Big data-driven framework for viral churn prevention: a case study

• Autorzy: Lucantoni Laura , Antomarioni Sara , Bevilacqua Maurizio , Ciarapica Filippo E.

Identyfikatory

DOI

10.24425/mper.2020.134930

• Języki publikacji: EN

Abstrakty

EN

The application of churn prevention represents an important step for mobile communication companies aiming at increasing customer loyalty. In a machine learning perspective, Customer Value Management departments require automated methods and processes to create marketing campaigns able to identify the most appropriate churn prevention approach. Moving towards a big data-driven environment, a deeper understanding of data provided by churn processes and client operations is needed. In this context, a procedure aiming at reducing the number of churners by planning a customized marketing campaign is deployed through a data-driven approach. Decision Tree methodology is applied to drow up a list of clients with churn propensity: in this way, customer analysis is detailed, as well as the development of a marketing campaign, integrating the individual churn model with viral churn perspective. The first step of the proposed procedure requires the evaluation of churn probability for each customer, based on the influence of his social links. Then, the customer profiling is performed considering (a) individual variables, (b) variables describing customer-company interactions, (c) external variables. The main contribution of this work is the development of a versatile procedure for viral churn prevention, applying Decision Tree techniques in the telecommunication sector, and integrating a direct campaign from the Customer Value Management marketing department to each customer with significant churn risk. A case study of a mobile communication company is also presented to explain the proposed procedure, as well as to analyze its real performance and results.

Słowa kluczowe

EN

big data analytics; machine learning; Probability Estimation Trees; Customer Value Management; ICT sector

• Wydawca: Production Engineering Committee of the Polish Academy of Sciences ; Polish Association for Production Management
• Czasopismo: Management and Production Engineering Review
• Rocznik: 2020
• Tom: Vol. 11, No. 3
• Strony: 38--47
• Opis fizyczny: Bibliogr. 29 poz., rys., wykr.

Twórcy

autor

Lucantoni Laura

• Dipartimento di Ingegneria Industriale e Scienze Matematiche, Universit`a Politecnica Delle Marche, Italy

autor

Antomarioni Sara

• Dipartimento di Ingegneria Industriale e Scienze Matematiche, Universit`a Politecnica Delle Marche, Italy

autor

Bevilacqua Maurizio

• Dipartimento di Ingegneria Industriale e Scienze Matematiche, Universit`a Politecnica Delle Marche, Via Brecce Bianche, 12, 60131, Ancona, Italy

autor

Ciarapica Filippo E.

• Dipartimento di Ingegneria Industriale e Scienze Matematiche, Universit`a Politecnica Delle Marche, Italy

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