Taking a product-led approach to product and feature adoption

In the past, product leaders often measured themselves based on the number of features shipped. While this type of measurement has a place in your overall strategy, it doesn’t paint a true measure of product success—much less an actionable one. Product-led companies are changing this.

The best product teams know that what they deliver is only as valuable as what’s actually used by their customers. So they’re turning to adoption data to better understand if users are engaging with their product and its key features, which signals whether or not the product is delivering on its intended value. More importantly, though, they recognize that driving adoption is an ongoing process—one that requires continuous measurement, intervention, and iteration.

At a high level, well-adopted products share these attributes:

• They help users discover value quickly
• They encourage habitual, regular usage
• They make expansion easy, as users explore more of the product over time

Why adoption matters for product teams

Every part of your product that isn’t used represents something customers are paying for, but not gaining value from. In a world where it’s easier than ever to switch software providers at any time, this underuse often leads to lower perceived value, and eventually churn.

On the other hand, customers that adopt and engage with a product regularly not only represent retained revenue for your business, but they’re also the ones who tend to become loyalists and advocates for your brand.

To help ensure the latter result, product teams can’t consider their work done at launch. Product managers (PMs) need to constantly be dialed in to how their customers are engaging with what they’ve built and ask themselves questions like: Which features are customers using the most? Is usage increasing or decreasing over time? Are users accessing the app daily (and is this a good thing)?

Although “good” adoption will be different for every product, measuring—and working to improve—adoption is core to being a product-led organization. In the end, it all comes down to creating a product experience that keeps users engaged long after their very first login.

How to measure adoption

Adoption includes both overall product adoption and individual feature adoption. Here’s a quick breakdown for measuring each:

Product adoption can be expressed over time by the total number of monthly active users (MAU), weekly active users (WAU), or daily active users (DAU). You can also measure product adoption as a rate relative to new user signups for a given period of time. 

Either way, the metric(s) you choose will depend on what it means to be an active user of your product. Companies with products for enterprise businesses, for example, might choose a weekly view since their software is typically used on a weekly basis. If your product is made for B2C consumption, looking at frequency of conversions (e.g. purchases) or time spent in the app is probably more beneficial. And remember: more usage isn’t necessarily better. Sometimes, more engagement is an indicator of friction, since users might be spending more time than they should be to complete a certain task or workflow.

Measuring feature adoption is similar to product adoption, but instead focuses on a specific feature (or features) within the product. One common way to measure feature adoption is by the percentage of features that generate 80% of your product’s total click volume. It’s also useful to understand which features are embraced and ignored by users. The former sheds light on where users are finding value, and the latter signals if you need to educate users on key features they aren’t utilizing in their workflows.

Product teams should also examine feature adoption at both the user and the account level. Measuring at the user level will help you understand the behavior of your target persona (or personas), while measuring at the account level (i.e. by company) will help separate out those who may not have needed a specific functionality because of their role or permission level.

Rethinking the post-launch experience

Where a product or feature launch may have previously been viewed as an endpoint, it’s really just the beginning. The new functionality you’ve worked so hard to build won’t gain proper adoption on its own—the announcement and discovery processes are crucial. 

Product-led companies use the product as a channel to announce and educate users on new functionality, bringing resources that previously lived in emails or lengthy documentation right inside the application itself. Product teams can also personalize in-app launch announcements and walkthroughs to ensure these messages are hyper-relevant to users’ needs. For example, if a new feature is particularly valuable for users with a certain type of role, you can target in-app communications to those users specifically and avoid distracting others for whom the feature isn’t relevant. 

In addition to bringing launch communications in-app, the other side of the coin is tracking product usage after you’ve launched a new product or feature. For a feature launch, you can use product analytics to see what percentage of users initially adopted the new feature and how many continued to use it after your promotional campaign tapered off. If you see low adoption, you create additional in-app guides to act as a reminder or point users to an area of your product where they can access in-app tutorials when they need them.

The elements of a successful product-led adoption strategy

Improving adoption doesn’t mean product teams should drive usage of as many features as possible. Instead, it’s about driving adoption of the right features—the ones that create value for your users, contribute to positive sentiment, and lead to positive customer outcomes. This requires a combination of quantitative and qualitative inputs, as well as the ability to take action on all of this data. 

Here are four components that every adoption strategy should include:

Product analytics data

In order to drive adoption, you need to be able to measure it. Being able to track aggregate user data, feature-level data, and user metadata (e.g. their role, industry, company size, etc.) will help you analyze behaviors in ways that empower your team to put these insights into action. In addition to tracking adoption for new products and features, it’s important to get a baseline understanding of how users engage with your product—and continue measuring this over time.

An understanding of user journeys

As you collect product usage data, start forming hypotheses about what typical and ideal user journeys look like. This will help you make sense of your adoption data and approach it with specific questions in mind. Here are a few to consider:

• Which features are most important for delivering customer value?
• What are the key workflows or actions that a user should complete?
• How do those workflows evolve throughout a customer’s lifecycle?
• What are our most used features? Do these align with expectations?
• What’s missing from the list of top features, and what top-used features are surprising?

From there, start comparing your predictions with actual user behaviors. When expectations differ from reality, explore why with a user path analysis that reveals the journey to or from a certain feature or page in your application. You can also examine user journeys for different segments of your user base, for example by comparing paths of users who have and have not used a key feature.

In-app communication

Once you have a solid understanding of how users are engaging with your product, it’s important to use this data to optimize your product experience even further. Is a key feature seeing low adoption? Are users getting stuck during a certain task? Did you just release an enhancement that’s relevant for a specific persona? 

In true product-led fashion, product teams should use in-app messages to drive positive workflows and behaviors while users are actively engaged with the product. It’s also helpful to put together a prioritization framework for in-app campaigns to avoid spamming users with too much in-app guidance.

User sentiment and survey programs

It’s one thing to know how customers navigate your product—it’s another to understand what they want and need from it. To truly understand the drivers of adoption, product teams need to pair quantitative usage data with qualitative data from user surveys. Again, use your product to deliver in-app surveys targeting certain segments of users. Also consider leveraging two types of survey programs: metrics-focused and project-focused. Metrics-focused surveys are always-on campaigns aimed at measuring changes in user experience or sentiment over time. Project-focused surveys are designed to collect specific feedback in support of a discrete initiative, like a feature launch or research for new functionality.