Overview
Cohort analysis helps you understand how groups of new customers behave over time—specifically, how often they return and how much revenue they generate. This article walks you through how to:
Understand what a cohort is
Read and interpret the Cohort Tables
Analyze customer behavior in the Cohort Evolution Graph
Understand how Polar calculates cohort data, and how it differs from Shopify
What is a Cohort?
A cohort is a group of customers who share a common trait—typically, the month they made their first purchase. Cohort analysis helps you track and compare the behavior of these groups over time.
This allows you to answer key questions such as:
What percentage of customers return 6 months after their first purchase?
Is my customer retention improving over time?
How do promotions or product changes affect long-term customer loyalty?
You’ll find cohort tables and graphs in the Retention tab. These tools help you analyze repeat purchase behavior and identify trends across different customer groups.
🎥 Watch the video below for a walkthrough of the Retention tab and cohort tables.
How to Read the Cohort Tables
Each cohort includes customers who made their first purchase within a 30-day period starting from their initial purchase date.
Choose among 6 metrics to view cohorts in the retention tab:
Total Sales
Customers
Orders
LTV
Gross Margin
Net Sales
You can each explore the table horizontally, vertically, or diagonally to uncover different insights.
📊 Horizontally (Row by Row)
This shows how a single cohort behaves over time.
For example:
In April 2024, you acquired 1.4k new customers.
By July 2024 (3 months later), 1.90% of them made another purchase.
In non-cumulative mode, you’ll only see purchases made in that specific month.
If a customer buys twice (e.g. in November and again in January), they’ll be counted in both months separately.
📈 Vertically (Column by Column)
This shows how all cohorts behave at a specific time after their first purchase.
For example:
You might notice that Month 1 and Month 2 after purchase show the highest repurchase rates.
This insight can inform your post-purchase email flows and CRM strategies.
🔀 Diagonally (Same Purchase Month Across Cohorts)
Looking diagonally shows trends during specific periods like holidays or promotional campaigns.
For example:
If March shows unusually high repurchases across several cohorts, a successful campaign likely drove that behavior.
You can switch between percentage and absolute values using the toggle at the top-right corner of the cohort table.
⚠️ Note: The cohort analysis only considers sales from new customers, not all sales made in a month.
Filtering and Customizing Your Cohort View
You can take your cohort analysis further by applying filters and customizing how data is displayed.
Filter Options:
You can break down your cohorts by:
Product collection – Based on the first item a customer purchased
Acquisition source – Using UTM tags or data pulled from Klaviyo
Shopify filters – Such as customer tags, geographic location, or customer type (new vs. returning)
These filters help answer questions like:
Which product categories drive the highest repeat purchase rates?
Do customers acquired via Meta ads return more frequently than those from email?
How does retention vary across different customer segments?
View Options:
You can also toggle the way cohort data is visualized:
Cumulative vs. Non-Cumulative View
Cumulative: Shows the total percentage or revenue of customers who have made at least one repeat purchase by that month.
Non-Cumulative: Displays only the customers who returned in that specific month.
Absolute vs. Percentage Values
Toggle between actual counts (e.g., number of customers or dollar amounts) and percentage-based views to better understand the scale and behavior across cohorts.
These toggles are located in the top right-hand corner of the Cohort Table.
How to Read the Cohort Evolution Graph
The Cohort Evolution Graph shows how the cumulative lifetime value (LTV) grows over time for a specific customer cohort.
Month 0: The customer’s first 30 days from their initial purchase.
As you move along the timeline, you’ll see how much more revenue that cohort has generated.
Hover over each point to view exact values and the total number of customers in that cohort.
Add breakdowns & top segments to identify how customers interact with your store after a purchase & what revenue they drive over time.
The customer count stays the same across time—only the LTV value increases as more purchases occur.
Polar vs. Shopify Cohort Calculations
There’s a key difference in how Polar and Shopify define months in cohort analysis:
| Polar | Shopify |
Month 0 | Starts on the exact purchase date | Starts on the 1st of the month |
Month progression | Every 30-day period from first purchase | Calendar months |
Example:
A customer purchases on November 15th.
In Polar:
Month 0: Nov 15 – Dec 14
Month 1: Dec 15 – Jan 14
Month 2: Jan 15 – Feb 14
In Shopify:
Month 0: November
Month 1: December
Month 2: January
Cumulative vs Non-Cumulative Views
Let’s say the same customer makes additional purchases on December 1 and January 28:
In cumulative view, they are counted as returning in:
Month 0, and Months 2, 3, 4, 5, etc.
In non-cumulative view, they are counted as returning in:
Month 0 and Month 2 only
(because those are the specific 30-day windows in which purchases occurred)
This calculation method gives you a more precise view of customer behavior based on actual lifecycle timing—not just calendar months.
This difference means Polar’s cohort percentages may vary slightly from Shopify’s.
Want to recreate Shopify-style cohort tables in Polar?
Use the Custom Reports feature with a filter on Customer First Order Date, and set your date range to one calendar month.
In this screenshot, you'd be viewing Shopify's "Month 1" cohort from the example above.
