Cohort Retention Analysis

Build a comprehensive cohort retention analysis for my Shopify store to understand customer lifetime behavior and reduce churn.

Store Information:

  Store URL: [YOUR_STORE_URL]
  Store operating since: [STORE_START_DATE, e.g., January 2022]
  Total customers: [TOTAL_CUSTOMERS, e.g., 12000]
  Current repeat purchase rate: [REPEAT_RATE, e.g., 28%]
  Average time between purchases: [PURCHASE_INTERVAL, e.g., 45 days or “unknown”]
  Product type: [PRODUCT_TYPE, e.g., consumable, durable, fashion/seasonal, subscription-eligible]


Cohort Analysis Components:


  Monthly Cohort Retention Table:
    
      Define cohorts by first purchase month (last 12-18 months)
      Track percentage of each cohort that makes a 2nd, 3rd, 4th, and 5th purchase
      Track cumulative revenue per customer in each cohort over time
      Display as a retention matrix with months since first purchase as columns
      Color-code cells: dark for high retention, light for low retention
      Calculate the “retention curve” showing typical drop-off pattern
    
  
  Cohort Quality Comparison:
    
      Compare cohorts by first-order AOV
      Compare cohorts by 90-day retention rate
      Compare cohorts by 12-month cumulative revenue per customer
      Identify which cohorts performed best and correlate with marketing activities
      Flag cohorts with unusually low retention for root cause investigation
    
  
  Retention by Acquisition Channel:
    
      Segment cohorts by traffic source of first order
      Compare retention rates: organic search vs paid ads vs email vs social vs direct
      Calculate lifetime value by acquisition channel
      Identify which channels bring customers with the highest long-term value
      Recommend channel investment adjustments based on retention data
    
  
  Retention by First Purchase Characteristics:
    
      Retention rate by first-order product category
      Retention rate by first-order value tier (low, medium, high)
      Retention rate by discount usage on first order (full price vs discounted)
      Retention rate by device type of first purchase
      Identify the “ideal first purchase” profile that predicts high retention
    
  
  Churn Analysis:
    
      Define churn threshold for your store: no purchase within [CHURN_WINDOW, e.g., 120 days]
      Calculate churn rate by cohort
      Identify the critical window when most churn occurs (e.g., days 30-60 after first purchase)
      Profile of churned customers vs retained customers (AOV, product type, channel)
      Revenue at risk from customers approaching churn threshold
    
  
  Retention Improvement Strategies:
    
      Based on cohort data, recommend specific interventions at each stage:
        
          Day 0-7: post-purchase experience optimization
          Day 7-30: engagement and education sequence
          Day 30-60: replenishment or next-purchase incentive timing
          Day 60-90: win-back campaign triggers
          Day 90+: re-engagement with new products or offers
        
      
      Calculate the revenue impact of improving retention by [RETENTION_TARGET, e.g., 5 percentage points]
      Recommend loyalty program features that address observed churn patterns
      Provide a 90-day retention improvement action plan with measurable milestones