Predictive Analytics for Customer Retention: A Data-Driven Framework for Proactive Engagement and Satisfaction Management
DOI:
https://doi.org/10.32628/CSEIT241061149Keywords:
Predictive Analytics, Customer Retention, Data-Driven Customer Engagement, Customer Satisfaction Management, Machine Learning in BusinessAbstract
This comprehensive article examines the implementation of predictive analytics and data-driven frameworks for enhancing customer retention in modern business environments. The article explores how advanced analytics, machine learning algorithms, and proactive engagement strategies can significantly improve customer satisfaction and reduce churn rates. Through detailed article analysis of usage patterns, engagement metrics, and customer behavior, the article demonstrates the effectiveness of sophisticated intervention strategies in maintaining strong customer relationships. The article investigates the development and implementation of satisfaction score algorithms, real-time monitoring systems, and customized support mechanisms that enable organizations to identify and address potential issues before they lead to customer attrition. Furthermore, it evaluates the impact of integrated feedback systems and sentiment analysis in creating more responsive and effective customer retention strategies. The article provides valuable insights into how organizations can leverage data analytics to create more personalized and proactive customer engagement approaches, ultimately leading to improved customer lifetime value and business sustainability.
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Li Zhang, Xiang Han, Faming Zhou, "Research on the Relationship Between Customer Value of E-Business and Customer Retention: An Empirical Study in China," 2009 IEEE International Conference on Industrial Engineering and Engineering Management, 2009. https://ieeexplore.ieee.org/document/5373097
Manav Gumber, Apoorv Jain, A L Amutha, "Predicting Customer Behavior by Analyzing Clickstream Data," 2021 5th International Conference on Computer, Communication and Signal Processing (ICCCSP), 2021. https://ieeexplore.ieee.org/document/9465526
Ankur Balar, et al., "Forecasting Consumer Behavior with Innovative Value Proposition for Organizations Using Big Data Analytics," 2013 IEEE International Conference on Computational Intelligence and Computing Research, 2013. https://ieeexplore.ieee.org/document/6724280
Nikolaos Matsatsinis, E. Ioannidou, E. Grigoroudis, "Customer Satisfaction Using Data Mining Techniques," 2019 IEEE International Conference on Data Mining (ICDM), 2019. https://www.researchgate.net/publication/255612679_CUSTOMER_SATISFACTION_USING_DATA_MINING_TECHNIQUES
Paritosh Mahto, "Customer Satisfaction Prediction Using Machine Learning," IEEE Journal of Selected Topics in Signal Processing, 2019. https://towardsdatascience.com/customer-satisfaction-prediction-using-machine-learning-66240e032962
Paolo Bethaz, et al., “Proactive user engagement via friendly survey and data-driven methodologies," 2020 IEEE 36th International Conference on Data Engineering Workshops (ICDEW), 2020. https://ieeexplore.ieee.org/document/9094117
Ranadip Pal, et al., "Robust Intervention in Probabilistic Boolean Networks," 2007 Conference Record of the Forty-First Asilomar Conference on Signals, Systems and Computers, 2007. https://ieeexplore.ieee.org/document/4436035
R.K. Buchheim, "Developing Performance Metrics for a Design Engineering Process," IEEE Transactions on Engineering Management, 2019. https://ieeexplore.ieee.org/document/865900
Bohdan Haidabrus, et al., "Improving Agile Teams Effectiveness Through the Metrics," 2023 IEEE 64th International Scientific Conference on Information Technology and Management Science of Riga Technical University (ITMS), 2023. https://ieeexplore.ieee.org/document/10317789
Pankaj, et al., "Sentiment Analysis on Customer Feedback Data: Amazon Product Reviews," 2019 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COMITCon), 2019. https://ieeexplore.ieee.org/document/8862258
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