Russian Federation
Russian Federation
Repeat calls and repeat calls in telecommunications services increase operating costs and impair service level agreement (SLA) compliance. Therefore, the development of predictive models for recurrence risk and their integration into decision-making is relevant. This paper discusses the problem of predicting repeat events over 7-, 14-, and 30-day horizons, a methodology for preparing telecom data from CRM, OSS, CPE/ONT diagnostics, and Workforce Management, and a prototype information system that prioritizes incidents and provides recommendations for reducing the rate of repeat calls. The initial database is the SLA_Telecom_71430 dataset, containing 71,430 telecommunications event associations over 182 days of observations from GPON/XGS-PON networks. It includes 68 telecom-specific features and three target variables reflecting the risk of SLA violations at different time horizons. The use of machine learning methods, taking into account class imbalance and economic asymmetry of errors, enables the construction of models for proactive service quality management.
telecommunications, SLA, repeat calls, repeat visits, machine learning, predictive analytics, imbalanced data, MLOps, CRM, OSS, Workforce
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