Studying Customer Behavior in the Libyan Telecoms Market using Data Visualization
Article Sidebar
-
Culture and Customer Behavior, Big Data, Data Visualization, Telecom Market, SMS
Abstract
In general, the main benefit of data visualization is that it provides a better understanding of complex data and enables faster decision-making.This study on call detail records (CDRs) and SMS activity data visualization was conducted to fully understand the customer behavior patterns and culture in the Libyan telecom market.A big telecom data (261 GB) was collected for CDRs between 28 March and 22 April 2020. This includes 19 working days, three Fridays and four Saturdays. The data visualization results show that the weekend SMS and call activity pattern shows a completely different behavior compared to other days of the week. Call behavior between 12:00 and 7:00 is generally almost the same. The peak times for calls on weekdays are between 11:30 and 13:00 and for SMS between 19:30 and 22:00. Furthermore, the analysis of SMS activities does not reflect customer behavior, but much of the SMS activity is the result of electronic payment services during shopping. However, Libyan culture is more about making phone calls than sending SMS messages. since the number of calls at dawn is minimal, the number of SMS increased slightly at this time. In order to retain customers, decision makers should consider these results and develop appropriate plans and strategies. However, in order to reduce call congestion and increase efficiency, decision makers should set appropriate pricing during peak or pre-peak and post-peak hours to convert many customers before or after peak hours.Also, they should somehow encourage customers to use SMS instead of calls.
Full text article
References
[1]-Marty, R. (2008). Applied security visualization. Addison-Wesley Professional.
[2]-Shiravi, H., Shiravi, A., & Ghorbani, A. A. (2011). A survey of visualization systems for network security. IEEE Transactions on visualization and computer graphics, 18(8), 1313-1329.
[3]-Gorodov, E. Y. E., &Gubarev, V. V. E. (2013). Analytical review of data visualization methods in application to big data. Journal of Electrical and Computer Engineering, 2013, 2-2.
[4]-Wilke, C. O. (2019). Fundamentals of data visualization: a primer on making informative and compelling figures. O'Reilly Media.
[5]-Jiang, W., Wu, J., Sun, G., Ouyang, Y., Li, J., & Zhou, S. (2020). A survey of time series data visualization methods. Journal of Quantum Computing, 2(2), 105.
[6]-Kosara, R., & Miksch, S. (2002). Visualization methods for data analysis and planning in medical applications. International journal of medical informatics, 68(1-3), 141-153.
[7]-Waskom, M. L. (2021). Seaborn: statistical data visualization. Journal of Open-Source Software, 6(60), 3021.
[8]-Shen, L., Shen, E., Luo, Y., Yang, X., Hu, X., Zhang, X., ... & Wang, J. (2021). Towards natural language interfaces for data visualization: A survey. arrive preprint arXiv:2109.03506.
[9]-Wu, T., Xiao, C. L., Lam, T. T. Y., & Yu, G. (2022). Biomedical Data Visualization: Methods and Applications. Frontiers in Genetics, 13.
[10]- [Ebert, L. C., Frankenberg, S., Siebert, T., Schweitzer, W., Thali, M., Ford, J., & Decker, S. (2021). A review of visualization techniques of post-mortem computed tomography data for forensic death investigations. International journal of legal medicine, 135(5), 1855-1867.
[11]-Huang, M. (2022). The Application of Data Visualization in Economic Law Under the Background of Big Data. Frontier Computing: Proceedings of FC 2021, 450-457.
[12]-Yakup, D., Mucahit, C., & Reyhan, O. (2011). The impact of cultural factors on the consumer buying behaviors examined through an empirical study. International Journal of Business and Social Science, 2(5), 109-114.
[13]-Qazzafi, S. H. E. I. K. H. (2019). Consumer buying decision process toward products. International Journal of Scientific Research and Engineering Development, 2(5), 130-134.
[14]-But, A., Bruma, I. S., Tanasi, L., Rodino, S., Dinu Vasiliu, C., Dobos, S., &But, M. (2020). The impact of COVID-19 crisis upon the consumer buying behavior of fresh vegetables directly from local producers. Case study: The quarantined area of Suceava County, Romania. International journal of environmental research and public health, 17(15), 5485.
[15]-Ridwan, M. (2022). Purchasing Decision Analysis in Modern Retail. AKADEMIK: JurnalMahasiswa Ekonomi &Bisnis, 2(1), 1-9.
[16]-Dewi, F. M., &Solovyov, L. (2022). Influence of Consumer Behavior and Marketing Mix on Product Purchasing Decisions. Patesi Transactions on Management (ATM), 6(2), 151-157.
[17]-Shukla, S., Singh, M., Vernekar, S., Navalgund, M. D., Mahadik, R., & Pawar, M. A. (2023). Influence of advergaming on customer buying decision.
[18]-Md Isa, M., M. Mohamad Sabri, M. Ariff Noh, Z. Zah arum, and R. A Latif. 2020. The adoption of mobile banking application among Muslim senior citizens in Selangor, Malaysia. Asian Journal of Research in Business and Management 2 (2):43–51.
[19]-P. Taylor, “Smartphone subscriptions worldwide 2027,” Statista, 18-Jan-2023. [Online]. Available: https://www.statista.com/statistics/330695/number-of-smartphone-users-worldwide/. [Accessed: 16-Mar-2023].
[20]- [Konopnicki, D., and M. Shmueli-Scheuer. (2013). Customer analyst for the telecom industry. Large-Scale Data Analytics 101–27. doi:10.1007/978-1-4614-9242-9_4.
[21]-Gorodov, E. Y. E., &Gubarev, V. V. E. (2013). Analytical review of data visualization methods in application to big data. Journal of Electrical and Computer Engineering, 2013, 2-2.
[22]-Lee, Selim. (2012, October 3). [Compare and Contrast] Calling vs. Text Messaging. Lynn’s Blog. Retrieved March 24, 2023from https://seonliml.wordpress.com/2012/10/03/compare-and-contrast-calling-vs-text-messaging/ [23]-Sarla, G. S. (2020). Texting or Calling: A Comparison. Journal of Open-Source Developments, 7(2), 18-21
Authors
Copyright (c) 2023 Journal of Pure & Applied Sciences
This work is licensed under a Creative Commons Attribution 4.0 International License.
In a brief statement, the rights relate to the publication and distribution of research published in the journal of the University of Sebha where authors who have published their articles in the journal of the university of Sebha should how they can use or distribute their articles. They reserve all their rights to the published works, such as (but not limited to) the following rights:
- Copyright and other property rights related to the article, such as patent rights.
- Research published in the journal of the University of Sebha and used in its future works, including lectures and books, the right to reproduce articles for their own purposes, and the right to self-archive their articles.
- The right to enter a separate article, or for a non-exclusive distribution of their article with an acknowledgment of its initial publication in the journal of Sebha University.
Privacy Statement The names and e-mail addresses entered on the Sabha University Journal site will be used for the aforementioned purposes only and for which they were used.