AI-Powered Client Turnover Prediction in US Business Markets

Authors

  • Lucas Daniel Department of Computer Engineering, Arizona State University Author
  • Samuel Alexander Department of Computer Engineering, Arizona State University Author

Keywords:

AI, machine learning, customer churn prediction, business markets, USA, client turnover, explainable AI, interpretability, feature engineering, retention strategies.

Abstract

This paper presents an AI-based customer churn prediction model tailored for business markets in the USA. Leveraging advanced machine learning techniques, the proposed model aims to anticipate client turnover, thereby empowering businesses to proactively mitigate churn and retain valuable customers. By analyzing historical data and identifying patterns indicative of potential churn, the model provides actionable insights for strategic decision-making. Moreover, the incorporation of explainable AI enhances the interpretability of predictions, fostering trust and understanding among stakeholders. Through a comprehensive evaluation of various machine learning algorithms and feature engineering techniques, the proposed model demonstrates promising performance in predicting customer churn. Ultimately, this research contributes to the advancement of customer retention strategies in US business markets through the application of AI and machine learning technologies.

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Published

2024-04-22

How to Cite

AI-Powered Client Turnover Prediction in US Business Markets. (2024). International Journal of Advanced Engineering Technologies and Innovations, 1(3), 116-131. http://ijaeti.com/index.php/Journal/article/view/232