Fun with Customer Cohorts
Fun with Customer Cohorts
Cohorts are a powerful tool for comparing one group of prospects to another. Look at their start date, close date, or other items to get a group. Read: "Everything Starts Out Looking Like a Toy" #211
Hi, I’m Greg 👋! I write weekly product essays, including system “handshakes”, the expectations for workflow, and the jobs to be done for data. What is Data Operations? was the first post in the series.
This week’s toy: a soundboard for making medieval music. No, can’t think of a reason to use this outside of a renaissance festival or a revival of Spinal Tap: The Movie … but it’s awesome. Edition 211 of this newsletter is here - it’s August 12, 2024.
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The Big Idea
A short long-form essay about data things
⚙️ Fun with Customer Cohorts
I remember when I first saw a chart like the one above - it seemed like magic to compare groups of users to other users at the same point in their journey. This cohort analysis is useful for looking at repeated passes through an initial trial and expansion growth and lifetime value of these customers over time.
By cohort analysis, we are referring to a group of users and customers in the same stage of their customer journey, like:
trialers of a SaaS product in their first week of a trial
customers in their third month after purchase
customers who have been with us for longer than a year who have not spent more money but continue to be happy
The goal of reviewing cohorts is to see if the effects a group sees at the same point in their journey differs from the effects another group sees at different points in time. We might ask questions like “is our initial conversion rate improving among trialers” or “how many customers do we expect to get in a cohort over a 3 month period from inception.”
Using Cohorts for Initial Funnel Analysis
One of the most effective ways to use cohorts in your GTM efforts is to watch the conversion rates from cohort to cohort. Whether you do this weekly, monthly, or by the day of the cohort in the trial, you get a fixed point to compare:
How many prospects entered the cohort on a day/week/month
How many of these prospects eventually converted to the next stage
How many converted prospects became customers
It’s really that simple. Comparing the conversion rate for a cohort helps you to know how the current group is behaving, and helps you measure the impact of changes to that part of the funnel.
Measuring Cohorts by ARR
Tagging individual customers by cohort also lets you filter them as a contributor to ongoing revenue. In a subscription business, net new ARR tells you about your new customers, and the expansion ARR in a period informs you of increasing contribution from current customers.
This “layer cake” of revenue stacks over time and the existing customer slice becomes more important (or equally as important) relative to new ARR. Expansion revenue is a lot easier to gain and more profitable than new revenue, since it’s adding to existing money teams are already paying.
Here’s an example chart showing this effect on a mature business like Slack:
When expansion revenue + reactivation revenue (customers who return after having left) exceed the combination of contraction and churn revenue leaving the business), it means less pressure on new revenue to be the only driver of the business.
For example, introducing a product add-on to an entire customer base lets you expand revenue not only with new customers but also with existing ones.
Seeing the lifetime value of cohorts
The chart below shows an e-commerce business sliced by the year they acquired their customers. Each stack shows the current revenue with a stack bar indicating the year that a customer was acquired. This is an example of looking at revenue aging, where you understand what percentage (and absolute number) of your revenue comes from existing customers.
Understanding the expected lifetime value and tenure of a customer will assist in deciding which slices of the revenue might be at risk depending upon their age and composition. If your numbers in any one cohort are too small it may be hard to get a statistically significant result, but you get the general idea here. Inspecting each stack and knowing how close or far away the average account is from the rest of the accounts is telling.
Finally, having cohorts in your ARR stack demonstrates the value of the average cohort and whether it’s increasing or decreasing over time. Increasing value per cohort and expansion within the cohort = success.
What’s the takeaway? Cohorted groups of prospects or customers provide valuable insights into the performance of customer journeys over time. When cohort metrics trend positively it confirms that part of your GTM effort is working! When these numbers trend poorly, it’s time to look critically at your funnel, while knowing things won’t change immediately.
Links for Reading and Sharing
These are links that caught my 👀
1/ Airlines are running out of numbers - Commercial flights are based on very old systems that depend upon 4 number flights (xxxx). This means that airlines need to be more creative in designating flight numbers. This raises the question of how many systems expect only numbers and not other characters. Putting aside for the moment the potential confusion of boarding Flight A123, adding a few letters (even just a leading letter) would give them a lot of headroom.
2/ Music stardom is fleeting - Music critics often talk about “one-hit” wonders as examples of artists who have an initial success and then are not heard from again in the music space. It turns out that (unsurprisingly) that commercial music success follows a power law distribution. It’s very hard to continue producing hits continuously (or even for more than a few years).
3/ Thoughts on doodling - The next time someone tries to tell you that “doodling” or distracted drawing is a sign of inattention, send them this article on the benefits and history of doodling.
What to do next
Hit reply if you’ve got links to share, data stories, or want to say hello.
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The next big thing always starts out being dismissed as a “toy.” - Chris Dixon