| _version_ | 1866902225613225984 |
|---|---|
| author | Randika Gunasinghe |
| author_facet | Randika Gunasinghe |
| contents | <p>This study examines the contribution of Artificial Intelligence (AI) and predictive analytics to enhancing customer retention activities, providing both conceptual and case-based evidence. Customer retention is a significant driver of business performance, but traditional methods, such as loyalty schemes and blanket marketing, often fail to effectively address churn. Organisations can leverage machine learning, deep learning, and natural language processing to precisely predict churn, personalise engagement, and execute real-time interventions.</p> <p>The research emphasizes three main areas: predictive modeling, ethical embedding of AI, and business impact. Outcomes show that very sophisticated AI models are 95% accurate in forecasting churn, with 20–40% decrease in customer attrition and 30% boost in customer lifetime value. However, challenges such as algorithmic prejudice, explainability, and compliance with regulations (e.g., GDPR, CCPA) remain core impediments.</p> <p>The study concludes that retention by AI is most effective when complemented by ethical safeguards and human-in-the-loop approaches. It offers a conceptual framework and real-world guidelines for data scientists, companies, and policymakers to adopt responsible and customer-centric AI implementation.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_17035552 |
| institution | Zenodo |
| language | |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | How AI and Predictive Analytics Improve Customer Retention Strategies – A Conceptual and Case-Based Analysis Randika Gunasinghe <p>This study examines the contribution of Artificial Intelligence (AI) and predictive analytics to enhancing customer retention activities, providing both conceptual and case-based evidence. Customer retention is a significant driver of business performance, but traditional methods, such as loyalty schemes and blanket marketing, often fail to effectively address churn. Organisations can leverage machine learning, deep learning, and natural language processing to precisely predict churn, personalise engagement, and execute real-time interventions.</p> <p>The research emphasizes three main areas: predictive modeling, ethical embedding of AI, and business impact. Outcomes show that very sophisticated AI models are 95% accurate in forecasting churn, with 20–40% decrease in customer attrition and 30% boost in customer lifetime value. However, challenges such as algorithmic prejudice, explainability, and compliance with regulations (e.g., GDPR, CCPA) remain core impediments.</p> <p>The study concludes that retention by AI is most effective when complemented by ethical safeguards and human-in-the-loop approaches. It offers a conceptual framework and real-world guidelines for data scientists, companies, and policymakers to adopt responsible and customer-centric AI implementation.</p> |
| title | How AI and Predictive Analytics Improve Customer Retention Strategies – A Conceptual and Case-Based Analysis |
| url | https://doi.org/10.5281/zenodo.17035552 |