Extracting Data-Driven User Segments and Knowledge by Using Online Product Reviews

Year 2023, Volume: 11 Issue: 1, 139 - 152, 29.03.2023

Serkan Güneş

Abstract

With the growth of e-commerce, consumer reviews are becoming more widely available and influential. These valuable online product reviews (OPR) show any product issues and contain unique and hidden user information fragments that designers can use in decision-making. OPRs are often unstructured, massive, disorganized, and highly detailed. OPRs are voluntary production and are available in large numbers, publicly available, and accessible. These features increase the number of samples and save money and time for designers to understand the user. The analysis of OPRs is done with AI-supported text analysis tools, especially if many reviews are to get through. In this study, user demographics and opinions about the product are extracted through text mining and statistical methods through the OPRs of a sample product. The data analysis results provided valuable information about the users and had the potential to develop new knowledge and generate new ideas for the design process. By arguing for the merit of adding Big Data analysis to the design process, first, valuable user information content contained in OPRs has been revealed. Secondly, it was possible to express user stacks as clusters with similar characteristics. Finally, it has been revealed that demographic user clusters become homogenized after the product experience, and the initially disjointed clusters begin to resemble independently from the demographic clusters due to independent product/aspect evaluations.

Keywords

Big Data, Decision making, Design knowledge, Industrial design, User behavior

Thanks

The author wishes to thank to Gazi University Design Application and Research Center for their technical support.

References

• [1] Almaliki, M., Ncube, C., Ali, R. (2015). Adaptive Software-Based Feedback Acquisition: A Persona-Based Design. In 2015 IEEE 9th International Conference on Research Challenges in Information Science (RCIS), 100-111. Doi:10.1109/rcis.2015.7128868
• [2] Boush, D., Kahle, L. (2001). Evaluating Negative Information in Online Consumer Discussions: From Qualitative Analysis to Signal Detection. Journal of Euro-marketing 11(2): 89-105.
• [3] Cooper, R. (2011). Users, Users, Users and the Use of Design. The Design Journal 14(4): 387-389.
• [4] Güneş, S. (2020). Extracting Online Product Review Patterns and Causes: A New Aspect/Cause Based Heuristic for Designers. The Design Journal 23(2): 375-393. Doi:doi.org/10.1080/14606925.2020.1746611
• [5] Margolin, V. (1997). Getting to Know the User. Design Studies 18(3): 227-236. Doi:10.1016/S0142-694X(97)00001-X
• [6] Miaskiewicz, T., Kozarb, K. (2011). Personas and User-Centered Design: How Can Personas Benefit Product Design Processes? Design Studies 32(5): 417-430. Doi:https://doi.org/10.1016/j.destud.2011.03.003
• [7] Norman, D. (2018). Ad-Hoc Personas and Empathetic Focus. https://jnd.org/ad-hoc_personas_empathetic_focus/ . Last Accessed: 20.08.2022
• [8] Oygür, I. (2018). The Machineries of User Knowledge Production. Design Studies (54): 23-49. Doi:https://doi.org/10.1016/j.destud.2017.10.002
• [9] Park, D., Lee, J., Han, I. (2007). The Effect of On-Line Consumer Reviews on Consumer Purchasing Intention: The Moderating Role of Involvement. International Journal of Electronic Commerce 11(4): 125-148. Doi:10.2753/JEC1086-4415110405
• [10] Pruitt, J., Adlin, T. (2006). Persona Lifecycle: Keeping People in Mind Throughout Product Design. Burlington: Morgan Kaufmann.
• [11] Ramachandran, G. (2017). Impact of Demographic Variables on the Use Patterns of Electronic Information Resources Among Aerospace Scientists and Engineers of Bangalore. International Journal on Environmental Sciences 8(1): 90–104.
• [12] Rogers, E. (2003). Diffusion of Innovations. New York: Free Press.
• [13] Turner, P., Turner, S. (2011). Is Stereotyping Inevitable When Designing with Personas? Design Studies 32(1): 30-44. Doi:https://doi.org/10.1016/j.destud.2010.06.002
• [14] Zhan, J., Loh, H., Liu, Y. (2009). Gather Customer Concerns from Online Product Reviews – A Text Summarization Approach. Expert Systems with Applications 36(2): 2107–2115. Doi:10.1016/j.eswa.2007.12.039