GA optimization-based BRB AI reasoning algorithm for determining the factors affecting customer churn for operators

• Main Authors: Kun, Liu, Alli, Hassan, Abd Rahman, Khairul Aidil Azlin
• Format: Article
• Language: English
• Published: Elsevier 2024
• Online Access: http://psasir.upm.edu.my/id/eprint/113372/1/113372.pdf; http://psasir.upm.edu.my/id/eprint/113372/; https://linkinghub.elsevier.com/retrieve/pii/S2590291124001414
• Institution: Universiti Putra Malaysia

Customer churn directly leads to the weakening of the economic efficiency of an operator and even the loss of its competitive advantage in the market share. The identification and prediction of customer churn in the era of interconnection has become more complex, as it not only is based on the analysis of customer information and customer churn data but also must take into account the intricate relationships between the market economy and culture. In the era of big data, numerous predictive models are based on more redundant features, which increases the complexity of the algorithms and the difficulty of analyzing customer churn. Therefore, in this paper, a belief rule base (BRB) artificial intelligence inference algorithm based on GA optimization to determine the factors affecting customer churn for operators proposed. First, customer churn data from a website are analyzed, and features are extracted. Second, a BRB is established from the experience of operator experts, and a BRB inference prediction model is constructed for predicting and analyzing customer churn. Finally, the BRB model is optimized by GA optimization, the input characteristics with high feature weights are obtained, and the accuracy of the churn analysis is verified according to the obtained features. The results show that this method not only outperforms the comparative SVM and BP neural network models for predicting customer churn but also provides better judgment of the main inputs of customer churn, thus reducing the reliance on input information, optimizing the complexity of the algorithm, and enabling operators to obtain a more accurate understanding of the main factors that lead to churn.