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An autonomous mixed data oversampling method for AIOT-based churn recognition and personalized recommendations using

Ghulam Fatima1, Salabat Khan1,2, Farhan Aadil1

  • 1Department of Computer Science, Comsats University Islamabad, Attock Campus Pakistan, Attock, Punjab, Pakistan.

Peerj. Computer Science
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Summary

This study integrates artificial intelligence (AI) and Internet of Things (IoT) for telecom customer retention. A unified platform treats churn recognition and segmentation as one problem, improving accuracy and enabling personalized service recommendations.

Keywords:
AIOTAutoML based oversamplingCustomer segmentation and churn predictionHyper-parameters optimizationMixed data over-samplingPersonalized recommendations

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Area of Science:

  • Telecommunications
  • Data Science
  • Artificial Intelligence

Background:

  • Telecoms face customer retention challenges amid digital transformation with AI and IoT.
  • Existing methods often treat churn recognition and customer segmentation as separate, reducing accuracy.
  • Analyzing IoT device data patterns is crucial for understanding customer behavior and service package relevance.

Purpose of the Study:

  • To introduce a unified customer analytics platform for telecom churn recognition and segmentation.
  • To address the challenge of treating churn and segmentation as independent tasks.
  • To leverage AI and IoT data for enhanced customer retention strategies.

Main Methods:

  • A bi-level optimization problem formulation for unified churn recognition and segmentation.
  • Auto Machine Learning (AutoML) oversampling, including SMOTE-NC and SMOTE-ENC, for imbalanced datasets.
  • Factor analysis with Bayesian logistic regression for identifying segmentation factors.

Main Results:

  • The proposed unified approach, particularly Random Forest with SMOTE-NC, significantly outperformed standard methods.
  • Achieved high accuracy (up to 94.54%) and F1-scores (up to 81.87%) across multiple datasets (IBM, Kaggle Telco, Cell2Cell).
  • The method autonomously determines cluster parameters and identifies key customer segmentation factors.

Conclusions:

  • Integrating AI and IoT through a unified analytics platform enhances telecom customer retention.
  • Behavioral customer segmentation and personalized recommendations are key outcomes.
  • The proposed bi-level optimization framework offers a robust solution for complex customer analytics in telecoms.