Customer Analytics
Customer Segmentation
Key Concept
Impact
Segment non-member behavior
Basket-level (shopping missions), store-level (store profile analysis)
Customize promotions by store/basket segments
Improve existing segments with clustering algorithms & new features
Explore different segment types (product, shopping missions, store, CLV, cohort, etc.)
Optimize member segment similarity with clustering
Better member segment lead to more effective A/B Tests
Customer Lifetime Value
Key Concept
Impact
Create daily historical+predicted CLV
Use historical CLV trend to predict future
More flexible tracking of CLV that represents current trends
Quantify key factors that drive CLV
Utilize regression analysis to quantify the impact of each factor (products and promotion type)
Actionable focus areas to improve LT customer value
Churn Prediction
Key Concept
Impact
Quantify % probability of member churn
Need to agree on churn business logic
Combine with CLV to focus the budget on retaining high value.
Identify key factors that prevent churn
Utilize regression analysis to quantify the impact of each factor (products and promotion type)