Feature discovery to adoption
Shows how many people encounter a feature and go on to try it for the first time, using feature interaction events.
Authored by Sid Chaudhary
Founder & CEO at Intempt
Agentic workflow
Coming soon1 step Blu runs end to end. It drafts a plan and nothing runs until you approve it.
- 1
Follow a feature from first look
Coming soonBlu builds a step
About this recipe
When to use it
A feature can get plenty of first clicks and still die from lack of repeat use. This funnels from first exposure through to repeated use in 4 real steps, so you know whether a feature landed or just got a courtesy click.
What it delivers
Builds a 4-step funnel from a feature's first exposure through to repeated use, using real click_on event patterns to distinguish genuine adoption from a one-time try.
How you prompt this
Describe the outcome in your own words and Blu Agent maps it to this recipe, or run it by name. It drafts a plan first and nothing runs until you approve it.
Tell Blu Agent what you want
“Shows how many people encounter a feature and go on to try it for the first time, using feature interaction events.”
recipe.md preview
Built from the published catalog. Blu Agent's full step prompts load in your workspace when you run it.
Inputs & outputs
What the recipe needs before it runs, and what it leaves in your workspace.
Inputs
- Nothing declared beyond an Intempt workspace. Blu asks for anything else it needs as it goes.
Outputs
How the recipe runs
Blu follows the steps in order and shows you each one before anything goes live.
- 01Follow a feature from first lookCreate a Funnel report called "Feature Discovery to Adoption". Steps: 1. Event "View page" where Page URL contains the feature path: "Discovered Feature" (first exposure) (alternative: Click on where target_id matches a feature-tour or tooltip element) 2. Event "Click on" where target_id matches the feature interaction handle: "Tried Feature" (first use) 3. Event "Click on" with same target_id as Step 2, count >= 3 by the same user within 21 days: "Used 3+ Times" 4. Event "Click on" with same target_id, frequency: at least 3 distinct days of use in the last 5 days: "Habitual User" Conversion window: 21 days Breakdown: By target_id (feature handle) Compare: Previous period (prior 21 days) For each feature, also surface: - Discovery to Habitual conversion rate (Step 4 / Step 1) - Median time-to-habitual (days from discovery to habitual) Annotations: - Flag features with discovery > 1000 users AND habitual conversion < 5%: high-discovery, low-stickiness; investigate UX. - Flag features where discovery to trial conversion < 20%: discovery moment isn't compelling. - Highlight features with discovery to habitual conversion > 25%: surface candidates for promotion. Surface the top 3 features by absolute habitual-user count and the top 3 by habitual-conversion rate. Taxonomy notes: - "feature_discovered" and "feature_habitual" as standalone events do not exist. Feature interactions are tracked via Click on with stable target_id values per feature. - Step 4 ("Habitual User") requires Lovable to compute the "3 of last 5 days" rule from Click on event timestamps grouped by user.
Install Intempt and run it
- 1
Sign up
Create a free Intempt workspace.
- 2
Connect your data
Add the sources this recipe reads from.
- 3
Ask Blu
Type /feature-discovery-adoption in Blu once this recipe is live there.
- 4
Review and approve
Blu builds each step and waits for your OK before anything goes live.
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