Machine Learning Framework for Improving Customer Retention and Revenue Using Churn Prediction Models

• Author(s): Aryyama Kumar Jana ; Rudrendu Kumar Paul
• Paper ID: 1704942
• Page: 100-106
• Published Date: 14-08-2023
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 7 Issue 2 August-2023

Abstract

Customer retention and monetization are crucial factors determining the success of businesses. With the increasing availability of data and advanced computational power, machine learning offers promising solutions to predict and manage customer churn. However, the implementation of machine learning in this domain is complex and demands a comprehensive approach. This paper introduces a novel machine learning framework designed to improve client retention and revenue by predicting customer churn. The proposed framework stands apart in its meticulous handling of diverse customer data, encompassing demographic features, purchase behaviors, and satisfaction metrics. The framework further emphasizes data pre-processing, feature engineering, model training, and iterative improvements based on real-world feedback. Lastly, it demonstrates how to translate churn predictions into effective retention and monetization strategies. This study bridges the gap between theory and practice, offering a stepping-stone towards a future where businesses can leverage data to its full potential, enhancing customer satisfaction, business growth, and sustainability.

Keywords

Monetization, Customer retention, Machine Learning, Models, Churn prediction

Citations

IRE Journals:
Aryyama Kumar Jana , Rudrendu Kumar Paul "Machine Learning Framework for Improving Customer Retention and Revenue Using Churn Prediction Models" Iconic Research And Engineering Journals Volume 7 Issue 2 2023 Page 100-106

IEEE:
Aryyama Kumar Jana , Rudrendu Kumar Paul "Machine Learning Framework for Improving Customer Retention and Revenue Using Churn Prediction Models" Iconic Research And Engineering Journals, 7(2)