Deep Dive into Activation and Retention
Actionable frameworks and examples
Hey there, it’s Jacob at Retention.Blog 👋
I got tired of reading high-level strategy articles, so I started writing actionable advice I would want to read.
Every week I share practical learnings you can apply to your business.
What does good retention mean?
It means you’re building a habit.
I like to work backward to figure out what leads to that habit.
It’s easier to define what an active user looks like versus figuring out the right path to get an active user.
But once we have that definition, we can figure out the steps that must come first.
Let’s say we have a meditation app and our active user definition is meditating 3x/wk.
We want to get our new users to that point as fast as possible to start forming a habit.
We can even set KPIs around how quickly we get users to that…”Habit Moment”
Let’s say our metric is the percentage of new users doing 3 meditations in the first 7 days.
It’s not like users can go straight to that level of activity.
They need to understand and experience the value of our product before they’re going to use it multiple times per week.
When a user first experiences the core value, it’s often called the “Aha Moment”
This could be as simple as a user completing one meditation.
This “Aha Moment” has to happen before a user can build a habit.
Before this, we have a setup process for what the user needs to accomplish to reach that “Aha Moment”
A “Setup Moment” could be one specific action they need to complete, or could it be all of the onboarding steps. Or account creation. Or something else that sets up the user to experience value.
How do you find your Setup, Aha, and Habit Moment?
Start by brainstorming what actions or events may lead to longer-term retention
If you have a definition of an active user, you can start there
Find the action that most strongly correlates with retention
Look at different frequencies of the events too e.g. meditate 1x/wk, 2x/wk, etc.
Next, figure out what action needs to happen first before that Habit Moment
It may be obvious, or it may take more data analysis
From that Aha moment, list out all the steps needed to get there
This is your Setup Moment or setup steps
You may need support from a data analyst to run regression analyses, correlation analyses, or other models to understand these events’ predictive power.
If you’re a paid subscriber to Lenny’s Newsletter, this post may be helpful. And here is another link: How to pick your activation metric by Henrique Cruz.
Your ultimate goal is to find a metric that is *causal* to longer-term retention.
At first, you can really only find metrics that correlate with retention.
The next step is to run experiments that are focused on improving the potential activation metric that strongly correlates with retention.
And then if you move that activation metric and retention also moves, you may have a winner!
Does this sound hard and you don’t think you have enough data? Well, then it might not be the right time for you to focus on this.
I’d say nailing down a specific activation metric is a mid-stage strategy that should come after you’ve figured out how to grow and are seeing some success.
You need to create something people actually want first. Figure out how to grow it a little before you start trying to systemize that growth.
Wait Jacob, you first started talking about an “Aha moment” and then mentioned “Activation” - are those the same thing?
Good question. Maybe…sometimes…
Different companies use different definitions of “Activation.” Sometimes activation is the same as the Aha moment. Sometimes activation is completing the Aha moment and the Habit moment.
The main goal is for your to have a metric that is predictive of retention: “If more users hit my activation point, it’ll increase retention and monetization.”
We have a path for how to improve retention, but how do we actually measure retention?
Unfortunately, there isn’t one simple metric.
There are so many!
Here are a few of the most important charts to use:
You need to know your D7, D14, D28 retention metrics
What’s the best way to improve long-term retention? Improve Day 28 retention.
What’s the best way to improve Day 28 retention? Improve Day 7 retention.
What’s the best way to improve Day 7 retention? Okay, I think you get it now…yep, improve Day 1 retention.
You need to understand how retention differs by platform and other key segments
Retention will differ by iOS and Android and Web
You should also understand how retention differs by geo
Does retention differ by age or gender? Does retention change based on other user attributes? If you don’t know the answer to these, it’s time to find out :)
You also need to understand what user behavior and actions correlate with retention.
You can’t change a user’s attribute, but you can influence their behavior in the app!
And this type of discovery (segmenting users by the actions they perform on a retention chart) is actually the first step in figuring out your activation metrics and moments.
For example, we can see the purple line for “app event 3” has the strongest correlation with retention. This would be a good starting point for brainstorming a possible activation/aha moment
I love cohort analyses. I think they’re one of the most valuable chart tables to understand your user base.
I don’t love cohort analysis when I have to code them myself with SQL, but that’s a different story…
So why are they so great?
It allows for a granular view of new cohorts coming into your product and you can understand the “health” of each new group of users.
I typically group the new user cohorts by week and then use a combination of days since they joined and weeks since they joined.
I can easily see the trends over time for retention
I can see if there may be an issue with acquisition campaigns helping or hurting retention
I can look back and see if past marketing has positively or negatively affected user’s long-term retention
I can connect early strong retention for a new cohort to long-term success to predict how that may affect things like renewal rates
Over time I can extrapolate how a win in Day 7 retention may impact Day 90 retention or 6-month retention
If you aren’t familiar with this chart, I would set one up in your BI tool and get in the practice of checking it every week on the same day to better connect your work to how it impacts your user base.
There is no one metric to tell you everything you need to know about retention, but you can set up dashboards that give you a decently complete view.
Here’s what I’d include to start:
Day 7, Day 14, and Day 28 retention metrics
and include a WoW or MoM comparison. The goal is to quickly understand if they’re trending up or down.
Longer-term retention measures like Day 90 or Day 180
Retention charts based on your core activity metric
Difference in free vs. paying user retention (if applicable)
Retention segmented by acquisition source (and maybe paid vs. organic)
Retention segmented by geo
Retention segmented by device type
Retention segmented by user goals (or other important attributes)
You also want to track the leading indicators of retention rates.
Any guess what those are? We talked about them above…
Yep! These are things like your Setup moment, Aha moment, and Habit moment completion rate.
Set up some charts to understand how these conversion rates are changing over time.
Often, I find the most success by focusing on activation and the onboarding experience.
Once people have made up their minds on a product, it’s hard to change.
But getting ahead of it and helping them decide early is a much easier way to influence behavior!
To think about it quantitatively, if we move day 1 retention rates up by 5%, that lifts the entire retention curve for all new users. Compared to if we move retention rates up 5% for users on day 90, that impacts a much smaller percentage of users.
The more time you invest in understanding your user base, the better results you’ll see.
You need to figure out the issues first.
Start with brainstorming possible reasons why users aren’t reaching your activation point.
The app is using the wrong type of copy or verbiage, and users don’t understand the problem.
The real value of the product is not what you think, so you need to get users to do different actions.
It’s not a one-size-fits-all all approach, and you need to have more segmentation or personalization.
Your new user experience is too complex or confusing and people get lost.
From all of those hypotheses, you can narrow them down a bit with user research.
Customer interviews: The Jobs To Be Done framework is a great one to start with
Another Lenny’s plug…Bob Moesta, co-creator of JTBD, has an awesome podcast with Lenny here. Listen to this before you do any more user research
Aim for 5 interviews to start. You need fewer than you think to get a pretty full spectrum of experiences
Feedback and Reviews
This is free and you already have it! Don’t ignore them
Email surveys
Get in the habit of answering your burning questions via research
Next, validate your user research findings with quantitative analysis
You started with a bunch of hypotheses and ideally, narrowed these down a bit by talking to customers.
From this shortlist, figure out which ones are actually true with data
Users will tell you a lot of things, but their actual behavior doesn’t always reflect what they say
Once you’ve done your quantitative and qualitative research and analysis, you can finally start prioritizing test ideas!
Feel free to skip these steps if you don’t want successful experiments
When is the last time you did customer interviews?
More than a year? Never?
If you haven’t done interviews or real user research recently, stop reading and get that process going. Talking to your customers will drive more impact than any newsletter post…what are you still doing here?!?
You made it this far!
🎁 Time for your reward with real examples!
Finch users engage with the app, which allows them to send their self-care pet on a journey. Their finch returns in 8 hours and you get a reward if you open the app when it returns.
If you wait too long to check in, you miss out on the post-journey reward, incentivizing users to come back in timely intervals
The 8 hours is especially interesting because it gets the user to come back at different parts of the day.
We’re different people in the morning and at night. Morning Jacob is excited for the day and ready to take on anything! Evening Jacob is tired and wants to sit on the couch and watch TV…
If I can get both “Jacobs” to engage with an app, I stand a much better chance of retaining Jacob
You can think of getting someone to use your app at multiple times of day as similar to expanding use cases, which we understand drives better engagement
Read more about how to create engagement/retention loops from Nir Eyal’s book Hooked. Reforge also should be credited for driving growth and retention loops into the modern growth vernacular.
A simple, but good one. Your app home page sucks.
It’s complex and confusing and no one knows what to do next or first.
You should either have a custom new user experience that bypasses the home page and takes users right to the “activation” experience.
Or, you should do something like Flo and integrate this guidance into the UI so there is no chance new users don’t know what to do.
Don’t use tooltips, they get in the way. Create a better UI so the guidance is built-in.
Well, actually you can use tooltips. But only once it’s obvious a user isn’t going to do what you want.
For example, you wait some amount of time or if a user is tapping around a bunch and confused, then trigger tooltips because they need a helping hand.
See my entire breakdown of Flo’s onboarding here
You ask all these questions during onboarding, but do you do anything with them?
Balance found that by asking users who selected Sleep as a goal if they were reading to sleep now, they could better personalize their recommendations.
If a user selects Sleep as a goal, they immediately ask if they need help falling asleep now. If you need better sleep, but you discovered the app on your lunch break, it doesn’t make much sense to recommend a sleep meditation.
Figure out how to better personalize the new user experience based on user data.
Lastly, you can extend these activation tactics into your lifecycle marketing (email, push, SMS).
Strava knows that getting a user to record an activity is the first step toward becoming an active and retained user. This is the most important next step.
All of their first push notifications are different tactics driving towards that key experience.
myfitnesspal does something similar. They know that tracking your food is the most important first step for new users.
Tracking your food is predictive of longer-term retention for myfitnesspal, so all the early messages are driving towards that if you haven’t done it yet.
🏁 Okay, that’s all I’ve got. You made it to the end!
I think that was a pretty good post, what do you think?
Here’s a quick summary of everything we went over:
Summary
Start by measuring your current retention
Figure out your setup, aha, and habit moment
Track these metrics to understand where to focus
Perform qualitative and quantitative research to inform experiments
Focus on improving the path toward activation to improve retention
Marketing plays a role, but product changes are often more impactful
Use engagement loops to create early “hooks”
Guide users and make the next step obvious
Use early data to create more personalized experiences
And extend the product value through lifecycle marketing
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