tl;dr
The Cohort Comparison report lets you cluster customers into groups based on their first order behaviour — by product, discount, marketing channel, country, and more
Customer Lifetime Value (CLV) is the core metric. The report shows you which customer groups generate the highest CLV over 90 days, 365 days, and all time — so you can invest more in what drives long-term profitability
When you have configured your cost datasources, you can also use CLV to set CAC targets: find out what a customer is worth and decide how much you can afford to spend acquiring them
This helps you identify which products, discount types, marketing activities, and countries are driving retention — and which aren't
⚡ Date range note: The date range you define selects customers acquired in that period — but all their activity up to today is used to calculate the values. More on date range definition
Revenue & CM2 Extension Graphs
At the top of the report, the extension graphs visualise how Net Revenue and CM2 develop across four time points for each of your customer groups:
First Order
Within 90 days
Within 365 days
All Time
Use the Net Revenue / CM2 toggle in the top right of the graph to switch between the two views.
How does it work?
This report condenses the most important retention metrics and lets you compare different customer segments based on various dimensions relating to their first order.
You can select a primary and (optionally) a secondary dimension to define your customer groups — and combine any two dimensions to slice your data exactly how you need it.
Marketing channels:
Channel Category — groups customers by the category of the channel their first order came from
Channel Group — groups customers by the channel group (Paid, Brand, Owned) of their first order
Channel Name — groups customers by the specific channel name of their first order
UTM Source — groups customers by the UTM source of their first order
UTM Medium — groups customers by the UTM medium of their first order
UTM Campaign — groups customers by the UTM campaign of their first order
Customers:
Customer Segment — groups customers by their configured customer segment
Customer Tags — groups customers by customer tags
Email Subscription Status — groups customers by whether they opted in to email marketing, and whether that happened before or after their first order
Product:
Product Titles — groups customers by which product they ordered first
SKUs — groups customers by which SKU they ordered first
Discount:
Discount Codes — groups customers by the discount code used on their first order
Discount Target — groups customers by whether the discount was applied to products or shipping
Discount Type — groups customers by whether they used a discount and what type
Discount Value — groups customers by the discount amount. Use with Discount Value Type for correct interpretation
Discount Value Type — groups customers by whether the discount was a percentage or fixed value
Order:
First Order Month — groups customers by the calendar month of their first order
First Order Quarter — groups customers by the quarter of their first order
First Order Tags — groups customers by the order tags on their first order
Payment Method — groups customers by the payment method used on their first order
Shipping Country — groups customers by the country their first order was shipped to
⚡ Note: A single customer can fall into multiple groups within one dimension. For example, if a customer orders a shoe and a t-shirt in their first order, their subsequent behaviour is counted in both product groups.
What can I analyse?
Metric Summary:
Per Customer — divides the total by the number of customers in the cohort. Use this to understand the quality of a cohort — it removes the impact of cohorts having different sizes
Total — shows the total sum. Use this to understand the overall impact on your business and your bottom line
Both are useful. Per Customer gives you the better comparison between cohorts of different sizes. Total shows you the absolute business impact.
Metrics:
You can select any combination of the following 32 metrics via the Metrics button:
Customers & orders:
Repurchase rates:
Rep. Rate — overall repurchase rate
30d / 60d / 90d / 180d / 365d Rep. Rate — % of customers who placed a second order within the respective timeframe
Revenue (CLR — Customer Lifetime Revenue):
First Order — Net Revenue from the first order
30d / 60d / 90d / 180d / 365d CLR — cumulative Net Revenue within each timeframe
CLR — total Net Revenue from the cluster up until today
Profitability (CLV — Customer Lifetime Value):
First Order CLV — CM2 of the first order
30d / 60d / 90d / 180d / 365d CLV — cumulative CM2 within each timeframe
CLV — total CM2 from the cluster up until today
CLV growth:
% CLV Delta 30 / 60 / 90 / 180 / 365 — percentage growth in CLV between timeframes, showing how quickly customers become more valuable
What can I use this for?
The Cohort Comparison is the operational version of the Cohort Analysis. Where the Cohort Analysis shows you how retention evolves over time, the Cohort Comparison lets you ask: why are some customer groups more loyal than others?
What products or variants are driving retention?
The first product a customer orders often has a significant impact on their retention. A Taster Bundle may be great for acquisition because it's cheap — but if customers don't experience your full value proposition, their retention may be much lower than customers who buy a larger package. Paying more per acquisition might actually be the better investment.
What products might need improvement?
Lower retention from certain variants can signal that the product simply doesn't meet customer expectations. Use the Product Variant dimension to surface underperformers and flag them for your product team.
What influencers or marketing activities bring profitable customers?
Influencers typically give out discount codes, which means you can track the quality of the customers they bring. Instead of only looking at immediate CAC and ROAS, use the Cohort Comparison to evaluate the CLV of their audience:
Two influencers with the same CAC may generate very different CLVs — work harder with the one whose customers return more
An influencer whose CAC exceeds your target might still be profitable if their customers' CLV is well above average
An influencer within your CAC target might actually be unprofitable if their customers' CLV is far below average
The same analysis works with UTM parameters — though keep in mind that UTM attribution only covers a subset of traffic, not all orders.
What discount structure drives the best customers?
Does a 10% discount on the first order produce more loyal customers than a €5 voucher with a minimum spend? The Discount dimensions let you find out and build a more profitable incentive strategy.
Which country is most profitable?
If you ship to multiple countries from one store, use the Shipping Country Code dimension to compare the retention behaviour and profitability by market. Make sure your Logistics Costs are configured per region in Klar so the CM2 comparison is accurate.