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Cohort Data Documentation

Menu Location: Reports > (needs sorting) > Cohort Data

Access Level: Administrator and above

Last Updated: 2026-03-01

Overview

The Cohort Data page provides a specialized data export tool for cohort analysis and customer lifetime value calculations. This tool generates comprehensive CSV files containing all completed order data with customer IDs, delivery dates, order values, and box prices - perfect for analyzing customer retention, lifetime value, and cohort performance over time.

Primary Functions:

  • Generate comprehensive order data exports
  • Analyze customer cohorts by signup date
  • Calculate customer lifetime value (LTV)
  • Track retention rates over time
  • Measure cohort performance
  • Export data for external analytics tools

Page Layout

Header Section

  • Page Title: "Cohort Data"
  • Tool Description: Brief explanation of CSV output

Action Section

  • Create Download button - Initiates data collection
  • Status message area
  • Processing indicator

Download History Table

  • List of previously generated cohort data files
  • Download buttons for each export
  • Timestamps for each file
  • Most recent export highlighted

What is Cohort Analysis?

Understanding Cohorts

Cohort Definition: A group of customers who share a common characteristic within a defined time period, typically when they first signed up or made their first purchase.

Example Cohorts:

  • All customers who signed up in January 2026
  • Customers who first ordered in Q1 2026
  • New customers acquired during spring promotion
  • Customers from specific marketing campaign

Why Cohort Analysis Matters

Business Insights:

  • Retention: How long customers stay active
  • Lifetime Value: Total revenue per customer over time
  • Performance: Which acquisition periods brought best customers
  • Trends: Are newer cohorts performing better or worse?
  • Seasonality: How seasons affect customer behavior

Strategic Decisions:

  • Marketing ROI: Which campaigns brought most valuable customers
  • Product Development: What keeps customers engaged long-term
  • Pricing: How price changes affect retention
  • Customer Experience: Improvements that increase retention

CSV File Contents

Data Columns Included

ID:

  • Unique order identifier
  • Reference for order lookup
  • Sequential numbering

Customer ID:

  • Unique customer account number
  • Link to customer record
  • Groups orders by customer

Deliver On (Order Date):

  • Delivery date for the order
  • Format: YYYY-MM-DD
  • Sortable date format

Order Value (Actual Total):

  • Total amount customer paid
  • Includes all fees and taxes
  • Actual revenue received

Box Price:

  • Base subscription box price
  • Excludes add-ons and extras
  • Core product value

Data Scope

Included Orders:

  • Status = 4 (Completed orders only)
  • All historical completed orders
  • Both active and inactive customers
  • All order types

Excluded Orders:

  • Cancelled orders
  • Pending orders
  • Open/unfulfilled orders
  • Test orders (if flagged)

How to Use Cohort Data

Step 1: Generate Data Export

Process:

  1. Click "Create Download" button
  2. System begins collecting data (runs in background)
  3. Status message appears: "Data is being collected"
  4. Email notification sent when complete
  5. Download appears in history table

Processing Time:

  • Small databases (under 10K orders): 1-2 minutes
  • Medium databases (10K-50K orders): 2-5 minutes
  • Large databases (50K+ orders): 5-15 minutes

Note: Page can be closed during processing - you'll receive email when ready

Step 2: Download CSV File

From History Table:

  1. Find most recent export (highlighted in green)
  2. Click "Download" button
  3. File saves to your computer
  4. Open in Excel, Google Sheets, or analytics tool

File Naming:

  • Format: cohort-data-YYYY-MM-DD_HH-MM-SS.csv
  • Example: cohort-data-2026-03-01_14-30-45.csv
  • Timestamp indicates when export was created

Step 3: Analyze in Spreadsheet

Basic Analysis in Excel/Sheets:

  1. Calculate Customer Lifetime Value (LTV):
  2. Create pivot table
  3. Rows: Customer ID
  4. Values: Sum of Order Value

  5. Shows total revenue per customer

  6. Identify Cohorts:

  7. Create pivot table
  8. Rows: Month of first order (derived from dates)
  9. Values: Count of unique Customer IDs

  10. Shows customer acquisition by month

  11. Calculate Retention:

  12. For each cohort, count active customers each month
  13. Retention Rate = (Active in Month N / Original Cohort Size) × 100

  14. Track decline over time

  15. Analyze Order Frequency:

  16. Pivot: Customer ID × Count of Orders
  17. Average: Total orders ÷ Customer count
  18. Shows engagement level

Common Use Cases

Use Case 1: Calculate Customer Lifetime Value

Goal: Determine average revenue per customer

Steps:

  1. Generate and download cohort data
  2. Open in Excel or Google Sheets
  3. Create pivot table:
    • Rows: Customer ID
    • Values: Sum of Order Value
  4. Calculate average LTV: Sum all customer totals ÷ Number of customers
  5. Calculate median LTV (50th percentile)
  6. Identify high-value customers (top 10%)

Example Results:

  • Total customers: 2,500
  • Average LTV: $850
  • Median LTV: $600
  • Top 10% LTV: $2,500+

Use insights to:

  • Set customer acquisition cost limits
  • Identify VIP customers for special treatment
  • Calculate acceptable churn rate
  • Forecast revenue based on new customers

Use Case 2: Cohort Retention Analysis

Goal: Understand how long customers stay active

Steps:

  1. Download cohort data
  2. Add "First Order Date" column (use MIN function by Customer ID)
  3. Add "Months Since First Order" column
  4. Create cohort table:
    • Rows: Month of first order
    • Columns: Months since first order (0, 1, 2, 3, etc.)
    • Values: Count of customers still ordering
  5. Calculate retention percentages
  6. Visualize with retention curves

Example Retention:

  • Month 0 (signup): 100%
  • Month 1: 85%
  • Month 3: 70%
  • Month 6: 60%
  • Month 12: 45%

Insights:

  • Biggest drop in first month (onboarding issue?)
  • Stabilizes after 3 months
  • Need better month 1-3 engagement

Use Case 3: Compare Cohort Performance

Goal: Determine if newer customers are more/less valuable

Steps:

  1. Generate cohort data
  2. Group customers by signup month/quarter
  3. Calculate metrics for each cohort:
    • Average LTV
    • Retention rates
    • Average order value
    • Order frequency
  4. Compare cohorts side-by-side
  5. Identify trends (improving or declining?)

Example Comparison:

  • Q1 2025 cohort: $750 avg LTV, 50% retained at 6 months
  • Q4 2025 cohort: $900 avg LTV, 65% retained at 6 months
  • Trend: Newer customers more valuable!

Possible Reasons:

  • Better product selection
  • Improved onboarding
  • Better customer targeting
  • Price optimization

Use Case 4: Seasonality Analysis

Goal: Understand how signup time affects customer value

Steps:

  1. Download cohort data
  2. Tag each customer by signup season (Spring, Summer, Fall, Winter)
  3. Compare metrics by season:
    • Summer signups: Higher initial orders, lower retention
    • Winter signups: Lower initial, higher retention
  4. Adjust marketing strategy by season
  5. Plan inventory and capacity

Strategic Actions:

  • Focus summer marketing on trial/sample offers
  • Focus winter marketing on long-term value
  • Adjust product mix by season
  • Plan staffing for seasonal patterns

Use Case 5: Marketing Channel LTV Comparison

Goal: Which channels bring most valuable customers?

Steps:

  1. Ensure customers tagged by acquisition source
  2. Generate cohort data
  3. In separate system, match Customer IDs to source tags
  4. Calculate average LTV by source:
    • Google Ads customers: $650 avg
    • Facebook Ads customers: $450 avg
    • Referrals: $1,200 avg!
  5. Calculate ROI: LTV ÷ Acquisition Cost
  6. Reallocate marketing budget accordingly

ROI Example:

  • Google: $650 LTV ÷ $80 CAC = 8.1x ROI
  • Facebook: $450 LTV ÷ $60 CAC = 7.5x ROI
  • Referrals: $1,200 LTV ÷ $25 CAC = 48x ROI!

Action: Invest heavily in referral program

Advanced Analytics

Combining with Other Data

Merge with Customer Data:

  • Customer demographics
  • Tags and segments
  • Acquisition sources
  • Geographic data

External Tools:

  • Import into Tableau, Power BI, Looker
  • Python/R for statistical analysis
  • SQL databases for complex queries
  • Google Analytics for web behavior correlation

Key Metrics to Calculate

Retention Rate:

  • (Customers active in Month N / Original cohort) × 100

Churn Rate:

  • 100% - Retention Rate

Customer Lifetime Value (LTV):

  • Average revenue per customer over their lifetime

Average Order Value (AOV):

  • Total revenue ÷ Total orders

Purchase Frequency:

  • Orders per customer per year

Customer Acquisition Cost (CAC):

  • (From marketing data) Cost to acquire one customer

LTV:CAC Ratio:

  • LTV ÷ CAC (should be 3:1 or higher)

Troubleshooting

Download Not Generating

Symptoms:

  • Click button but no processing message
  • No email received
  • No new download in history

Solutions:

  1. Refresh page and try again
  2. Check if another export is running
  3. Verify permissions (Administrator required)
  4. Check email spam folder
  5. Contact support if issue persists

CSV File Won't Open

Symptoms:

  • File downloads but won't open
  • Corrupted file error
  • Empty file

Solutions:

  1. Try different program (Excel vs Google Sheets)
  2. Check file size (should be > 0 bytes)
  3. Re-download from history
  4. Try older export to verify system

Data Seems Incomplete

Symptoms:

  • Missing recent orders
  • Customer count seems low
  • Date range shorter than expected

Check:

  1. Verify export timestamp
  2. Check if recent orders are complete (status = 4)
  3. Confirm all customers have completed orders
  4. Generate new export if data is old

Processing Takes Too Long

Symptoms:

  • No email after 30+ minutes
  • Status stuck on processing
  • Previous exports took much less time

Solutions:

  1. Check database size (may be very large)
  2. Generate during off-peak hours
  3. Contact administrator to check server status
  4. Previous export may still be usable
  • Customer Engagement - Customer activity metrics
  • Customer Longevity - Retention analysis
  • Revenue by Tag - Segment performance
  • Monthly Recurring Reports - MRR and subscription metrics

Permissions & Access

Required Access Level: Administrator or higher

Why Administrator Access:

  • Large data exports (resource intensive)
  • Contains all customer order data
  • Potential business intelligence sensitivity
  • Server resource considerations

Best Practices

Export Frequency

  1. Generate monthly for consistent analysis
  2. Export before major reports/presentations
  3. Keep 6+ months of historical exports
  4. Document export dates for comparison

Data Management

  1. Store exports in organized folders (by month)
  2. Document any analysis performed on each file
  3. Backup important exports
  4. Delete very old exports to save space

Analysis Approach

  1. Start with simple metrics (LTV, retention)
  2. Progress to cohort comparisons
  3. Combine with other business data
  4. Share insights with team
  5. Act on findings

Things to Avoid

  • Don't run exports during peak hours
  • Don't delete recent exports (may need to verify)
  • Don't share customer data externally without permission
  • Don't compare incomplete cohorts (need time to mature)

Quick Reference Card

Task Action/Location
Create new export Click "Create Download" button
Download latest data Click "Download" in green-highlighted row
Check export status Look for email notification
Find old exports Scroll down history table
Calculate LTV Pivot: Customer ID, Sum Order Value
Analyze retention Group by cohort, count active per month
Compare cohorts Group by signup period, calculate averages
Export to Excel Click Download, open in Excel

FAQs

How often should I generate cohort data?

Monthly is recommended for tracking trends. Generate more frequently if running specific analysis or campaigns. Keep historical exports for comparison.

What's a good customer lifetime value?

Varies by industry and business model. For subscription services, LTV should be 3-5x customer acquisition cost. Track your baseline and aim for 10-15% annual improvement.

How do I calculate retention rate?

For each cohort: (Customers active in Month N / Original cohort size) × 100. Example: 100 customers in Jan cohort, 70 still active in July = 70% retention at 6 months.

Can I get data for specific date ranges?

Currently exports all completed orders. You can filter by date in Excel after downloading using the "Deliver On" column.

Why are there multiple downloads available?

Historical exports preserved for comparison and verification. Most recent (green highlighted) is usually what you want unless comparing to previous period.

What's the difference between Order Value and Box Price?

Order Value = total paid (including add-ons, fees, taxes). Box Price = base subscription price only. Order Value is better for revenue analysis.

How do I compare monthly cohorts?

Group customers by their first order month, then track performance over time. Compare metrics like retention rate, average LTV, and order frequency across months.

Can I analyze this data in Google Sheets?

Yes! Google Sheets works great. Upload CSV, create pivot tables, use formulas for calculations. Free alternative to Excel.

What's a good retention rate?

For subscription food services, 60% at 6 months and 45% at 12 months are solid benchmarks. Higher is better. Track improvement over time.

Should I include cancelled customers in LTV calculations?

Yes - LTV includes all customers, active and cancelled. This gives realistic picture of average customer value for acquisition planning.

End of Documentation

For additional help, contact your system administrator or Kiva Logic support.