Imagine working on building a new product feature for months. Your team is convinced it will be a hit—bringing in new users and getting existing users to stick around. Then launch day comes around and... crickets. Time passes, and still, zip, zilch, nada. Your new feature didn’t get the traction you thought it would.
Are you getting a good number of product downloads or signups but finding it difficult to retain users? Feature adoption might be the issue. It’s not enough to keep track of how many active users you have. You also need to monitor and increase feature adoption to retain users.
Feature adoption is when a user is introduced to a product feature and decides to use it. More specifically, your feature adoption rate tells you how many new users use a particular feature.
Here’s how to calculate this rate:
feature adoption rate (%) = (# of new users of a specific feature/total # of product users) x 100
Unlike the feature adoption rate—which pinpoints usage of a certain feature—your product adoption rate gives you a holistic view of overall usage.
Calculating product adoption is like measuring feature adoption, except you’re taking into account all new users and not just new users of a certain feature:
product adoption rate (%) = (# of new users/total # of product users) x 100
If you solely focus on product adoption, you’re missing out on the full story. Knowing your product adoption rate gives you high-level insight into a product’s success. But to really gauge how well your product is doing, you’ve got to dig a bit deeper into the data. Your feature adoption rate helps you zero in on the factors that affect your overall product adoption trends.
Getting users to stick around past the first login boils down to how engaged they are and how much value they find in your product. The more features people use, the more value they get from the product. The fewer features people use, the less value they get for their money.
When customers feel as though they aren’t getting their money’s worth from a product, they’re on the fast track to Churn City. To retain users, you need to keep an eye on how every one of your product’s features performs.
In the SaaS world, where subscription-based services are the norm, tracking feature adoption is especially crucial. Every time a company sends over monthly bills to subscribers, those subscribers consider whether that product is still beneficial. This setup puts pressure on SaaS businesses to regularly show how their product meets users’ needs and adds value.
Tracking feature adoption empowers you to optimize your product at very specific touchpoints, feature by feature. When you figure out which features people use frequently, you can investigate what makes those features so sticky and use that insight to make other features more attractive.
Tracking feature adoption also shines a spotlight on unpopular features and nudges you to dive into product analytics to understand why those features aren’t so hot. Equipped with information about your less-utilized features, you can head back to the drawing board and think of how to improve them or toss them if the demand just isn’t there.
To truly track the performance of a feature, you can’t look at formulas alone. You also want to assess the entire feature adoption funnel. The four parts of the funnel are exposed, activated, used, and used again.
Developed by analytics specialist and founder of ProjectBI Justin Butlion, this four-step framework provides a more nuanced understanding of feature adoption. In Butlion’s own words, it’s “a high-level way to understand the most basic usage metrics of [a product’s] features.”
To explain this framework, let’s imagine you’re launching a feature that lets users share data from your product to their social media channels. To keep things simple, we’ll call this feature “CueIt.” To see how well CueIt performs, let’s take a journey through the feature adoption funnel, shall we?
How can people use your new CueIt feature if they don’t know it exists? Most users aren’t going to go hunting for new features. If you want users to discover and use new parts of your product, you need to alert them about these additions.
Exposure is measured as the percentage of users that landed on a feature’s screen or page (screen on mobile apps, page on a website). No action from the user is necessary at this point. We’re just trying to figure out how many people even know the feature exists. If 1,000 people used your product and 450 of those people saw the CueIt page or screen, your exposure rate is 45%.
If your feature adoption rate is on the low end, a low exposure rate could be the culprit. If you determine that not enough users know about a feature, you can figure out ways to increase feature discoverability and raise the likelihood of feature adoption.
Now that 450 users know CueIt exists, the next step is to find out what percentage of them took action and activated it after exposure. Let’s say 300 of the users activate CueIt by connecting the product to their social media account. That’s a 67% activation rate.
At this step in the funnel, it helps to consider two points:
The answers to these questions can provide insight into how well your feature screens/pages effectively sell users on the feature’s benefits.
Keep in mind that features can be enabled and then disabled, so just knowing how many users currently have the feature enabled won’t tell you how many people activated it since its launch. Use event-based tracking to keep a historical record of activation and learn when users first activated the feature.
The “used” step of the feature adoption funnel shows you how engaged users are with the feature after activating it. If folks aren’t moving from activation to usage, you probably need to put more effort into highlighting the feature’s benefits. It’s not enough just to tell users a feature exists. You also need to sell them on the value the feature provides for regular users.
For some features, activation and usage are one and the same (like filling out a profile). But in CueIt’s case, usage is distinct. People must take the step of actually sharing information on social media after they’ve enabled the feature.
Although a large percentage of users activated CueIt, only 50 of those people used it. That means only 16% of activated users went on to use CueIt.
We’ve reached the final step of the funnel, where we find out just how sticky your new product feature is. Is it a one-hit wonder, or do people use it repeatedly?
In the case of CueIt, 45 of the 50 people who used the feature—90%—continue to do so after the initial use. Once users get on the CueIt train, they aren’t rushing to hop off.
Not every feature requires repeat use, so this part of the funnel may not always apply. But for a new feature designed for repeated use, assess your product data to make sure users are consistently engaging with it and getting value from it.
This funnel framework highlights how important it is to break down feature adoption into these different stages. If you only calculate how many of the product’s total users used CueIt once, the feature adoption rate equals 30%. While useful, the overall feature adoption rate doesn’t give us a thorough look into which parts of the feature adoption funnel we can improve. Breaking down feature adoption provides these insights.
You’ve got the formulas and funnel framework to measure feature adoption. Now, let’s equip you with some tips on how to increase your feature adoption rates.
Getting users to adopt new features starts with feature discovery. The best way to help users overcome feature blindness is by putting it front and center for all to see.
We’ve got a few ideas for how you can get your features in front of users:
Just because you expose users to a feature and they don’t activate right away doesn’t mean they won’t ever use that feature. Sometimes there’s an old feature that users love but have forgotten; announcements can encourage them to revisit it.
Find opportunities to re-introduce ignored features to existing users. Continue educating users on relevant features with well-placed in-app prompts. Tooltips are great for resurfacing features to existing users.
To see examples of companies making moves to increase feature adoption, check out 9 top-notch examples of feature tours and prompts that improve adoption.
For a more unbiased view of a feature and its value, tap into your most valuable resource: your customers. Before launching a new feature, conduct user tests to get a feel for how the feature works for your customers Users might find some kinks in the user experience (UX) that you can work out before you release the feature into the wild.
Once you feel like the feature is ready to go, and you make it available to everyone, get into some cohort analysis, and conduct user surveys to discover how a wider audience feels about it. This customer feedback helps you gauge how good the UX is for customers and what benefits users find in the feature. User surveys are also a great way to find out if your customers turn to a competitor’s product to address the pain point the feature in question is meant to solve.
Get some inspiration for your customer surveys in 6 outstanding examples of in-app user surveys, customer feedback forms, and NPS.
It’s not safe to assume that once a feature is successful, it’ll stay that way forever. As people’s needs and wants shift, the way they interact with your product will, too. Your feature adoption rate will constantly change, so it’s important to monitor it consistently.
And remember that even the feature adoption rate only gives you so much information. Check on a feature’s performance at every stage of the funnel. Use that information to pivot if adoption is decreasing, or stick with what’s working if users just can’t get enough.
