Customer Churn Prediction
This innovative analysis examines the impact of customer sentiment on predicting churn in e-commerce businesses. By comparing traditional predictive models against those enhanced with sentiment analysis, we provide deeper insights into customer departure patterns and opportunities for targeted retention.
Key Insights
Dual-Phase Approach
Phase 1: Traditional churn prediction using transactional and behavioral variables
Phase 2: Enhanced models incorporating customer sentiment analysis from feedback
Comparison: Up to 3% improvement in prediction accuracy when sentiment is included
Sentiment Integration
Implemented advanced NLP using DistilBERT for customer feedback analysis
Sentiment scores serve as powerful indicators of future customer behavior
Transformed qualitative feedback into quantifiable predictive metrics
Model Performance
Support Vector Machine and Random Forest models demonstrated superior performance
Comprehensive evaluation using accuracy, recall, precision, F1 score, and ROC-AUC
Feature importance analysis revealed key churn drivers and sentiment impact
Business Applications
Early Intervention: Identify at-risk customers before they churn
Targeted Retention: Develop personalized strategies based on sentiment indicators
Feedback Prioritization: Focus on addressing negative sentiment patterns
ROI Optimization: Allocate retention resources to highest-value at-risk customers
Technologies Leveraged
Python, Scikit-learn, TensorFlow, Hugging Face Transformers, LightGBM, Pandas