You’ve done your market research. Surveyed your community. Figured out exactly what problem is plaguing your users. Worked with your development team to build a killer new offering (then QA’ed the heck out of it). And, finally, released your shiny new feature into production.
The only problem is… no one is using it.
Sounds like every product manager’s worst nightmare, right? Unfortunately, it’s an all-too frequent phenomenon. In fact, upwards of 80% of features in the average software product are rarely or never used, amounting to $29.5 billion in wasted time and resources (by publicly traded cloud companies in 2019 alone) that could have been allocated to more fruitful endeavors.
For any organization, that staggering waste metric is no small drop in the bucket. Now think about what that dollar amount looks like at scale. For large or enterprise-scale companies with massive product portfolios and complex feature sets, a single unused feature could equate to millions in lost revenue—and even customer churn.
But this trend doesn’t necessarily mean that the features themselves are bad. More often than not, it comes down to users not knowing that those features exist, or not getting the contextual guidance they need in order to use them correctly. That’s where product experience software can help.
1. Don’t build blind
Giving your new feature every fighting chance of success starts and ends with data. Just as you conduct deep product discovery before you begin building features (you do, right?), you should use product analytics to get an objective view into what’s working well (or not so well) about your existing feature set and establish a performance baseline. Understanding user engagement pre- and post-feature launch is a critical way to correlate features to business outcomes—giving you an easy and irrefutable way to demonstrate the value and impact of your efforts.
Product analytics data is also useful for identifying current users who will benefit from your new offering, so that you can ultimately target them with your marketing, onboarding, and adoption motions. Particularly for software that’s used on an enterprise-wide scale, this granular metadata can help you figure out exactly who your feature’s ideal users might be (and where they’re spending most of their time) so you can meet them where they are in your product. For example, if a new feature is meant to complement an existing feature, you should start by targeting users who currently use the existing offering to let them know about this new functionality and how it will improve their workflows.
Be sure to review any user feedback you’ve received related to the feature you’re working on and look for trends to help inform your build, launch, or enablement strategies. With a tool like Pendo, you can even alert users who submitted feedback or requests related to specific features whenever the status of their requests change—and when the new feature is ultimately released. This allows you to reach your most invested users at scale, encourages early adoption, and keeps the door open for them to provide ongoing feedback to improve your product.
Post-launch, it’s important to keep an eye on the data and see how things are trending, relative to how frequently you expect users to leverage the feature. Monitoring feature adoption via a product analytics tool allows you to understand things like:
- When features become part of your users’ everyday lives (also known as stickiness)
- The typical paths users follow before or after engaging with the feature
- How long users continue to leverage the feature post-launch—so you can determine if it’s actually adding value to their experience or needs to be reconfigured
2. Make adoption a team effort
Feature adoption isn’t the sole responsibility of a single team. Particularly in complex, enterprise-scale organizations, driving adoption is a multi-pronged effort that often includes contributions from product, marketing, enablement, customer success, and more. And because feature releases are typically rolled out progressively—moving from internal release, to limited beta, to open beta, and finally general availability—assessing usage, managing communications, and collecting feedback requires help from different teams at each stage. For example:
As your product team looks through product analytics data to uncover usage trends, your customer success team can help recruit customers who might make good design partners, or who could be good candidates to test and provide feedback on the feature once it hits beta. Your marketing team can also help target and promote the call for feedback or limited beta to the right customers in-app, so you can expand your reach. Your change management or education team can also partner with marketing to create messaging that readies (and proactively enables) any impacted users for the change, so that they aren’t blindsided when they see the new feature in your product.
When it’s time to release your new feature to market, your sales and customer success teams are valuable partners to help spread the word and encourage their prospects and customers to give it a go—through targeted in-app communications and other recurring touchpoints. Your marketing team should also continue to promote the new feature in-app via guides, tooltips, or by promoting related webinars and events within the product—particularly to users who haven’t yet engaged with the new feature (hint: product analytics is your friend here!). And depending on how complex the new feature is, you can work with your training and enablement teams to create in-app walkthroughs or onboarding flows to get users up to speed on how to use it correctly.
3. Maintain a steady drumbeat
Driving continuous feature adoption isn’t a “set it and forget it” kind of thing. It’s important to maintain a steady and sustainable drumbeat of user enablement post-launch, so that new and returning visitors alike know its value and feel confident using it.
Continue to keep an eye on analytics data to identify how well (and which) users are adopting the feature. And look out for signs of users getting stuck or struggling with it (e.g. dropping off mid-workflow), or declining usage at the user or account level (which could indicate a technical or enablement issue). If you see users who are fully embracing and seeing value from the feature, you can dig into their behaviors via analytics and use that information to build in-app guidance or onboarding programs that encourage other users to follow along the same track.
It’s also important to continue promoting the feature inside your product to make sure new and returning users are aware it exists (and why it’s so great). Different types of in-app messaging and guides are especially handy here. For example, you can segment users based on feature usage, then use lightbox or interstitial in-app guides to target the ones who haven’t yet engaged with the feature yet with value prop-focused messaging. You could add a user guide to the Resource Center in your product that gives users an always-available refresher on how to complete their workflows using the feature (which ultimately reduces your support backlog). Or once the feature has been live for a while and is seeing steady usage, you could roll back your promotional campaigns and add a simple tooltip near the feature to provide evergreen context on how to use it most effectively.
Finally, in the same way your feature launch began, come back to data and discovery. Build guide experiments to see what formats or messaging drive the most meaningful adoption. And continue to collect user feedback and compare it against your product analytics data. Not only will this help you gain a complete view of what’s working well or what can be improved, it’s also a valuable source of inspiration for the next feature iteration—or even releases in other products throughout your portfolio.
Check out our Product-led Hub to learn how to leverage your product (and product experience software) throughout the customer journey to improve the product experience—from product discovery and onboarding to adoption and growth.