How To Drive Your User Adoption Metrics [In-Depth Guide]
If you don’t know which of your new users are going to turn into paying customers and which are going to drift away, you need to be thinking about User Adoption Metrics.
Improving your metrics and pushing new users to the Activation milestone is critical to the long-term growth of your SaaS business. If learning how to measure and improving your user activation is on your agenda – you’ve come to the right place.
In this post, we’ll examine how you can drive your User Adoption Metrics forward
- Why is User Activation so important?
- Find the User Adoption Metrics that Matter
- Define ‘Activation’
- How You Can Increase Activation Rates and Cut Churn
- User Adoption Metrics – In Conclusion
Why is User Activation so important?
For SaaS product managers, getting new people to try out your app is only half the battle. As we’ve mentioned before, many companies struggle to convert 25% of free trialists into paying customers.
Gen Furukawa of Retainable shows perfectly why this matters in his recent Product Drive talk. If you can stop users dropping off early on in the customer journey, you increase overall retention. And if you do that, you’ll increase revenue.
Source: Gen Furukawa, Retainble
Why do users churn in the early months?
Because too many don’t REALISE the value they ANTICIPATED when they signed up. Either they don’t realise it quickly enough or they don’t realise it at all.
We’ve talked a lot about ‘Aha’ moments here on the Userpilot blog. They’re the moments when users perceive the difference your product will make to their lives.
But perceiving value is not the same as EXPERIENCING it.
Once a product has actually lived up to the promise that tempted a user to try it in the first place, we can say that the user has made a crucial step in their journey.
He or she has become an ACTIVATED user.
Activation is one of the most important early milestones along the customer journey.
But how do you quantify it? How do you define, measure, track, replicate and scale that experience?
That’s what we’re going to look at now.
Find the User Adoption Metrics that Matter
“Not everything that can be counted counts.”
Not every measurement matters, and to see which do, you need to start with clearly-defined GOALS.
We’ve already narrowed this down a bit: we’re trying to achieve is improved user retention in the early months.
That’s still too vague
Take a step back and answer these questions:
- What is the customer drop-off rate now?
- What do you want it to be?
- What will have to change – that is, how many users will have to stay on how much longer – for you to get from 1 to 2?
The more precisely you can state your goal, the more accurate your metrics will be.
Then, the more closely you can target your actions to affect them.
A Cohort Analysis is often a good place to start.
This chart shows the cohort of new customers coming on each month and the percentage churning each month.
Breaking overall churn down by WHEN a new user started and HOW LONG they were a user for before churning helps narrow down the metrics to target and identify the actions that will improve them.
In this chart, for example, you can see that for March 2018 starters, a big retention problem occurs in month 6. Churn rises from 7.69% in month 5 to 16.67%.
That seems to be happening for more recent cohorts at around the same time:
- April 2018 cohort – month 4 is August
- May 2018 cohort – month 3 is August
- June 2018 cohort – month 3 is September
- July 2018 cohort – month 2 is September
This suggests that the problem is down to something that has been happening in those months. Not something that regularly happens once a user has had the product for a certain amount of time.
So we’d start looking at what changed between August and October 2018. Perhaps a new version was rolled out, old features were replaced, prices went up, etc?
Usually, Cohort Analysis alone won’t point to such an obvious solution.
You need to combine it with behavioral analysis.
And that’s why in-app tracking of user activity is crucial.
Activity tracking needs to be built in.
If you can’t see WHAT users are doing, how can you work out WHY they do anything?
Once you CAN see this, look for correlations. For example:
- 20% of users who don’t use two or more product features in the first 30 days churn within that time, compared to just 5% who do
- A greater proportion of those who don’t open your welcome email allow their free trial to lapse when it ends than those who do open it
- There is a linear correlation between how much time users spend on the site on average per week and how long they stay as a customer.
When you have a correlation, you can develop a hypothesis.
Eg: “early adoption of multiple features tends to lead to longer retention”.
This points to the metrics you need to track:
- Number of features adopted per user
- Days after becoming a user they begin using each feature
Finally, you’d track the churn rates for users who hit the metrics versus users who don’t. If there’s a significant positive difference over time, then you’ve chosen the right metrics to validate that hypothesis!
To be clear, there are other important milestones on the user journey. But today, we’re looking at Activation. We’ll look at the others another time.
Your definition of “User Activation” should include:
- Everything that you’ve shown makes a positive difference to retention in (say) the first 90 days
- PLUS everything you THINK will make a difference that you haven’t proven yet (ie all your working hypotheses)
It’s also important to differentiate Activation from Engagement. Sherlock has a great way of putting this.
Maybe you don’t have any data yet?
In this section, we’ll give you some ideas about User Adoption Metrics that have been shown to affect retention.
#1 Product Access
If users are regularly logging into your app, it’s a good sign that they’re liking it and finding value in it. If they’re not logging in… well… it’s the opposite.
On its own, however, this is far too simple to be a meaningful measure. A signed-in user is not necessarily an active user.
- Does your app automatically log inactive users out, like the example above? If somebody is logging in repeatedly within the same hour, doesn’t it suggest that the app is crashing on them rather than that they’re getting value?
- Many SaaS businesses track access in the last 7 days as a User Adoption Metric – but of course, by itself, this doesn’t give you any information in that critical first week. Brian Balfour of Reforge has waned that Week 1 engagement”has one of the biggest impacts on your retention over time”.
Access data needs to be combined with other metrics to get a true picture of Activation.
#2 Time Spent
You start getting a much better sense of what’s going on in users’ heads when you track how long they are logged in for – in addition to whether they have logged in.
How often are users coming back?
Duolingo is all over this!
Many SaaS businesses track their Daily Active User (DAU), Weekly Active User (WAU) and Monthly Active User (MAU) figures religiously. Dividing the these figures by the TOTAL user numbers for the period gives a useful percentage ratio.
And the DAU divided by the MAU gives a “Stickiness” rate. If the Stickiness rate is 25%, it means that your mean user is logging in on 7.5 days in every 30.
#4 Task Completion and Feature Adoption
This is where things start to get interesting, because we’re now looking at how users are engaging with your product – not just if, when, how often and for how long.
Remember, we’re looking for the moment when a new user EXPERIENCES the value they anticipated in their “Aha!” moment.
That’s going to associated with them USING your product for a purpose – so it could include:
- Completing their user profile
- Going through some or all of your in-app onboarding steps
- Creating and saving a template, a web page, a workflow, etc etc
- Checking for updates
- Opening a support ticket
BUT, if they only complete the action once it could indicate that a user tried it and was disappointed by the experience! It may not be enough simply to track the first completion.
So the SECOND time a user completes a task or when they adopt MORE THAN ONE feature is a more valuable signal of Activation.
#4 Red Flags
It’s critical to track what people who churn early DON’T do, just as much as what users that don’t churn DO do.
We’ve already alluded to this a couple of times (eg not repeating a task after the first attempt, not logging in frequently enough).
Other red flags to watch out for include:
- Tasks started but not completed – this suggests users are not getting what they want from the product
- Declining frequency of access or stickiness rate
- Falling dwell time (but only where the latter is correlated with fewer tasks being completed – if time on site drops while task completion is unchanged, a user is getting better at using the product!)
#5 Customer Health Score
Once you have validated a number of User Adoption Metrics that feed into your definition of Activation, you can start to get creative.
In his recent Product Drive presentation, Patrick Thompson of Iteratively explains how combining a series of weighted metrics can give an overall “Customer Health Score”.
Once the combination of user’s actions and events takes them over a certain score, they are deemed to be active users – but crucially, there are MANY DIFFERENT WAYS to build up that threshold score.
Red Flag events of the sort we mentioned above would reduce the Customer Health Score. Bringing them in turns the whole system into a dynamic Engagement rating rather than a static case of Activated/Not Activated.
#6 Qualitative Evidence
Patrick also makes another important point in his talk. It’s one that anyone who ever deals with analytics has to confront.
Even the most sophisticated predictive analytics models can only correlate behaviors and probabilities. And correlation is not causation.
So, you need to spend some time validating your hypotheses QUALITATIVELY. That is, asking users what they think and feel and why.
The best ways to do this are:
- In-app or email surveys and rating systems
- NPS tools (see above for how you can add this to your SaaS product with Userpilot)
- Offboarding questionnaires – for finding out why somebody stops being a user (and putting in a final plea to stay, like this…)
How You Can Increase Activation Rates and Cut Churn
The whole point of coming up with a detailed definition of an Activated user is to get more users activated, experiencing the value and staying for longer as customers!
Now we know what we’re aiming at, that’s a lot easier to achieve.
In this section, we’re going to share 7 steps you should take to improve any set of User Adoption Metrics.
#1 First Impressions Count
SaaS products can be complex and they often require users to learn new processes and terms.
You need to help them overcome this – or else they won’t complete tasks, they won’t adopt new features, they won’t log back in, etc. That first impression affects almost every metric you can think of!
That’s why we were really surprised when our UserPilot 2020 State of SaaS Product Onboarding review found that only 60% of the 1,000 products we tested provided a welcome screen for new users.
A welcome screen:
- Makes new users feel valued
- Allows you to provide “getting started” advice
- Gives you an opportunity to find out more about your users, so that you can provide a more tailored experience (aka ‘personalized onboarding’)
Notion does a great job of this. By using multiple pages in a welcome sequence, they cram a lot of onboarding essentials into the first few moments of the user journey. See how they push the adoption of multiple features right at the start.
Here at UserPilot, the very first thing we need a new user to do is install our browser extension. So the welcome screen we provide is aimed exclusively at driving that, by restating the outcome users want to achieve and providing a single Call To Action.
Once that’s done, we guide and support the next steps by encouraging them to create a first experience. For us, that’s one of the critical steps every user needs to take to experience the value of our service.
No matter what User Adoption Metrics you are driving NEVER just throw new users in at the deep end! Nothing is more confusing than an empty dashboard and a mass of unexplained controls.
#2 Provide Help Where It’s Needed
Support new users on their journey towards Activation by providing in-app help.
There are lots of different ways you can do this.
If you can see what a user is trying to do, provide contextual help that explains how to complete the task.
For example, Box provides pop-up tooltips when a user is needs to take action, explaining what they need to do, why they need to do it and what it will enable them to do.
This is something Postfity does too.
#3 Let Users Learn At Their Own Pace
To be fair, contextual prompts are not ideal for every user. Some people find them intrusive, and you should always provide an option to switch them off. Experienced users will not appreciate seeing basic help messages over and over again!
But there are many other ways to help users achieve their goals.
- FAQs, Document Libraries and Online Academies: rather than proactively providing help where you think it’s needed, you can provide it reactively. That is, allow users to learn at their own pace and access information when they decide they need it;
- Live Chat Support: this is much better than email or phone support, as it allows you to solve users’ problems while they are working in your app
Here’s how Poptin provides reactive help to users:
And here is Storychief’s live chat window, which incorporates a document and video library:
#4 Add an Onboarding Checklist
Ever heard of the Zeigarnik Effect? It’s a principle in psychology that says people remember incomplete tasks better than completed ones.
So if you need users to finish certain tasks to hit your User Adoption Metrics, keep reminding them! Onboarding checklists are a great way to do this. Here’s one that we use to remind users what they still need to do:
And here’s a very similar-looking one from Productboard:
Keep reminding users of what they need to finish to experience the value, and they’re more likely to get there – especially if they need multiple sessions.
Onboarding checklists are pretty widespread today. Our study found that 56% of SaaS products use them. But that still leaves a big minority missing out!
That leads us nicely to the concept of Gamification.
If you’re making your users perform certain actions in your app – make it fun!
People love earning rewards and scoring points, and this can be harnessed as a great way to guide them through an onboarding workflow.
This is how Stabucks does it with their app:
You can do really anything that you think will keep your users hooked. The creative possibilities for gamified onboarding are endless!
#5 Reach Out to Red Flagged Users
Don’t let frustrated users simply drift away. Try to get them back on track with re-engagement messaging that addresses the problems you think they have been experiencing.
The more contextual you can make this, the better. It’s far more effective to reach out with a suggested solution to a recognized problem than simply to ask users to come back (and experience the same frustrations all over again!)
But if you don’t know – ask! Get user feedback wherever you can. Particularly from leaving users: if you can fix the reasons that caused them to churn for future users, you’ll have a better product and better User Adoption Metrics.
#6 Test Everything
We can always be mistaken in our explanatory hypotheses. Even when we’re right, we may not be right for the right reasons!
So it’s sensible to split test any measure you put in place to improve User Adoption Metrics. That means give one cohort of users the new experience, and another the same old one. If the new way shows better results, it’s worth sticking with.
Here’s a UI split test that Wordstream ran on a landing page. It found that the Variation converted better than the Original, so they adopted that instead.
But be careful!
If you test too many variables at once or you don’t test against the control state, the story your data tells may be misleading.
- Which of the many changes you made caused the shift in behavior?
- If you don’t test against the original state, how do you know if changes improve against it?
Notice how the Wordstream alternatives are identical in all ways but one. Small changes, one at a time, are the way to go about optimizing your onboarding workflow effectively.
#7 Remember, Things Change
Users change. Your product changes. The competitive environment changes.
You can’t simply assume that your definition of “Activation” will hold permanently.
As David Apple of Notion explains in this video, you have to keep challenging all of your assumptions.
User Adoption Metrics – In Conclusion
The ideal User Adoption Metrics differ:
- Between businesses
- Between cohorts of users
- Over time
To identify what Activation means to you:
- Collect as much in-app behavior data as you can
- Analyse behavioral cohorts for different retention rates
- Hypothesize and test the groups of behavior that correlate with increased retention in the early weeks and months
And then drive those behaviours to push the User Adoption Metrics up!
👉 If you’re looking for help with that, book a demo of Userpilot today. Effective onboarding is what we’re all about!
Any other tips and advice on Activation? Please share them below!
Aazar Ali Shad is the Head of Growth at Userpilot, and has more than 6 years of SaaS Experience. He is currently helping 600+ SaaS companies improve user onboarding and increase product adoption. You can connect with on LinkedIn or Twitter.