Cohort Comparison Report | Klar Knowledge Base

tl;dr

The Cohort Comparison Report allows you to cluster your customers into different cohorts based on past purchase behaviour.
These cohorts can then easily be compared as the report condenses the most important retention metrics into one easy to read chart and graph.
This allows you to clearly identify what behaviour or characteristics are driving long-term retention and therefore profitability.

⚡ Since this is a retention report, the Date Range you define in this report select customers that were acquired in that period, but all activities until today are used to calculate the values. More Information here - Date Range Definition

How does it work:

This report condenses the most important retention metrics and allows you to compare different customer segments based on various dimensions relating to their first order.

You can select a primary and (if you want) a secondary dimension that you want to use to define your customer groups. The available dimension are:

Product:

Product Title - groups customers based on which Product they ordered in their first order.
Product Variant Title - groups customers based on which Variant they ordered in their first order.
Product Type - groups customers based on which Product Type they ordered in their first order.
Brand - groups customers based on which Brand they ordered in their first order.

Discount:

Discount Type - groups customers on whether they used a discount in their first order and if so what type.
Discount Value Type - groups customers on whether they used a discount in their first order and if so whether it was a percentage or value discount.
Discount Value - groups customers on whether they used a discount in their first order and if so what it value was. To probably interpret, use it in conjunction with the Discount Value Type.
Discount Taget - groups groups customers on whether they used a discount in their first order and if so whether it was applied on products or shipping.

Marketing:

UTM Source - groups customers based on the utm source of their first order based on Shopify's attribution logic (last-click)
UTM Medium - groups customers based on the utm medium of their first order based on Shopify's attribution logic (last-click)
UTM Campaign - groups customers based on the utm campaign of their first order based on Shopify's attribution logic (last-click)
Email Subscription Status - groups customers based on whether or not they have opted-in to receive email communication from you and splits these up based on whether or not that permission was given before or after the first order was placed.

Other:

Customer Tags - groups customers based on customer tags
First Order Tags - groups customers based on which Order Tags of their first order
Shipping Country Code - groups customers based on where their first order was sent.

⚡ A single customer can fall into multiple groups within one dimension. For example, if a customers orders two products in my first order - one shoe and one t-shirt, his following buying behaviour will be included in both Product Types.

What can I analyse:

The Cohort Comparison report offer different metics and controls so that you can display it exactly the way you need it.

Metric Summary:

Total - Shows you the total sum of the activities that took place.
Per Customer - Divides the total by the number of customers in the cohort.

Both of these are useful. To get a better understanding of the quality of a cohort, you should look at it Per Customer as this removes the impact from cohorts having different sizes. To see the overall impact in your business and your bottom line, look at the Total value.

Metrics

Customers - Number of customers in the cluster
Time on Books - average elapsed time since placing the first order
Repurchase Rate - % of customers in the cluster that placed a second order.
2nd Order Time Lag - Average elapsed between the first and the second order of a customers in a cluster.
Orders - Total Number of Orders placed by the cluster
Net Revenue - Total Net Revenue (so excl. VAT) from the cluster
First Order Net Revenue - Net Average Order Value of the first order
First Order CM2 - Average Contribution Margin 2 of the first order
90 Day CLV - Average CM2 within the first 90 days after placing the first order
365 Day CLV - Average CM2 within the first 365 days after placing the first order
CLV - Average CM2 of the cluster up until today

What can I use this for?

The Cohort Comparison Report is the more operational version of the Cohort Report. It allows you to compare th

Here are some examples of where this can be extremely useful:

What Product/Product Variants are driving retention

The first product your customers are ordering often has a significant impact on their retention.

For example. a Taster Bundle might be great way to get new customers because it's cheaper but it also may not be enough to experience your core value proposition and therefore retention is much lower than for people who buy a larger package. So paying more money in the acquisition upfront may actually be a worthwhile investment.

What Products might need improvement

Similar to the above, poorer retention from certain variants might also show you that the product is simply not as good as the others.

Customers have a different taste than you might have. So when you have different colours and flavours you can analyse their retention and highlight the ones that are lacking to your Product Development department so that they can be improved.

What Influencers/Marketing activities are bringing me profitable customers

Influencers are often giving discount codes so that they can incentivize their audience to place an order. This allows you at the same time to track the quality of the customers that they are bringing you in.

Before, you probably only analysed their immediate Customer Acquisition Costs (CAC) and Return on Ad Spend (ROAS). With the Cohort Comparison Report you can actually analyse the quality of their audience and instead to a CAC to CLV analysis.

Maybe two influencers have the same CAC, but when looking at the CLV one clearly outperforms the other. That's the one you should aim to deepen the relationship with.
You can identify influencers that you discarded because their CAC was over what you are willing to spend. Now you realise that the CLV of their customers they bring is way above average and therefore, even when their CAC is higher, they are highly profitable.
It also works the other way around. Influencers with a CAC within your targets range only generate customers with a way below CLV and therefore are not profitable. Thus, stopping the Partnership might be the best decision.

The same analysis can also be done with the utm Parameter. The difference here however is, that you only get the analyse a subset of the traffic as there is no 100% attribution as you have with Influencers through Voucher Codes.

💡 The same analysis can also be done with the utm Parameter. The difference here however is, that you only get the analyse a subset of the traffic as there is no 100% attribution as you have with Influencers through Voucher Codes.

What kind of discount code works bests

Did you ever wonder? Does it actually make a difference when I offer my customers 10% on their first order or €5 with a minimum spend. It just might. The Cohort Comparison with it's discount dimensions give you then insights you need in order to put together an effective and profitable incentivation strategy for your new customers.

What country is the most profitable for me

When you ship from one shop to different countries, it also make sense to analyse whether there are any noticeable differences in the retention behaviour of the different countries.

Just select the Shipping Country Code dimension. There can be big difference in the Logistics Costs between different shipping countries so make sure that you have a detailed configuration of these costs in our custom costs section so that you can effectively compare long-term profitability.

Revenue & CLV Extension Graph

If you want to look at the core metrics from the above graph visually, you can simply scroll down a bit to this graph.

It takes the same 25 records shown in the table above and visually shows you how the Net Revenue and Customer Lifetime Value of these cohorts extends over time: