Customer segmentation analysis using DBSCAN method in marketing research of retail company
DOI:
https://doi.org/10.35335/cit.Vol16.2024.906.pp321-328Keywords:
Customer Segmentation, DBSCAN, Marketing, Data Analytics, Machine LearningAbstract
Customer segmentation is an important aspect of an effective marketing strategy, yet many traditional methods are unable to capture the complexity of diverse customer behaviors. This research aims to apply the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) method for customer segmentation in retail companies, focusing on identifying patterns of purchasing behavior and product preferences. Data was collected through a questionnaire distributed to 500 respondents, then analyzed using the DBSCAN method. The results showed that DBSCAN successfully identified several customer segments with unique characteristics, and provided an average Silhouette Score of 0.67 and Davies-Bouldin Index of 0.45, indicating good cluster quality. The findings imply that a density-based approach can improve a company's understanding of customer dynamics, and enable the development of more targeted and effective marketing strategies. This research makes an important contribution to the marketing literature, while opening up opportunities for further exploration of the use of machine learning methods in customer segmentation.
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V. Sima, I. G. Gheorghe, J. Subi?, and D. Nancu, “Influences of the Industry 4.0 Revolution on the Human Capital Development and Consumer Behavior: A Systematic Review,” Sustainability, vol. 12, no. 10. 2020. doi: 10.3390/su12104035.
R. N. Bolton et al., “Customer experience challenges: bringing together digital, physical and social realms,” J. Serv. Manag., vol. 29, no. 5, pp. 776–808, Jan. 2018, doi: 10.1108/JOSM-04-2018-0113.
M. M. Mariani and S. Fosso Wamba, “Exploring how consumer goods companies innovate in the digital age: The role of big data analytics companies,” J. Bus. Res., vol. 121, pp. 338–352, 2020, doi: https://doi.org/10.1016/j.jbusres.2020.09.012.
D. Arunachalam and N. Kumar, “Benefit-based consumer segmentation and performance evaluation of clustering approaches: An evidence of data-driven decision-making,” Expert Syst. Appl., vol. 111, pp. 11–34, 2018, doi: https://doi.org/10.1016/j.eswa.2018.03.007.
D. Jaiswal, V. Kaushal, P. K. Singh, and A. Biswas, “Green market segmentation and consumer profiling: a cluster approach to an emerging consumer market,” Benchmarking An Int. J., vol. 28, no. 3, pp. 792–812, Jan. 2021, doi: 10.1108/BIJ-05-2020-0247.
J. Oyelade et al., “Data Clustering: Algorithms and Its Applications,” in 2019 19th International Conference on Computational Science and Its Applications (ICCSA), 2019, pp. 71–81. doi: 10.1109/ICCSA.2019.000-1.
M. Franco and J.-M. Vivo, “Cluster Analysis of Microarray Data BT - Microarray Bioinformatics,” V. Bolón-Canedo and A. Alonso-Betanzos, Eds. New York, NY: Springer New York, 2019, pp. 153–183. doi: 10.1007/978-1-4939-9442-7_7.
M. Zhang, “Use Density-Based Spatial Clustering of Applications with Noise (DBSCAN) Algorithm to Identify Galaxy Cluster Members,” IOP Conf. Ser. Earth Environ. Sci., vol. 252, no. 4, p. 42033, 2019, doi: 10.1088/1755-1315/252/4/042033.
D. Deng, “DBSCAN Clustering Algorithm Based on Density,” in 2020 7th International Forum on Electrical Engineering and Automation (IFEEA), 2020, pp. 949–953. doi: 10.1109/IFEEA51475.2020.00199.
N. Hanafi and H. Saadatfar, “A fast DBSCAN algorithm for big data based on efficient density calculation,” Expert Syst. Appl., vol. 203, p. 117501, 2022, doi: https://doi.org/10.1016/j.eswa.2022.117501.
R. Varadarajan, “Customer information resources advantage, marketing strategy and business performance: A market resources based view,” Ind. Mark. Manag., vol. 89, pp. 89–97, 2020.
S. Dolnicar, “Market segmentation for e-tourism,” in Handbook of e-Tourism, Springer, 2022, pp. 849–863.
S. Arefin et al., “Retail Industry Analytics: Unraveling Consumer Behavior through RFM Segmentation and Machine Learning,” in 2024 IEEE International Conference on Electro Information Technology (eIT), 2024, pp. 545–551. doi: 10.1109/eIT60633.2024.10609927.
S. Zeng, T. Wang, W. Lin, Z. Chen, and R. Xiao, “A Patent Mining Approach to Accurately Identifying Innovative Industrial Clusters Based on the Multivariate DBSCAN Algorithm,” Systems, vol. 12, no. 9. 2024. doi: 10.3390/systems12090321.
T. Fan, N. Guo, and Y. Ren, “Consumer clusters detection with geo-tagged social network data using DBSCAN algorithm: a case study of the Pearl River Delta in China,” GeoJournal, vol. 86, no. 1, pp. 317–337, 2021, doi: 10.1007/s10708-019-10072-8.
D. Arunachalam, N. Kumar, and J. P. Kawalek, “Understanding big data analytics capabilities in supply chain management: Unravelling the issues, challenges and implications for practice,” Transp. Res. Part E Logist. Transp. Rev., vol. 114, pp. 416–436, 2018, doi: https://doi.org/10.1016/j.tre.2017.04.001.
T. Choi, S. W. Wallace, and Y. Wang, “Big Data Analytics in Operations Management,” Prod. Oper. Manag., vol. 27, no. 10, pp. 1868–1883, Oct. 2018, doi: 10.1111/poms.12838.
P. Mikalef, M. Boura, G. Lekakos, and J. Krogstie, “Big data analytics and firm performance: Findings from a mixed-method approach,” J. Bus. Res., vol. 98, pp. 261–276, 2019, doi: https://doi.org/10.1016/j.jbusres.2019.01.044.
M. Chaudhry, I. Shafi, M. Mahnoor, D. L. Vargas, E. B. Thompson, and I. Ashraf, “A Systematic Literature Review on Identifying Patterns Using Unsupervised Clustering Algorithms: A Data Mining Perspective,” Symmetry, vol. 15, no. 9. 2023. doi: 10.3390/sym15091679.
S. Pitafi, T. Anwar, and Z. Sharif, “A Taxonomy of Machine Learning Clustering Algorithms, Challenges, and Future Realms,” Applied Sciences, vol. 13, no. 6. 2023. doi: 10.3390/app13063529.
P. Govender and V. Sivakumar, “Application of k-means and hierarchical clustering techniques for analysis of air pollution: A review (1980–2019),” Atmos. Pollut. Res., vol. 11, no. 1, pp. 40–56, 2020.
Mahnoor et al., “A Review of Approaches for Rapid Data Clustering: Challenges, Opportunities, and Future Directions,” IEEE Access, vol. 12, pp. 138086–138120, 2024, doi: 10.1109/ACCESS.2024.3461798.
A. M. Ikotun, A. E. Ezugwu, L. Abualigah, B. Abuhaija, and J. Heming, “K-means clustering algorithms: A comprehensive review, variants analysis, and advances in the era of big data,” Inf. Sci. (Ny)., vol. 622, pp. 178–210, 2023.
A. M. Ikotun, M. S. Almutari, and A. E. Ezugwu, “K-means-based nature-inspired metaheuristic algorithms for automatic data clustering problems: Recent advances and future directions,” Appl. Sci., vol. 11, no. 23, p. 11246, 2021.
S. Wang, L. Sun, and Y. Yu, “A dynamic customer segmentation approach by combining LRFMS and multivariate time series clustering,” Sci. Rep., vol. 14, no. 1, p. 17491, 2024.
N. A. Morgan, K. A. Whitler, H. Feng, and S. Chari, “Research in marketing strategy,” J. Acad. Mark. Sci., vol. 47, pp. 4–29, 2019.
F. Li, J. Larimo, and L. C. Leonidou, “Social media marketing strategy: definition, conceptualization, taxonomy, validation, and future agenda,” J. Acad. Mark. Sci., vol. 49, pp. 51–70, 2021.
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