How to develop, measure, implement, and increase feature adoption

For many years, I had no idea where to put my sunglasses in my car. I’d hop in and simply chuck them into whatever vacant space was nearest. Far too often, that space was the black-hole gap between the edge of the driver’s seat and the center arm-rest panel. I lost many pairs of sunglasses to that gap, to the point where I finally stopped buying any pair of shades worth more than $50 because the ROI on them, given the amount of time I managed to keep them, was too small.

Then one day, my wife pointed something out to me: Most cars—actually pretty much all cars now—have a special compartment for sunglasses, a very safe and convenient one, in fact, that pops open from a very centrally located panel just under the rearview mirror.

Color me shocked—and embarrassed. 

I now had a safe and obvious place to put my sunglasses. I still had to remember it was there, of course, and remember to use it, but this feature of my car soon became one of my favorites, second only to the cup holder. 

Unlike the brick-sized car phone of the 1990s, the sunglasses compartment is a feature that’s stuck around for most cars because it’s got all the right ingredients: It’s very simple, it’s required little to no modification over the years, it continues to be relatively inexpensive to include and install, and—perhaps most importantly—it’s extremely practical and useful and clearly gets a lot of use by car drivers.

Which is all just to say that… product features matter. 

Features, and their adoption, are an extremely important element of both product analytics and product-led growth. You can’t have successful products without successful features, and you can’t have successful features without being able to measure their performance. 

Let’s dig in a little more to this idea of feature adoption (and analytics) and what it all means for you, the product manager, trying to get people to use and keep using the features in your product that you work so hard to develop and launch.

Feature adoption is the act of a product user “adopting”—i.e., using—certain features within a product. While the name is more or less self-explanatory, choosing which features to include (or not include) in a product isn’t, which is why feature adoption and the process of analyzing are their own little sub-arts within the greater art of product analytics. 

Obviously, feature adoption matters because if your customers aren’t using certain features within your product, you are wasting a ton of internal resources in developing and/or updating those features, and you will want to shift your labor and investments to other projects or features. Eventually, people would stop using your product altogether if they stop using its features. 

Probably a better question is: Why does measuring feature adoption matter? 

To answer that, let’s go back to our car and sunglasses example. Imagine if my car had a way to track and measure exactly how often I use the sunglasses compartment. (It doesn’t, but I’m guessing more modern cars, such as Teslas, potentially do?) Using that data, the car manufacturer can then correlate it with data from all the other users of the same model to extract a “usage curve” for that specific feature (the sunglasses compartment). With that curve, the manufacturer can then monitor, practically in real-time, whether this feature’s usage is trending up or down and hence stay apprised of the feature’s value to customers and change product development plans and strategies accordingly to match customer designers, needs, and behavioral changes. 

Feature adoption metrics are important, and you can measure feature adoption in a number of ways. First, though, It’s important to distinguish feature adoption from other commonly measured types of adoption. 

Feature adoption measures either how much new features are getting used by current users or how much new users are using current features. User adoption, by contrast, is the process by which new users become acclimated to a product or service and decide to keep using it, and user engagement correlates the use of a product with the value users are deriving from it.

On the surface, measuring feature adoption may seem like a no-brainer: Either users are using a certain feature or they aren’t. But simply measuring a “yes or no” provides no actionable data to work with for improving your feature or understanding users’ behavior in relation to that feature and/or other features that may be closely connected to it. 

If users are using the feature, that’s great, but you should also be continually gathering information on:

How widely has your user base adopted a feature and is the feature being used across the entire user base or only within certain segments? What if only certain types of users are using this feature? What does that mean for this feature’s future? 

This one is fairly self-explanatory. How often do your users use the feature and how are they using it? Are they using it in the way it was intended to be used or in some other way? Obviously, if they aren’t using the feature very much or are using it in the wrong way, this information is invaluable to your product development team. 

This one is also self-explanatory. How long does it take for customers to adopt—i.e., start using—a new feature once they are introduced or exposed to it, and how long do they use it once they start using it? Duration of feature use is especially important because it correlates with customer retention and is a key indicator of the feature’s success in terms of providing genuine value.

All of the above, taken together, should give your product development team a very clear picture of which features are and aren’t working and why. 

Feature announcements are what make new features discoverable for your product’s users. Companies use various channels to announce new features, including social media, email, blogs, and in-app notifications. 

While feature announcements should be an automatic part of any new feature launch strategy, remember that it’s possible to “over-announce” a new feature. The same rules should be followed in feature announcements that you’d follow for getting out any other type of message: the right channel, at the right time. Tailor the announcement tone and channel to the feature and to the user. 

In addition to announcing new features in the right way when the features come out, there are a few other methods you can use to drive higher feature adoption rates. These include:

One of the keys to getting as many users as possible to use your features is to always be measuring feature usage. You can do this in the aforementioned ways, but a key part of the process is having a powerful product analytics tool, such as Mixpanel, at your disposal. Product analytics lets you dig much deeper into user behavior and extract insights that would otherwise be impossible—or extremely time-consuming—through regular dashboards that you put together yourself. 

Using your feature adoption analytics, you can then put your data to work in your iteration cycles. Every feature update should be data-driven and couched in the context of what your users are doing with the feature and why. Iterate on the feature to match user behavior and then check to make sure your update worked. 

Returning to the sunglasses compartment in my car, I do still need to remember that it’s there. Sometimes my wife needs to remind me, lest I chuck the sunglasses in the nearest vacant space and watch them slip into the abyss. It’s no different with the features of digital and software products. Users need to be reminded they’re there. You can do this with SMS messages, in-app notifications, emails, or even social posts showcasing how other users have used that feature. Just remember to remind them and set a schedule to do it. 

All of the above will help you drive effective feature adoption, which, in turn, will help you retain customers, which, in turn, will help your company’s bottom line. It kind of adds a new perspective on the term “feature-rich.”