Customer Insights and Predictive Analytics
- Conducted A/B testing to analyze the purchasing behavior of Frequent Shoppers versus Occasional Shoppers, identifying a significant difference in total purchase amounts
- Implemented a collaborative filtering recommendation system using k-nearest neighbors, providing personalized product recommendations for customers based on their purchase history
- Developed a machine learning model to predict customer churn, utilizing recency, frequency, and monetary value features, achieving an accuracy of 100% on the test set
- Utilized time series forecasting with ARIMA to predict future total purchases, demonstrating the model’s effectiveness in capturing trends and making accurate predictions
- Applied k-means clustering to segment customers based on recency, frequency, and monetary values, enabling targeted marketing strategies for different customer groups
