases well. However, this research showed that data science also supports designers outside this scope. Design teams can use data science indirectly in tools during the whole project. Data science can additionally stimulate inspiration during the ideation phase.
Methods in the design process. The most relevant phases of each method are highlighted.
Conclusion
The project demonstrates that data science assists service design in multiple ways:
- Data science can make hidden information accessible to designers with specialised user research tools.
- Data science can help designers in their creative process through relevant resources, inspiration and an alternative perspective.
- Process mining can support designers with understanding and testing models of processes, such as the customer journey.
- Data science can increase the validity of user research with method triangulation.
Deliverables
This project was part of the thesis that was submitted in fulfilment of the requirements for the degree of Master of Science in Industrial Design Engineering at the University of Twente, December 2019. Furthermore, the research resulted in a conference paper, which was presented at the Design Research Society (DRS) Conference in 2020.
References
- Cao, L. (2017). Data science: a comprehensive overview. ACM Computing Surveys (CSUR), 50(3), 1-42. (Cao, 2017)
- Costa, N., Patrício, L., & Morelli, N. (2018). A designerly-way of conducting qualitative research in design studies. In Servdes2018. service design proof of concept, proceedings of the servdes. 2018 conference, 18-20 june, milano, italy (pp. 164– 176). Linköping University Electronic Press, Linköpings universitet. (Costa, Patrício & Morelli, 2018)
- Hui, S. C., & Jha, G. (2000). Data mining for customer service support. Information & Management, 38(1), 1-13. (Hui & Jha, 2000)
- Patrício, L., Gustafsson, A., & Fisk, R. (2018). Upframing service design and innovation for research impact. Journal of Service Research, 21(1), 3–16. (Patrício et al., 2018)
- Stickdorn, M., Schneider, J., Andrews, K., & Lawrence, A. (2011). This is service design thinking: basics, tools, cases. Wiley Hoboken, NJ. (Stickdorn et al., 2011)
- van der Aalst, W. (2014). Data scientist: The engineer of the future. In Enterprise interoperability vi (Vol. 7, pp. 13–26). Cham: Springer International Publishing. (van der Aalst, 2014)
- Zomerdijk, L. G., & Voss, C. A. (2010). Service design for experience- centric services. Journal of Service Research, 13(1), 67–82. (Zomerdijk & Voss, 2010)
- Nylind, L. (2014). Archie bland tries out a gopro camera. Retrieved from www.theguardian.com (visited on February 2019)
