If you release a new feature and no one uses it, did you really release a new feature? In all seriousness, as companies move beyond measuring success by the number of features shipped, it’s even more important to understand how users engage with (and what they want from) specific features inside the application. This way, product teams can use this data to continue improving and building the features and functionality that best serve customers’ needs.
While there are many different ways to track, measure, and improve feature adoption, let’s start with the basics: what is it?
What is feature adoption (and why does it matter)?
Put simply, feature adoption measures the usage for a software product’s specific features. The more features a user adopts, the more value they receive, and the less likely they are to abandon the product–making feature adoption a key metric for retention and expansion.
With the shift to subscription-based software licensing, software is no longer just purchased once–it’s purchased over and over again as customers renew their subscriptions, sometimes as frequently as every month. This makes it crucial for users to recognize the value of your product as quickly as possible, and continue receiving value on an ongoing basis. Each new feature represents an opportunity to add more value to your users’ experience–but only if they are aware of these features and understand how to make the most of them.
On the flip side, every feature that isn’t being used represents something a customer is paying for, but not getting value from. This lowers a customer’s perceived value and, ultimately, impacts their willingness to renew at the current service/price level, or even renew at all.
Measuring feature adoption effectively
On the surface, measuring feature adoption seems simple (i.e. are customers using the feature or not?). While that’s one way to measure, it doesn’t provide any actionable takeaways or insights that teams can use to improve feature adoption over time. When it comes to measuring feature adoption, it’s important to track it on multiple levels, including:
- Breadth of adoption: This measures how widely a feature has been adopted across your user base (or a specific user segment). Is the feature being used by a majority of users, or only a small percentage? Looking at the breadth of adoption can help you understand the initial appeal of a brand new feature.
- Depth of adoption: This refers to how often key types of users access the feature. How often do they utilize the feature? Are they applying a desired workflow to demonstrate stickiness? Are they behaving in unexpected ways? Depth of adoption can signal relevance for an ongoing need or difficulty of use, so you should be continuously monitoring it.
- Time to adopt: How long does it take for a user to begin using a new feature–do they immediately try it out, or do they wait several days (or weeks) before picking it up? The more quickly a feature is adopted, the more likely it addresses a significant customer pain point or need.
- Duration of adoption: After learning about a new feature, how long do users continue to use it? Do they try it out a few times, or use it regularly? This metric helps show whether a feature is providing any real value beyond its initial novelty.
The definition of success for these four measures will be different for every company, but you can use tools like product benchmarks to see how your feature adoption compares to companies of a similar size or stage. These metrics also present an opportunity to combine quantitative and qualitative data to understand why users are (or aren’t) adopting certain features, and what they think about a particular feature.
As you track the quantitative side of feature adoption with a product analytics platform, look for opportunities to collect feedback–bonus points if you can collect it while users are interacting with a new feature for the first time. By collecting feedback in-app, you’re more likely to get a response that reflects what customers thought about a specific feature right when they used it. With both types of data, product teams can get a more holistic view of feature adoption and which enhancements or changes will have the biggest impact.
Pro tip: Be sure to look at feature adoption at the user and account level. Measuring at the account level (i.e. for a specific company) will help you separate out any users who may not need the feature because of their role.
How to drive higher feature adoption rates
Measuring feature adoption is important, but it’s only half the story. Once you’ve collected data and formed insights around which features users are and aren’t adopting, there are certain tactics you can use to improve your feature adoption. Here are four ways we recommend:
- Announce new features in-app: One of the best tools for announcing new features is your product itself. When you deliver feature announcements in-app using pop up guides or tooltips, you can ensure you’re reaching users when the information is most relevant. In the end, you want to make it as easy as possible for customers to find and use the new feature you’re promoting.
- Make the value (and desired action) clear: As you create in-app messages or other communications about a feature, use straightforward language that makes it clear what the value of this feature is. Similarly, be sure to clearly state or point users to the next action you want them to take–whether it’s to immediately use the feature, read documentation about it, or watch a guided demo.
- Drive adoption beyond the announcement: Once you’ve announced a new feature, the work doesn’t end there. Especially for features that are more intricate or require multiple steps in the workflow, it’s helpful to create in-app walkthroughs to guide users through the desired steps.
- Target your communications: Since your product likely has different types of users (e.g. different roles at the company, permissions, or technical proficiency), not every feature will be relevant to every user. Because of this, you should segment your users and target in-app communications about new features for these various groups. For every feature launch, think about who the target audience is and how you might need to adjust your communication strategy for different types of users.