Product Analytics 101: User/company-level retention
In this 4-part series, I outline four common metrics or charts I use in my day-to-day work, and how I derive actionable insights from them. I’ll also give you a little peek into the product analytics tech stack I use for B2B startups.
User or account-level retention (today’s post!)
Feature adoption and retention
Defining retention: What does healthy retention look like for our product?
Before analysing your retention rates, you should understand what user behaviour you’re expecting for the different personas, roles, and ICPs you serve.
Do you expect users to trigger certain events daily, weekly, or monthly? Compare yourself to the direct competition, or to your customers’ current alternative, which might be in a different product category.
A healthy, flattening retention curve
Here’s an example of a relatively healthy retention chart for a B2B SaaS company:
The relatively steep drop-off in the first days is expected and lines up perfectly with a 25% median activation rate. If most users don’t activate, they also won’t become recurring users. Especially if you’re operating in a red ocean with lots and lots of competition, there will naturally be plenty of users who never come back after trying your product once.
We want our retention curve to flatten at some point. This signifies that there’s a subset of users getting continued value out of your product - a clear indicator of product-market fit.
There are lots of industry benchmarks for where your retention rate should flatten out. June.so indicates that appr. 20% of new users should become steady recurring users.
But the reality is heavily impacted by a wide range of factors.
Think about it: Your retention rate is calculated by dividing the number of active users after a certain time frame divided by the total number of users at the beginning of the period. If you run a crazy marketing campaign that boosts your customer acquisition but brings in the wrong kind of users, it will harm your overall retention rate, although your product remains the same.
By combining customer acquisition costs with the % at which your retention rate flattens out and your Average Revenue per User or Customer, you can calculate whether you’re running a sustainable business model.
Cohort retention
Cohort retention is far more interesting than a static retention rate.
For specific acquisition cohorts (e.g. all new users that joined in the week commencing 20.05.24) I look at how many of these new users are still active after a specific number of days, weeks, or months - whichever interval is relevant for my product.
Now I can quickly see
Whether retention is increasing or decreasing over time skimming down any column to see if the numbers are trending up or down.
Where the biggest drop-off in retention is happening. Is this drop-off expected or normal, or is there an issue we need to address?
Whether something went very wrong or very right for a cohort. This can be worth exploring and repeating (or avoiding).
Looking at cohort retention often leaves us wondering “why…”.
I always supplement quantitative data with qualitative data, such as user interviews, shadowing sessions, session recordings, etc.
Using retention data to identify your power users and your beachhead segment
The #1 most useful exercise with retention data is to build a deep understanding of who your best retaining users are.
Can you identify a target audience segment that’s more successful than your overall user base? Why is that? Is there some connective tissue between them?
How can you get more of these types of users, or help more new users become as engaged as they are?
Most product analytics tools allow you to build audience lists for your best-retained users and run feature usage reports for those users vs. regular users.
1. Build lists for your best-retained users, so you can trigger short in-app surveys to them, or even better - reach out to hop on the phone. User interviews can give you great insights into why these users tried your product in the first place, why their alternative solution wasn’t cutting it, and what they love about your product.
2. Run feature usage reports usage or analyze paths for your best-retained vs. ‘regular’ or poorly retainer users. You might find out that your power users are uncovering a certain feature that others aren’t, in which case you might consider guiding everyone to that specific feature (a potential Aha).
A deep understanding of who your power users are, what drives them to your product, and what keeps them coming back, is everything.
Identify your beachhead segment - the subsegment of your target audience that is in the most excruciating pain without your solution, where you can establish market leadership.
Lower your CAC/payback period by targeting your ideal users more specifically (less spray and pray, higher retention rates) and improving your website conversions by honing in your messaging.
Uncover which other steps on the job map, Jobs to be Done, or problems you can solve for them to inform your product roadmap (product/customer expansion)