To solve the problem that is relevant for Mega Telco, specialists need to carefully analyze the data. It is worth highlighting that the most effective types of information in this area will be customer data, their current tariffs, pricing of competitors, and place of residence (Provost and Fawcett, 2013).
Information about the state and city of the individual will help specialists to identify average prices, and therefore the level of the total costs of the client (Jackson and Lockwood, 2018). This will lead to the possibility of providing the most favorable offer to the client in a way that will retain them. Individual data refers to the age and period of use of Mega Telco services (Anandan, 2021). Such data will help to form an approximate picture of a person in such a way as to identify their most important needs. For example, young people tend to prioritize faster internet, gaming, and long phone calls (Hassanien et al., 2018). At the same time, the older generation needs other services, such as SMS (Hassanien et al., 2018). The term of use will help evaluate customer loyalty and satisfaction with the provided services and identify the most valuable Mega Telco products for a particular individual.
Finally, it is necessary to constantly analyze the pricing of competitors and the general situation on the market since the offer must remain as profitable and relevant as possible compared to the backward ones. Thus, all of the above data is used to apply an individual approach to each client, increasing their loyalty and satisfying the strongest needs of the service. In addition, a plan must be made for how Mega Telco should select customers to reduce churn as effectively as possible (Hill and Berry, 2021). It is worth carefully analyzing the data on existing contracts, which will allow specialists to make an offer before it ends. The group of interest here is primarily young people because they are most willing to engage in dialog and make decisions faster (Ram and Kumar, 2021). When interacting with this group, the company significantly increases the chances of attracting and retaining a client (Isson, 2018). For example, young people are more likely to pick up the phone and listen to promotional offers (Saura, 2021).
Based on the above, the offer to extend services or change the tariff younger before the end of the contract is the most effective strategy for Mega Telco.
Anandan, R. (Ed.). (2021) A closer look at big data analytics. New York: Nova Science Publishers, Incorporated.
Hassanien, A. E., Abraham, A. & Panda, M. (Eds.). (2018) Big data analytics. A social network approach. Boca Raton: CRC Press.
Hill, R. & Berry, S. (2021) Guide to industrial analytics. Solving data science problems for manufacturing and the internet of things. New York: Springer International Publishing.
Isson, J. P. (2018) Unstructured data analytics. How to improve customer acquisition, customer retention, and fraud detection and prevention. New Jersey: Wiley.
Jackson, T. W. & Lockwood, S. (2018) Business analytics. A contemporary approach. London: Bloomsbury Publishing.
Provost, F. & Fawcett, T. (2013) Data sciences for business: What you need to know about data mining and data-analytics thinking. Sebastopol: O’Reilly Media.
Ram, M. & Kumar, V. (Eds.). (2021) Predictive analytics. Modeling and optimization. Boca Raton: CRC Press.
Saura, J. R. (Ed.). (2021) Advanced digital marketing strategies in a data-driven era. Hershey: IGI Global.