Analyze customer retention, lifetime value, repurchase rates, and acquisition ROI by cohort in MerchantFlow. Group customers by first purchase date.

Customer Cohort Analysis

Cohort analysis in MerchantFlow groups your customers by the date of their first purchase, then tracks how each group performs over subsequent periods. This reveals customer retention trends, lifetime value (LTV) trajectories, repurchase behavior, and which acquisition periods produce your most valuable customers.

What Is Cohort Analysis?

A cohort is a group of customers who share a common characteristic -- in MerchantFlow, they are grouped by the week, month, or year of their first order. By comparing cohorts side by side, you can answer questions like:

Are newer customers spending more over time than older ones?
Is my retention rate improving or declining?
Which marketing campaigns acquired the highest-LTV customers?
How quickly do I recover my customer acquisition cost?

How Cohort Analysis Works

MerchantFlow automatically groups customers into cohorts based on their first order date. You can then track metrics like revenue, gross profit, repurchase rate, and ROAS across subsequent periods without any manual setup.

How to Access Cohort Analysis

Navigate to Customers > Cohorts from the main navigation.

How to Configure Your Cohort View

Grouping Options

Weekly -- group customers by the week of their first purchase (best for short-term campaigns)
Monthly -- group by month (default and recommended for most analysis)
Yearly -- group by year for long-term trend analysis

Metric Options

Revenue -- total revenue generated by each cohort over time
Gross Profit -- revenue minus COGS for each cohort (requires COGS setup)
Repurchase Rate -- percentage of customers in the cohort who ordered again
ROAS -- return on ad spend for each cohort (requires ad platform connections)

Display Mode

Accumulated -- running totals across periods (recommended for LTV analysis)
Period Only -- values for each specific period only (useful for spotting period-specific spikes)

Calculation Mode

Per Customer -- average per customer in the cohort (normalizes for different cohort sizes)
Whole Cohort -- total for the entire cohort (shows absolute impact)

Time Range

Choose from 7 days, 30 days, 90 days, 1 year, 2 years, or all time.

How to Read the Cohort Matrix

The cohort matrix is a color-coded grid where:

Rows represent cohorts (e.g., "Jan 2026" = customers who first purchased in January 2026)
Columns represent periods after acquisition (P0 = first period, P1 = second period, P2 = third period, and so on)
Cell values show the selected metric for that cohort in that period
Color intensity indicates relative performance (darker = better)

Example interpretation: If the "Jan 2026" row shows $50 in P0 and $15 in P1 (accumulated revenue per customer), it means the average January customer spent $50 in their first month and an additional $15 in their second month.

Understanding the Summary Table

Below the matrix, a summary table shows key metrics for each cohort:

Column	Description
Cohort Label	The acquisition period (e.g., "Jan 2026")
New Customers	Number of first-time buyers in that period
CAC	Customer Acquisition Cost for the cohort
Current LTV	Current lifetime value per customer
Payback Period	Number of periods until CAC is recovered
ROAS	Return on ad spend for the cohort
Margin %	Profit margin percentage for the cohort

What Insights to Look For

Improving LTV -- are newer cohorts accumulating more revenue over time than older ones?
Repurchase trends -- is your repeat purchase rate increasing across recent cohorts?
Payback period -- how quickly do you recover your customer acquisition cost? Shorter is better.
Seasonal patterns -- do holiday cohorts have different LTV or retention characteristics?
Marketing effectiveness -- which campaign periods produced customers with the highest LTV and best retention?

Tips for Effective Cohort Analysis

Start with Monthly grouping and Revenue metric for a broad overview of customer behavior
Use Per Customer mode to normalize across different-sized cohorts and compare fairly
Compare Accumulated Revenue across cohorts to identify your best acquisition periods
Look at P1 through P3 repurchase rates to evaluate whether your retention strategy is working
Cross-reference cohort data with your Attribution reports to connect marketing campaigns to long-term customer value

Frequently Asked Questions

How far back does cohort analysis go?

Cohort analysis covers all historical data available in your MerchantFlow account, going back to when you first connected your Shopify or WooCommerce store.

What is a good repurchase rate for e-commerce?

Repurchase rates vary by industry, but a P1 (second-period) repurchase rate of 20-30% is typical for consumer goods e-commerce. Subscription-based businesses often see higher rates.

How does cohort analysis help with marketing budget decisions?

By comparing CAC, payback period, and LTV across cohorts, you can identify which acquisition periods (and associated campaigns) deliver the best long-term ROI -- then allocate future budget accordingly. See Ads and Channels for campaign-level data.

Can I see cohort data for specific products or channels?

The current cohort view groups all customers by first order date. Product-level and channel-level cohort filtering is on the roadmap.

What is the difference between Per Customer and Whole Cohort mode?

Per Customer divides the total by the number of customers in the cohort, giving you an average. Whole Cohort shows the raw total. Per Customer is better for comparing cohorts of different sizes.

Related Guides

Attribution -- connect campaigns to cohort performance
AI Insights -- ask questions about cohort trends
P&L Overview -- financial context for cohort profitability
Dashboard Overview -- navigate the full MerchantFlow experience

Last updated: March 14, 2026