Application Of K-Means Clustering For Customer Segmentation In Grocery Stores In Kenya
Abstract
The retail industry, particularly in the context of grocery stores, plays a vital role in meeting consumers' daily
needs. To optimize marketing strategies and enhance customer satisfaction, understanding customer behavior and
preferences is crucial. Customer segmentation, a powerful market research technique, enables businesses to group
customers with shared characteristics into distinct segments, allowing targeted and personalized approaches. This
article explores the application of the K-means clustering algorithm for customer segmentation in grocery stores
within the unique context of Kenya. By leveraging transactional and demographic data from diverse grocery stores
across Kenya, the study aims to identify homogeneous customer groups with similar purchasing behaviors and
preferences. The data collection process involved obtaining consent from store owners and ensuring data privacy
and security. Following data preprocessing, K-means clustering was applied, and various validation techniques
were utilized to determine the optimal number of clusters. The results yielded valuable insights into customer
segments, aiding the identification of key customer groups and their distinct preferences.
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References
Omol, Edwin, Lucy Mburu, and Paul Abuonji. "Digital Maturity Action Fields for SMES in Developing
Economies." Journal of Environmental Science, Computer Science, and Engineering & Technology, 12(3),
https://doi.org/10.24214/jecet.B.12.3.10114. (2023)
Makara, Isabel. A Clustering Approach to Market Segmentation Using Integrated Business Data. Diss.
University of Nairobi, 2021.
Makana, Asha P. Customer Segmentation on Mobile Money Users in Kenya. Diss. University of Nairobi, 2020.
Maneno, Khamis Mwero, Richard Rimiru, and Calvins Otieno. "Segmentation via principal component analysis
for perceptron classification: a case study of Kenyan mobile subscribers." Proceedings of the 2nd International
Conference on Intelligent and Innovative Computing Applications. 2020.
Kansal, Tushar, et al. "Customer segmentation using K-means clustering." 2018 international conference on
computational techniques, electronics and mechanical systems (CTEMS). IEEE, 2018.
Shirole, Rahul, Laxmiputra Salokhe, and Saraswati Jadhav. "Customer segmentation using rfm model and kmeans clustering." Int. J. Sci. Res. Sci. Technol 8 (2021): 591-597.
Tabianan, Kayalvily, Shubashini Velu, and Vinayakumar Ravi. "K-means clustering approach for intelligent
customer segmentation using customer purchase behavior data." Sustainability 14.12 (2022): 7243.
Ong, Ardvin Kester S., et al. "Consumer preference analysis on the attributes of samgyeopsal Korean cuisine and
its market segmentation: Integrating conjoint analysis and K-means clustering." Plos One 18.2 (2023):
e0281948.
La Cruz, Alexandra, et al. "Users Segmentation Based on Google Analytics Income Using KMeans." Information and Communication Technologies: 9th Conference of Ecuador, TICEC 2021, Guayaquil,
Ecuador, November 24–26, 2021, Proceedings 9. Springer International Publishing, 2021.
Pradana, Musthofa Galih, and Hoang Thi Ha. "Maximizing strategy improvement in mall customer segmentation
using k-means clustering." Journal of Applied Data Sciences 2.1 (2021): 19-25.
Li, Yue, et al. "Customer segmentation using K-means clustering and the adaptive particle swarm optimization
algorithm." Applied Soft Computing 113 (2021): 107924.
Tabianan, Kayalvily, Shubashini Velu, and Vinayakumar Ravi. "K-means clustering approach for intelligent
customer segmentation using customer purchase behavior data." Sustainability 14.12 (2022): 7243.
Kennedy, Ryan, et al. "Multilevel customer segmentation for off-grid solar in developing countries: Evidence
from solar home systems in Rwanda and Kenya." Energy 186 (2019): 115728.
Matute, Roberto, and Juan Estrada. "Users Segmentation Based on Google Analytics Income Using KMeans." Information and Communication Technologies: 9th Conference of Ecuador, TICEC 2021, Guayaquil,
Ecuador, November 24–26, 2021, Proceedings. Springer Nature, 2021.
Khan, Riyo Hayat. LRFS: online shoppers’ behavior based efficient customer segmentation model. Diss. Brac
University, 2023.
Salamzadeh, Aidin, et al. "Grocery apps and consumer purchase behavior: application of Gaussian mixture
model and multi-layer perceptron algorithm." Journal of Risk and Financial Management 15.10 (2022): 424.
International Journal of Science, Technology & Management ISSN: 2722 - 4015
Omol, Edwin, and Collins Ondiek. "Technological Innovations Utilization Framework: The Complementary
Powers of UTAUT, HOT–Fit Framework and; DeLone and McLean IS Model." International Journal of
Scientific and Research Publications (IJSRP) 11.9 (2021): 146-151.
Omonge, Jevans. A Customer segmentation model using logistic regression: a case of Telkom Kenya. Diss.
Strathmore University, 2021.
Omol, Edwin J., Silvance O. Abeka, and Kelvin K. Omieno. "Relevance of Demographic profile on Acceptance
of Mobile Money Payment in enterprise management. A case of MSEs in Kisumu City, Kenya." (2016).
Nandapala, E. Y. L., and K. P. N. Jayasena. "The practical approach in Customers segmentation by using the KMeans Algorithm." 2020 IEEE 15th International Conference on Industrial and Information Systems (ICIIS).
IEEE, 2020.
Syakur, M. A., et al. "Integration k-means clustering method and elbow method for identification of the best
customer profile cluster." IOP conference series: materials science and engineering. Vol. 336. IOP Publishing,
Balakrishnan, PV Sundar, et al. "Comparative performance of the FSCL neural net and K-means algorithm for
market segmentation." European journal of operational research 93.2 (1996): 346-357.
Wu, Jun, et al. "An empirical study on customer segmentation by purchase behaviors using a RFM model and Kmeans algorithm." Mathematical Problems in Engineering 2020 (2020): 1-7.
Huang, Yong, Mingzhen Zhang, and Yue He. "Research on improved RFM customer segmentation model based
on K-Means algorithm." 2020 5th International Conference on Computational Intelligence and Applications
(ICCIA). IEEE, 2020.
Deng, Yulin, and Qianying Gao. "A study on e-commerce customer segmentation management based on
improved K-means algorithm." Information Systems and e-Business Management 18 (2020): 497-510.
Deng, Yulin, and Qianying Gao. "A study on e-commerce customer segmentation management based on
improved K-means algorithm." Information Systems and e-Business Management 18 (2020): 497-510.
Zhao, Hong-Hao, et al. "An extended regularized K-means clustering approach for high-dimensional customer
segmentation with correlated variables." Ieee Access 9 (2021): 48405-48412.
Li, Yue, et al. "Customer segmentation using K-means clustering and the hybrid particle swarm optimization
algorithm." The Computer Journal 66.4 (2023): 941-962.
Aryuni, Mediana, Evaristus Didik Madyatmadja, and Eka Miranda. "Customer segmentation in XYZ bank using
K-means and K-medoids clustering." 2018 International conference on information management and technology
(ICIMTech). IEEE, 2018.
Omol, Edwin Juma. "Organizational digital transformation: from evolution to future trends." Digital
Transformation and Society (2023).
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