New Marketing Aspects in the Digital Economy

Agafonova, Anna Nikolaevna; Novikova, E. N.; Shakirov, R. A.

doi:10.1007/978-3-030-83175-2_65

Cited by 1 publication

(1 citation statement)

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“…Companies are dealing with new opportunities and challenges in their development (Li, 2022). Information technologies are steadily entering the business environment, there is a rapid development of predictive analytics, data analysis, telecommunications, as the economic value of information is related to business success and emerging business management opportunities (Agafonova et al, 2022). With advances and the explosive growth in data mining technologies and the availability of vast amounts of customer data, companies have begun to use powerful data science tools in precision marketing to dive deeper into the nature and habits of their customers.…”

Section: Introductionmentioning

confidence: 99%

Customer churn prediction model: a case of the telecommunication market

et al. 2022

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The telecommunications market is well developed but is characterized by oversaturation and high levels of competition. Based on this, the urgent problem is to retain customers and predict the outflow of customer base by switching subscribers to the services of competitors. Data Science technologies and data mining methodology create significant opportunities for companies that implement data analysis and modeling for development of customer churn prediction models. The research goals are to compare different approaches and methods for customer churn prediction and construct different Data Science models to classify customers according to the probability of their churn from the company’s client base and predict potential customers who could stop to use the company’s services. On the example of one of the leading Ukrainian telecommunication companies, the article presents the results of different classification models, such as C5.0, KNN, Neural Net, Ensemble, Random Tree, Neural Net Ensemble, etc. All models are prepared in IBM SPSS Modeler and have a high level of quality (the overall accuracy and AUC ROC are more than 90%). So, the research proves the possibility and feasibility of using models in the further classification of customers to predict customer loyalty to the company and minimize consumer’s churn. The key factors influencing the customer churn are identified and form a basis for future prediction of customer outflow and optimization of company’s services. Implementation of customer churn prediction models will help to maintain customer loyalty, reduce customer outflow and increase business results

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Section: Introductionmentioning

confidence: 99%