Практические методы прогнозирования сохранения клиентской базы (перевод на русский язык)

Черкашин, Александр and Сахаджи, Владислав and Гулиев, Руслан and Большунова, Елена (2024): Практические методы прогнозирования сохранения клиентской базы (перевод на русский язык).

Abstract

This study examines methods for analyzing and forecasting the retention of active subscribers in the telecommunications industry using various criteria for subscriber activity. The results demonstrate that the retention dynamics of an active subscriber base can be effectively modeled using a decreasing power function. This allows for medium-term forecasting based on initial subscriber activity data. However, it is important to note the potential limitations in the effectiveness of the proposed approach for long-term forecasting, associated with changes in subscriber churn dynamics over time. This is a Russian translation of «Practical Methods for Predicting Customer Retention» paper published on MPRA (https://mpra.ub.uni-muenchen.de/id/eprint/122400) 15.10.2024.

• Item Type: MPRA Paper
• Commentary on: Cherkashin, Alexander and Sakhadzhi, Vladislav and Guliev, Ruslan and Bolshunova, Elena (2024): Practical Methods for Predicting Customer Retention.
• English Title: Practical Methods for Predicting Customer Retention
• Language: Russian
• Keywords: абонентская база; удержание абонентов; отток абонентов; степенная функция; телекоммуникации; LTV
• Subjects: C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C53 - Forecasting and Prediction Methods ; Simulation Methods; D - Microeconomics > D1 - Household Behavior and Family Economics > D12 - Consumer Economics: Empirical Analysis; L - Industrial Organization > L9 - Industry Studies: Transportation and Utilities > L96 - Telecommunications; M - Business Administration and Business Economics ; Marketing ; Accounting ; Personnel Economics > M3 - Marketing and Advertising > M31 - Marketing

References

Andriani, P. & McKelvey, B. (2007). Beyond Gaussian averages: Redirecting international business and management research toward extreme events and power laws. Journal of International Business Studies, 38(7), 1212-1230

Cherkashin, A. Practical Methods for Predicting Customer Retention / A. Cherkashin, V. Sakhadzhi, R. Guliev, E. Bolshunova // https://mpra.ub.uni-muenchen.de/id/eprint/122400

Deevey, E.S., Jr (1947). Life tables for natural populations of animals. The Quarterly Review of Biology, 22, 283-314.

Fader, P. S. & Hardie, B. G. (2009). Probability models for customer-base analysis. Journal of interactive Marketing, 23(1), 61-69.

Gabaix, X. (2009). Power Laws in Economics and Finance. Annual Review of Economics, 1, 255-293.

Gupta, S. & Zeithaml, V. (2006). Customer metrics and their impact on financial performance. Marketing Science, 25(6), 718-739.

Hill, R. W., Sleboda, D. & Millar, J.J. (2021). Youth in the study of comparative physiology: insights from demography in the wild. Journal of Comparative Physiology B, 191, 1-16.

Jain, H., Khunteta, A. & Srivastava, S. (2021). Telecom churn prediction and used techniques, datasets and performance measures: a review. Telecommunication Systems, 76(4), 613-630.

Krstevski, D. & Manchenski, G. (2016). Managerial Accounting: Modeling Customer Lifetime Value – An Application in the Telecommunication Industry. European Journal of Business and Social Sciences, 5(1), 64-77.

Kumar, V. (2008). Costumer lifetime value: The path to profitability. Now Publishers.

Newman, M. E. J. (2005). Power laws, Pareto distributions and Zipf’s law. Contemporary Physics, 46(5), 323-351.

Pearl, R. & Miner, J. R. (1935). Experimental studies on the duration of life. XIV. The comparative mortality of certain lower organisms. The Quarterly Review of Biology, 10, 60-79.

Ribeiro, H., Barbosa, B., Moreira, A. C. & Rodrigues, R. G. (2023). Determinants of churn in telecommunication services: a systematic literature review. Management Review Quarterly, 74(3), 1327-1364.

Staddon, J. (1978). Theory of behavioral power functions. Psychological Review, 85(4), 305-320.

Verhelst, T., Caelen, O., Dewitte, J. C., Lebichot, B. & Bontempi, G. (2021). Understanding Telecom Customer Churn with Machine Learning: From Prediction to Casual Inference. Artifival Intelligence and Machine Learning, 1996, 182-200.

Verbeke, W., Martens, D., Mues, C. & Baesens, B. (2011). Building comprehensible customer churn prediction models with advanced rule induction techniques. Expert Systems with Applications, 38(3), 2354-2364.

Zhang J. Z. & Chang, C. W. (2021). Consumer dynamics: Theories, methods, and emerging directions. Journal of the Academy of Marketing Science, 49(1), 166-196.