Online Retail Project

Data: kaggle Github: github

• pandas, numpy, matplotlib, seaborn, plotly, sklearn(k-means clustering), elbow method, xgboost

This is a sample gallery to go along with this case study.

Metrics for Business

• Build metrics: Monthly Revenue, Monthly Active Customers, Monthly Order Count, Average Revenue per Order, New Customer Ratio, Activation Rate, Monthly Retention Rate, Churn Rate, Cohort Base Retention

Customer Segmentation

• RFM: Recency, Frequency, Monetary value (Revenue)
• The main strategies are quite clear: • High Value: Improve Retention • Mid Value: Improve Retention + Increase Frequency • Low Value: Increase Frequency

Customer Lifetime Value Prediction

• RFM: Recency, Frequency, Monetary value (Revenue)
• LTV (Life Time Value), Accuracy, Precision, Recall

Predicting Next Purchase Day

• Data Wrangling (creating previous/next datasets and calculate purchase day differences)
• Feature Engineering
• Selecting a Machine Learning Model
• Multi-Classification Model
• Hyperparameter Tuning