{"id":168429,"date":"2026-07-31T19:59:19","date_gmt":"2026-07-31T19:59:19","guid":{"rendered":"https:\/\/userpilot.com\/blog\/?p=168429"},"modified":"2026-08-03T04:45:01","modified_gmt":"2026-08-03T04:45:01","slug":"userpilot-feature-adoption","status":"publish","type":"post","link":"https:\/\/userpilot.com\/blog\/userpilot-feature-adoption\/","title":{"rendered":"Userpilot for Feature Adoption: Features, Pricing, Pros & Cons"},"content":{"rendered":"

If you are evaluating Userpilot for feature adoption<\/a>, you are asking three things at once: can it measure whether the people who could use a feature actually do, can it help you lift that number, and are the parts you need sitting inside a plan you can afford? I run product here, so I will answer all three directly, including the answers our own pricing page makes you dig for.<\/p>\n

Feature adoption is the share of active users who use a specific feature, and stickiness is whether those users keep coming back to it. The two belong together, because adoption on its own only tells you people found the feature once. Measuring feature adoption against active users, not total signups, is the part most teams get wrong before they even open a dashboard.<\/p>\n

Userpilot treats feature adoption as one loop instead of three disconnected tools. It collects interaction data with autocapture and event labeling, reports an adoption rate and a stickiness trend in product analytics<\/a>, then lets you respond to a weak number with an in-app experience aimed at the exact segment that is not adopting.<\/p>\n

That loop is the real reason to consider the tool, and it is also where the plan you pick decides what you get. The no-code event labeling that powers feature adoption reporting lives on Growth and Enterprise, while Starter caps you at 25 active events. This review walks through how Userpilot collects usage data, how adoption is measured, what to do when the number is low, how AI agents complicate the denominator, what each plan costs, where the limits are, and who the tool actually suits.
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How Userpilot collects feature usage data<\/h2>\n

Here is the correction that matters most in this review. Older write-ups, including the earlier version of this very page, explain feature adoption through feature tagging, and that is no longer how the tool primarily works. The current model runs Autocapture<\/a> into Labeled Events into Tracked Events into Custom Events, and each layer plays a distinct role in measuring feature adoption.<\/p>\n

\"Turn
Turn clicks into feature adoption data<\/figcaption><\/figure>\n

Autocapture<\/strong> records front-end interactions automatically as soon as Userpilot is installed and Raw Events Autocapture is switched on. It captures meaningful clicks, text inputs, and form submissions without you deciding in advance which interactions matter. That is the point: you collect the feature usage data first and choose what counts as a feature later.<\/p>\n

Labeled Events<\/strong> turn that raw stream into events a product person can read. You label interactions through the Visual Labeler, with CSS selectors, or by labeling raw events you have already collected. Think of labeling as the no-code bridge between messy interaction data and a clean product analytics<\/a> report.<\/p>\n

\"Labeled
Labeled events.<\/figcaption><\/figure>\n

The historical-data behavior here is worth stating precisely, because it trips people up. Once you label an event, the interaction history that Autocapture already collected becomes available in Event Overview and analytics reports. Segments, event-based targeting, and content triggering, though, only use data from after the moment you labeled the event.<\/p>\n

Tracked Events<\/strong> are a separate thing. These are developer-defined events sent through the SDK or API, they work across web and mobile, they carry metadata, and they can include backend actions Autocapture can never see, like a subscription upgrade or a database-driven state change. They complement Autocapture rather than replacing it.<\/p>\n

\"tracked
Tracked events dashboard.<\/figcaption><\/figure>\n

Custom Events<\/strong> combine several labeled or tracked events into one business event. When a feature only counts as “adopted” after a user completes a few interactions rather than a single click, this is how you model it. For most real features, the custom event is the honest unit of feature adoption.<\/p>\n

\"Custom
Custom event dashboard.<\/figcaption><\/figure>\n

Plan limitations and implementation details<\/h3>\n

Labeled Events are available on Growth and Enterprise only, and Autocapture has to be enabled before any labeled event can start collecting. That single dependency is the quiet gate on most feature adoption reporting in Userpilot.<\/p>\n

Starter has a specific ceiling worth reading carefully: 25 total active events across labeled events, tracked events, and legacy feature tags combined. Custom events do not count against that limit, and if you exceed it, additional tracked events get archived until capacity frees up. For a product with more than a handful of features, 25 goes fast.<\/p>\n

\ud83d\udca1 Read related blog posts:<\/strong> How to Drive Feature Adoption in 2026 (Human + AI Agents)<\/a><\/div>\n

One more thing on the legacy path. Feature Tags still exist for backward compatibility and for filtering historical data, but new implementations should use Autocapture and the Visual Labeler instead. If you see the Chrome extension mentioned, treat it as one way to visually create labeled events, not as a feature-tagging tool you should build a 2026 setup around.<\/p>\n

How feature adoption is measured in Userpilot<\/h2>\n

Feature adoption rate is the percentage of active users who used a specific feature during a selected period, calculated as active users of the feature divided by total active users. Adoption measures discovery, and repeat usage plus stickiness tells you whether the feature keeps delivering value after that first interaction. You need both numbers to say anything true about a feature.<\/p>\n

The Core Feature Engagement dashboard is the primary report for your most important features. It reports four metrics that are meant to be read together:<\/p>\n