Abstract
Dividing up a company’s clientele into distinct groups is one of the most critical components of its decision-making support system. It is an essential piece of marketing tool that enables the targeting of particular client groups through the application of specialized marketing strategies. In most instances, clustering approaches serve as the foundation for client segmentation. This study’s findings have the potential to improve marketing techniques as well as product design. It can be used to improve customer satisfaction, encourage customer participation, enhance the effectiveness of marketing campaigns by participating in targeted marketing activities, and do away with transaction fees associated with merchant agreements that do not significantly influence consumer preferences. A company must identify potential customers in a highly competitive market on time. This will make it possible for the organization to increase its customer base gradually. This research presents an innovative approach to customer segmentation using spectral clustering and clustering ensemble. The possibility exists for our model to evolve into required items for boosting the general clustering solutions quality. It combines the results from several different clustering methods based on spectral clustering. These methods include Mini Batch K-means, K-means, and MeanShif.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ahani, A., et al. (2019). Revealing customers’ satisfaction and preferences through online review analysis: The case of Canary Islands hotels. Journal of Retailing and Consumer Services, 51, 331‑343. https://doi.org/10.1016/j.jretconser.2019.06.014
Carnein, M., & Trautmann, H. (2019). Customer segmentation based on transactional data using stream clustering. In Q. Yang, Z.-H. Zhou, Z. Gong, M.-L. Zhang, & S.-J. Huang (Eds.), Advances in knowledge discovery and data mining (vol. 11439, pp. 280–292). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-16148-4_22
Chacón, J. E., & Rastrojo, A. I. (2020). Minimum adjusted Rand index for two clusterings of a given size. arXiv, 9 décembre 2020. Consulté le: 15 juin 2022. [En ligne]. Disponible sur: http://arxiv.org/abs/2002.03677
Chavan, M. M., Patil, A., Dalvi, L., & Patil, A. (2015). Mini batch K-means clustering on large dataset. International Journal of Engineering Sciences & Research, 4(07), 1356–1358.
Duong, T., Beck, G., Azzag, H., & Lebbah, M. (2016). Nearest neighbour estimators of density derivatives, with application to mean shift clustering. Pattern Recognition Letters, 80, 224–230. https://doi.org/10.1016/j.patrec.2016.06.021
Dwivedi, Y. K., et al. (2021). Setting the future of digital and social media marketing research: Perspectives and research propositions. International Journal of Information Management, 59, 102168. https://doi.org/10.1016/j.ijinfomgt.2020.102168
Ganatra, V., et al. (2021). A study of subway marketing 4P strategy. Asia Pacific Journal of Management and Education (APJME), 4(2), 47–64. https://doi.org/10.32535/apjme.v4i2.1069
Griva, A. (2022). “I can get no e-satisfaction”. What analytics say? Evidence using satisfaction data from e-commerce. Journal of Retailing and Consumer Services, 66, 102954. ISSN 0969–6989. https://doi.org/10.1016/j.jretconser.2022.102954
Hicks, S. C., Liu, R., Ni, Y., Purdom, E., & Risso, D. (2021). mbkmeans: fast clustering for single cell data using mini-batch k-means. PLoS Computational Biology, 17(1), e1008625.
Hidayati, R., Zubair, A., Pratama, A. H., & Indana, L. (2021). Analisis silhouette coefficient pada 6 perhitungan jarak K-means clustering. Techno.Com, 20(2), 186‑197. https://doi.org/10.33633/tc.v20i2.4556
Iglesias, O., Markovic, S., & Rialp, J. (2019). How does sensory brand experience influence brand equity? Considering the roles of customer satisfaction, customer affective commitment, and employee empathy. Journal of Business Research, 96, 343–354. https://doi.org/10.1016/j.jbusres.2018.05.043
Jin, H. (2022). The effect of overspending on tariff choices and customer churn: Evidence from mobile plan choices. Journal of Retailing and Consumer Services, 66, 102914. https://doi.org/10.1016/j.jretconser.2022.102914
Kaur, B., & Sharma, P. K. (2019). Implementation of customer segmentation using integrated approach. International Journal of Innovative Technology and Exploring Engineering (IJITEE), 8(6), 3.
Lin, Y. H. (2015). Innovative brand experience’s influence on brand equity and brand satisfaction. Journal of Business Research, 68(11), 2254–2259. https://doi.org/10.1016/j.jbusres.2015.06.007
Lin, Z., & Vlachos, I. (2018). An advanced analytical framework for improving customer satisfaction: A case of air passengers. Transportation Research Part E: Logistics and Transportation Review, 114, 185–195. https://doi.org/10.1016/j.tre.2018.04.003
Nguyen, S. P. (2021). Deep customer segmentation with applications to a Vietnamese supermarkets’ data. Soft Computing, 25(12), 7785–7793. https://doi.org/10.1007/s00500-021-05796-0
Nisar, T. M., & Prabhakar, G. (2017). What factors determine e-satisfaction and consumer spending in e-commerce retailing? Journal of Retailing and Consumer Services, 39, 135–144. https://doi.org/10.1016/j.jretconser.2017.07.010
Pramono, P. P., Surjandari, I., & Laoh, E. (2019). Estimating customer segmentation based on customer lifetime value using two-stage clustering method. In 2019 16th International Conference on Service Systems and Service Management (ICSSSM) (pp. 1–5). Shenzhen, China. https://doi.org/10.1109/ICSSSM.2019.8887704
Qiu, Y., Chen, P., Lin, Z., Yang, Y., Zeng, L., Fan, Y. (2020). Clustering analysis for silent telecom customers based on Kmeans++. In 2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC).
Řezanková, H. (2018). Different approaches to the silhouette coefficient calculation in cluster evaluation. In 21st International Scientific Conference AMSE Applications of Mathematics and Statistics in Economics (p. 10).
Rita, P., Oliveira, T., & Farisa, A. (2019). The impact of e-service quality and customer satisfaction on customer behavior in online shopping. Heliyon, 5(10), e02690. https://doi.org/10.1016/j.heliyon.2019.e02690
Santos, J. M., & Embrechts, M. (2009). On the use of the adjusted rand index as a metric for evaluating supervised classification. In C. Alippi, M. Polycarpou, C. Panayiotou, & G. Ellinas (Eds.), Artificial neural networks—ICANN 2009 (vol. 5769, pp. 175–184). Springer. https://doi.org/10.1007/978-3-642-04277-5_18
School of Management, Hospitality and Tourism (ESGHT), Universidade do Algarve, Portugal et al. (2022). Hotel customer segmentation and sentiment analysis through online reviews: An analysis of selected European markets. Tourism & Management Studies, 18(1), 29–40. https://doi.org/10.18089/tms.2022.180103
Su, Q., Zhu, Y., Jia, Y., Li, P., Hu, F., & Xu, X. (2018). Sedimentary environment analysis by grain-size data based on mini batch K-means algorithm. Geofluids, 2018, 1–11. https://doi.org/10.1155/2018/8519695
Vega-Pons, S., & Ruiz-Shulcloper, J. (2011). A survey of clustering ensemble algorithms. International Journal of Pattern Recognition and Artificial Intelligence, 25(03), 337–372. https://doi.org/10.1142/S0218001411008683
Vega-Pons, S., Correa-Morris, J., & Ruiz-Shulcloper, J. (2010). Weighted partition consensus via kernels. Pattern Recognition, 43(8), 2712–2724. https://doi.org/10.1016/j.patcog.2010.03.001
Wu, C.-L., & Ma, N. K. (2022). The impact of customised mobile marketing on passenger shopping behaviour in the airport terminal. Journal of Retailing and Consumer Services, 66, 102941. https://doi.org/10.1016/j.jretconser.2022.102941
Yuan, C., & Yang, H. (2019). Research on K-value selection method of K-means clustering algorithm. J, 2(2), 226–235. https://doi.org/10.3390/j2020016
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Hicham, N., Karim, S., Habbat, N. (2024). Novel Approach and Innovative Strategy for Mall Customer Segmentation Using Machine Learning Techniques. In: El Bhiri, B., Saidi, R., Essaaidi, M., Kaabouch, N. (eds) Smart Mobility and Industrial Technologies. ICATH 2022. Advances in Science, Technology & Innovation. Springer, Cham. https://doi.org/10.1007/978-3-031-46849-0_5
Download citation
DOI: https://doi.org/10.1007/978-3-031-46849-0_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-46848-3
Online ISBN: 978-3-031-46849-0
eBook Packages: Earth and Environmental ScienceEarth and Environmental Science (R0)