Information technology for the analysis of mobile operator sales outlets based on clustering methods

2023;
: 105-113
https://doi.org/10.23939/ujit2023.02.105
Received: October 23, 2023
Accepted: October 26, 2023

Цитування за ДСТУ: Нарушинська О. О., Мотрунич В. І., АрзубовМ. В. , Теслюк В. М. Інформаційна технологія для аналізу пунктів продажу мобільного оператора на основі методів кластеризаціЇ. Український журнал інформаційних технологій. 2023. Т. 5, № 2. С. 105–113.
Citation APA: Narushynska, O. O., Motrunych, V. I., Arzubov, M. V., & Teslyuk, V. M. (2023). Information technology for the analysis of mobile operator sales outlets based on clustering methods. Ukrainian Journal of Information Technology, 5(2), 105–113. https://doi.org/10.23939/ujit2023.02.105

1
Lviv Polytechnic National University, Lviv, Ukraine
2
Lviv Polytechnic National University, Lviv, Ukraine
3
Lviv Polytechnic National University, Lviv, Ukraine
4
Lviv Polytechnic National University, Lviv, Ukraine

This research presents the development and implementation of information technology for monitoring and analyzing segments of a mobile operator's stores using clustering methods. The study addresses a pertinent issue in marketing and business optimization, namely the enhancement of strategies for the network of mobile communication stores.

The research paper presents a novel approach to segmenting mobile operator stores using clustering algorithms. A software product was developed that includes machine learning algorithms for clustering stores according to critical parameters. A comprehensive analysis of the mobile operator's database was conducted to identify critical characteristics of the stores, such as profitability, patterns of mobile operator service usage, the number of new and lost customers, geographical location, and other vital indicators.

Particular attention was paid to developing tools for preparing and processing input data, ensuring the accuracy of subsequent clustering. With the created product, the mobile operator can identify the most profitable stores, uncover growth opportunities, and develop targeted strategies for each segment.

By applying the developed technology, the mobile operator gains the ability not only to identify crucial and profitable sales points but also to develop focused strategies for different groups of stores, taking into account their unique characteristics. This approach strengthens the company's market position, increasing customer satisfaction and profitability.

Additionally, when examining the possibilities of analyzing store dynamics over time, it is necessary to consider the ever-evolving business environment. Such a tool can assist the operator in swiftly adapting strategies and responding to new trends and challenges while preserving stability and profitability.

Similar innovative approaches not only facilitate the management of a mobile operator's store network but also enable the establishment of more open and flexible customer relationships. By providing personalized services and responding to their needs, businesses can enhance customer loyalty and increase their profits.

In conclusion, this research endeavour carries significant practical implications for the realms of marketing and mobile operator development. Its findings can be harnessed to enhance the efficiency of operations and profitability within this industry.

1. Chiang, M. MT., & Mirkin, B. (2010). Intelligent Choice of the Number of Clusters in K-Means Clustering: An Experimental Study with Different Cluster Spreads. J Classif 27, 3 40. 
https://doi.org/10.1007/s00357-010-9049-5
2. Collica, R. S. (2021). Segmentation Analytics with SAS Viya: An Approach to Clustering and Visualization. SAS Institute.
3. Doroshenko, A. (2020). "Analysis of the Distribution of COVID-19 in Italy Using Clustering Algorithms," 2020 IEEE Third International Conference on Data Stream Mining & Processing (DSMP), Lviv, Ukraine, 2020, pp. 325-328.
https://doi.org/10.1109/DSMP47368.2020.9204202
4. Hanafizadeh, P., & Mirzazadeh, M. (2011). Visualizing market segmentation using self-organizing maps and Fuzzy Delphi method - ADSL market of a telecommunication company. Expert Systems with Applications, 38(1), 198-205.
https://doi.org/10.1016/j.eswa.2010.06.045
5. Maddumala, V. R., Chaikam, H., Velanati, J. S., Ponnaganti, R., & Enuguri, B. (2022). Customer Segmentation using Machine Learning in Python. 2022 7th International Conference on Communication and Electronics Systems (ICCES), Coimbatore, India, 1268-1273.
https://doi.org/10.1109/ICCES54183.2022.9836018
6. Mim, S. S., & Logofatu, D. (2022). A Cluster-based Analysis for Targeting Potential Customers in a Real-world Marketing System. 2022 IEEE 18th International Conference on Intelligent Computer Communication and Processing (ICCP), Cluj-Napoca, Romania, 159-166.
https://doi.org/10.1109/ICCP56966.2022.10053985
7. Nandapala, E. Y. L., & Jayasena, K. P. N. (2020). The practical approach in Customers segmentation by using the K-Means Algorithm. 2020 IEEE 15th International Conference on Industrial and Information Systems (ICIIS), RUPNAGAR, India, 344-349.
https://doi.org/10.1109/ICIIS51140.2020.9342639
8. Rosário, A. T., Dias, J. C., & Ferreira, H. (2023). Bibliometric Analysis on the Application of Fuzzy Logic into Marketing Strategy. Businesses, 3(3), 402-423.
https://doi.org/10.3390/businesses3030025
9. Stiadi, M. (2022). Market segmentation analysis in food selection. Jurnal Ekonomi, 11(03), 169 173.
https://doi.org/10.1016/j.eswa.2010.06.045
10. Wu, J., Xiong, H., & Chen, J. (2009). Adapting the right measures for K-means clustering. In Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD '09). Association for Computing Machinery, New York, NY, USA, 877 886.
https://doi.org/10.1145/1557019.1557115