Implementation of clustering technique for analyzing consumer buying behavior during the COVID-19 pandemic: a case in the beauty industry

Authors

DOI:

https://doi.org/10.15587/1729-4061.2023.274299

Keywords:

COVID-19 pandemic, consumer behavior, product preference, beauty industry, clustering technique

Abstract

The global demand for beauty products continues to grow due to raised public awareness of applying cosmetics, with a 1.45 % to 3.34 % growth annually. Unfortunately, the COVID-19 outbreak broke out globally in December 2019, affecting face-to-face businesses such as the beauty industry falling until –7.11 % in 2020. This study aims to analyze the impact of the COVID-19 outbreak on Indonesia’s beauty industry and the shift in the beauty consumer segment during the pandemic.

This study adopts the react-cope-adapt (RCA) framework to construct the COVID-19 pandemic periodization in Indonesia. The correlation analysis was used to investigate the impact of the COVID-19 pandemic on the beauty industry. In addition, clustering techniques were employed to identify hidden consumer segments and product preferences throughout the COVID-19 outbreak.

The study shows that COVID-19 cases positively impact beauty company’s sales during the reacting phase. A strong negative relationship between COVID-19 and company revenue was observed in the coping phase. In the adapt phase, the negative impact of COVID-19 on the company’s sales has decreased. Our finding also confirms the shift in consumer buying behavior during the pandemic. Consumers prefer to buy cosmetics products online than offline during the reaction phase. In the coping phase, consumers slowly begin to purchase in-store. Finally, consumers return to buying cosmetics offline in the adapting phase, similar to before the pandemic. The clustering results showed three hidden consumer segments: the loyal consumer segment, the impulsive consumer segment, and the compulsive consumer segment. In addition, during the pandemic, consumers prefer to buy skincare products over make-up products since government policies forced people to stay, work, and study at home.

Our study has theoretical and practical implications. Theoretically, our results support the usefulness of the RCA model and clustering techniques in analyzing the change in consumer buying behavior during a time of crisis, such COVID-19 pandemic. Practically, beauty industries can anticipate this shift by accelerating the digital business transformation and focusing on the most preferred product to sustain their business

Author Biographies

Beny Maulana Achsan, University of Indonesia

Master Student of Information Technology

Faculty of Computer Science

Achmad Nizar Hidayanto, University of Indonesia

Professor of Information Systems, Vice Dean for Resources, Venture, and General Administration

Faculty of Computer Science

References

  1. Beauty & Personal Care - Worldwide (2022). Statista. Available at: https://www.statista.com/outlook/cmo/beauty-personal-care/worldwide
  2. Mega farisha, Hartoyo, Safari, A. (2022). Does Covid-19 Pandemic Change the Consumer Purchase Behavior Towards Cosmetic Products? Journal of Consumer Sciences, 7 (1), 1–19. doi: https://doi.org/10.29244/jcs.7.1.1-19
  3. Kim, M., Kwon, K. H. (2022). Significant paradigm of beauty ecosystem after COVID ‐19 pandemic in Republic of Korea. Journal of Cosmetic Dermatology, 21 (10), 4114–4121. doi: https://doi.org/10.1111/jocd.15192
  4. Arora, N. et al. (2020). A global view of how consumer behavior is changing amid COVID-19. McKinsey & Company. Available at: https://www.mckinsey.com/~/media/McKinsey/Business%20Functions/Marketing%20and%20Sales/Our%20Insights/A%20global%20view%20of%20how%20consumer%20behavior%20is%20changing%20amid%20COVID%2019/20200707/covid-19-global-consumer-sentiment-20200707.pdf
  5. Choi, Y.-H., Kim, S. E., Lee, K.-H. (2022). Changes in consumers’ awareness and interest in cosmetic products during the pandemic. Fashion and Textiles, 9 (1). doi: https://doi.org/10.1186/s40691-021-00271-8
  6. Jílková, P., Králová, P. (2021). Digital Consumer Behaviour and eCommerce Trends during the COVID-19 Crisis. International Advances in Economic Research, 27 (1), 83–85. doi: https://doi.org/10.1007/s11294-021-09817-4
  7. Shaw, N., Eschenbrenner, B., Baier, D. (2022). Online shopping continuance after COVID-19: A comparison of Canada, Germany and the United States. Journal of Retailing and Consumer Services, 69, 103100. doi: https://doi.org/10.1016/j.jretconser.2022.103100
  8. Agarwal, R., Gopinath, G., Farrar, J., Hatchett, R., Sands, P. (2022). A Global Strategy to Manage the Long-Term Risks of COVID-19. IMF Working Papers, 2022 (068), 1. doi: https://doi.org/10.5089/9798400205996.001
  9. Alsaydia, O. M., Saadallah, N. R., Malallah, F. L., AL-Adwany, M. A. S. (2021). Limiting COVID-19 infection by automatic remote face mask monitoring and detection using deep learning with IoT. Eastern-European Journal of Enterprise Technologies, 5 (2 (113)), 29–36. doi: https://doi.org/10.15587/1729-4061.2021.238359
  10. Wibowo, A., Chen, S.-C., Wiangin, U., Ma, Y., Ruangkanjanases, A. (2020). Customer Behavior as an Outcome of Social Media Marketing: The Role of Social Media Marketing Activity and Customer Experience. Sustainability, 13 (1), 189. doi: https://doi.org/10.3390/su13010189
  11. Tanveer, T., Kazmi, S. Q., Rahman, M. U. (2022). Determinants of Impulsive Buying Behavior: An Empirical Analysis of Consumers’ Purchase Intentions for Offline Beauty Products. Nurture, 16 (2), 75–89. doi: https://doi.org/10.55951/nurture.v16i2.129
  12. Jo, H., Shin, E., Kim, H. (2020). Changes in Consumer Behaviour in the Post-COVID-19 Era in Seoul, South Korea. Sustainability, 13 (1), 136. doi: https://doi.org/10.3390/su13010136
  13. Adibfar, A., Gulhare, S., Srinivasan, S., Costin, A. (2022). Analysis and modeling of changes in online shopping behavior due to Covid-19 pandemic: A Florida case study. Transport Policy, 126, 162–176. doi: https://doi.org/10.1016/j.tranpol.2022.07.003
  14. Kirk, C. P., Rifkin, L. S. (2020). I’ll trade you diamonds for toilet paper: Consumer reacting, coping and adapting behaviors in the COVID-19 pandemic. Journal of Business Research, 117, 124–131. doi: https://doi.org/10.1016/j.jbusres.2020.05.028
  15. Guthrie, C., Fosso-Wamba, S., Arnaud, J. B. (2021). Online consumer resilience during a pandemic: An exploratory study of e-commerce behavior before, during and after a COVID-19 lockdown. Journal of Retailing and Consumer Services, 61, 102570. doi: https://doi.org/10.1016/j.jretconser.2021.102570
  16. Anitha, P., Patil, M. M. (2022). RFM model for customer purchase behavior using K-Means algorithm. Journal of King Saud University - Computer and Information Sciences, 34 (5), 1785–1792. doi: https://doi.org/10.1016/j.jksuci.2019.12.011
  17. Tabianan, K., Velu, S., Ravi, V. (2022). K-Means Clustering Approach for Intelligent Customer Segmentation Using Customer Purchase Behavior Data. Sustainability, 14 (12), 7243. doi: https://doi.org/10.3390/su14127243
  18. Zhang, L., Priestley, J., DeMaio, J., Ni, S., Tian, X. (2021). Measuring Customer Similarity and Identifying Cross-Selling Products by Community Detection. Big Data, 9 (2), 132–143. doi: https://doi.org/10.1089/big.2020.0044
  19. Taghikhah, F., Voinov, A., Shukla, N., Filatova, T. (2021). Shifts in consumer behavior towards organic products: Theory-driven data analytics. Journal of Retailing and Consumer Services, 61, 102516. doi: https://doi.org/10.1016/j.jretconser.2021.102516
  20. Abbasimehr, H., Bahrini, A. (2022). An analytical framework based on the recency, frequency, and monetary model and time series clustering techniques for dynamic segmentation. Expert Systems with Applications, 192, 116373. doi: https://doi.org/10.1016/j.eswa.2021.116373
  21. Li, Z., Gao, X., Lu, D. (2021). Correlation analysis and statistical assessment of early hydration characteristics and compressive strength for multi-composite cement paste. Construction and Building Materials, 310, 125260. doi: https://doi.org/10.1016/j.conbuildmat.2021.125260
  22. Miloudi, S., Wang, Y., Ding, W. (2021). A Gradient-Based Clustering for Multi-Database Mining. IEEE Access, 9, 11144–11172. doi: https://doi.org/10.1109/access.2021.3050404
  23. Sinaga, K. P., Yang, M.-S. (2020). Unsupervised K-Means Clustering Algorithm. IEEE Access, 8, 80716–80727. doi: https://doi.org/10.1109/access.2020.2988796
  24. Arunachalam, D., Kumar, N. (2018). Benefit-based consumer segmentation and performance evaluation of clustering approaches: An evidence of data-driven decision-making. Expert Systems with Applications, 111, 11–34. doi: https://doi.org/10.1016/j.eswa.2018.03.007
  25. Ikotun, A. M., Ezugwu, A. E., Abualigah, L., Abuhaija, B., Heming, J. (2023). K-means clustering algorithms: A comprehensive review, variants analysis, and advances in the era of big data. Information Sciences, 622, 178–210. doi: https://doi.org/10.1016/j.ins.2022.11.139
  26. Batool, F., Hennig, C. (2021). Clustering with the Average Silhouette Width. Computational Statistics & Data Analysis, 158, 107190. doi: https://doi.org/10.1016/j.csda.2021.107190
  27. Archived: WHO Timeline - COVID-19 (2020). WHO. Available at: https://www.who.int/news/item/27-04-2020-who-timeline---covid-19
  28. Clear and free: Wuhan evacuees head home from Natuna quarantine (2020). The Jakarta Post. Available at: https://www.thejakartapost.com/news/2020/02/16/clear-and-free-wuhan-evacuees-head-home-from-natuna-quarantine.html
  29. BREAKING: Jokowi announces Indonesia’s first two confirmed COVID-19 cases (2020). The Jakarta Post. Available at: https://www.thejakartapost.com/news/2020/03/02/breaking-jokowi-announces-indonesias-first-two-confirmed-covid-19-cases.html
  30. Presiden Tetapkan COVID-19 Sebagai Bencana Nasional (2020). BNPB. Available at: https://bnpb.go.id/berita/presiden-tetapkan-covid19-sebagai-bencana-nasional#:~:text=JAKARTA%20%2D%20Presiden%20Joko%20Widodo%20secara,%2D19)%20Sebagai%20Bencana%20Nasional
  31. Jokowi declares COVID-19 ‘national disaster’, gives task force broader authority (2020). The Jakarta Post. Available at: https://www.thejakartapost.com/news/2020/04/14/jokowi-declares-covid-19-national-disaster-gives-task-force-broader-authority.html
  32. COVID-19 Brief Information (2020). Ministry of Foreign Affairs of the Republic of Indonesia. Available at: https://kemlu.go.id/download/L3NpdGVzL3B1c2F0L0RvY3VtZW50cy9DT1ZJRC0xOS9CcmllZiUyMEluZm9ybWF0aW9uJTIwQ292aWQtMTklMjBPdXRicmVhayUyMEFzJTIwT2YlMjAyMDAzMjYucGRm
  33. President Jokowi gets first coronavirus jab (2020). The Jakarta Post. Available at: https://www.thejakartapost.com/news/2021/01/13/jokowi-gets-first-coronavirus-jab.html
  34. Indonesia fights back the covid-19 second wave (2021). Available at: https://covid19.go.id/p/berita/indonesia-fights-back-covid-19-second-wave#
  35. US CDC Lists Indonesia at Level 1. Cabinet Secretariat of The Republic of Indonesia. Available at: https://setkab.go.id/en/us-cdc-lists-indonesia-at-level-1/
  36. Cakupan Vaksinasi di Indonesia Lampaui 200 Juta Dosis, Penuhi Target WHO (2021). Available at: https://covid19.go.id/edukasi/masyarakat-umum/cakupan-vaksinasi-di-indonesia-lampaui-200-juta-dosis-penuhi-target-who
  37. Jokowi Izinkan Warga Lepas Masker, Ini Penjelasan Lengkapnya (2022). CNBC Indonesia. Available at: https://www.cnbcindonesia.com/news/20220517171322-4-339601/jokowi-izinkan-warga-lepas-masker-ini-penjelasan-lengkapnya
  38. Chi, N. T. K. (2021). Innovation capability: The impact of e-CRM and COVID-19 risk perception. Technology in Society, 67, 101725. doi: https://doi.org/10.1016/j.techsoc.2021.101725
Implementation of clustering technique for analyzing consumer buying behavior during the COVID-19 pandemic: a case in the beauty industry

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Published

2023-06-30

How to Cite

Achsan, B. M., & Hidayanto, A. N. (2023). Implementation of clustering technique for analyzing consumer buying behavior during the COVID-19 pandemic: a case in the beauty industry. Eastern-European Journal of Enterprise Technologies, 3(2 (123), 14–25. https://doi.org/10.15587/1729-4061.2023.274299