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Related Concept Videos

Prediction Intervals01:03

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Related Experiment Videos

Artificial Intelligence Based Customer Churn Prediction Model for Business Markets.

J Faritha Banu1, S Neelakandan2, B T Geetha3

  • 1Department of Computer Science and Engineering, SRM Institute of Science and Technology, Ramapuram, Chennai, India.

Computational Intelligence and Neuroscience
|October 14, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces an AI-based Customer Churn Prediction (CCP) model for telecommunications, achieving high accuracy in identifying potential churners. The novel AICCP-TBM model effectively reduces customer churn by improving prediction performance.

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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Telecommunications

Background:

  • Customer churn significantly impacts telecommunication company revenue.
  • Developing effective Customer Churn Prediction (CCP) models is crucial for the telecom industry.
  • Existing AI and ML models show promise for CCP solutions.

Purpose of the Study:

  • To propose a unique AI-based CCP model for Telecommunication Business Markets (AICCP-TBM).
  • To control the identification of churners and non-churners in the telecom sector.
  • To enhance classification performance for improved customer retention strategies.

Main Methods:

  • Utilizing Chaotic Salp Swarm Optimization-based Feature Selection (CSSO-FS) for optimal feature selection.
  • Employing a Fuzzy Rule-based Classifier (FRC) to differentiate between churners and non-churners.
  • Optimizing FRC membership functions using Quantum Behaved Particle Swarm Optimization (QPSO).

Main Results:

  • The AICCP-TBM model demonstrated superior performance compared to state-of-the-art CCP models.
  • Achieved high accuracy rates of 97.25%, 97.5%, and 94.33% on three benchmark datasets.
  • Validated improved prediction performance through extensive experimental analysis.

Conclusions:

  • The proposed AICCP-TBM model offers an effective solution for customer churn prediction in telecommunications.
  • The integration of CSSO-FS and QPSO-optimized FRC enhances predictive accuracy.
  • This AI-driven approach aids telecom businesses in proactive customer retention efforts.