Skip to main content
Intempt
Coming soon

Product engagement and adoption

Answers whether the product is getting more or less engaging, which features users return to, and how users say they feel. The cohort heatmap output is in Retention, not Insights.

Authored by Intempt Technologies

Recipe curators

Agentic workflow

Coming soon

1 step Blu runs end to end. It drafts a plan and nothing runs until you approve it.

  1. 1

    Build the product health board

    Coming soon

    Blu builds a step

About this recipe

When to use it

Raw usage numbers hide whether a product is actually sticky or just being tried once. This surfaces stickiness, feature adoption depth, and the active-vs-passive user split for a real PM-level health read.

What it delivers

Tracks stickiness, feature adoption depth, retention by feature, NPS, and the active vs. passive user split for the PM view.

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

“Answers whether the product is getting more or less engaging, which features users return to, and how users say they feel. The cohort heatmap output is in Retention, not Insights.”

recipe.md preview

Built from the published catalog. Blu Agent's full step prompts load in your workspace when you run it.

Sign up to read the full recipe.md and run it with Blu Agent.

Sign up to see the full recipe

Inputs & outputs

What the recipe needs before it runs, and what it leaves in your workspace.

Inputs

  1. 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.

    1. 01Build the product health boardCreate a Dash board (12-column composition canvas) titled "Product Health". Persona: Product Manager / Product Lead. Question answered: "Is the product getting more or less engaging? Which features matter? How do users feel about it?" Board-level configuration: - defaultDateRange: last_30_days - exclusionPeriod: incomplete_periods - visibility: project - boardFilters: none by default - boardBreakdowns: Plan (resolved from each user's most-recent active subscription): pushed down to all cards as a secondary breakdown Layout: 4 rows. Row 1: Engagement headline (heightPx: 200, four metric cards at widthUnits: 3): - Card 1: Insights metric to source recipe: stickiness-ratios-dau-wau-mau, vizType: metric, titleOverride: "DAU/MAU Stickiness" - Card 2: Insights metric to source recipe: weekly-active-users-trend, vizType: metric, titleOverride: "WAU" - Card 3: Insights metric to source recipe: active-vs-passive-users, vizType: metric, titleOverride: "Producer Share %" - Card 4: Insights metric to source recipe: nps-tracking, vizType: metric, titleOverride: "NPS Score" Row 2: Feature engagement depth (heightPx: 440, two cards at widthUnits: 6): - Card 1: Insights to source recipe: feature-adoption-by-plan, displayMode: chart, vizType: bar - Card 2: Funnel to source recipe: feature-discovery-adoption, displayMode: chart, vizType: funnel_steps Row 3: Retention drivers (heightPx: 400, two cards at widthUnits: 6): - Card 1: Retention to source recipe: retention-lifted-by-feature-adoption, displayMode: chart (the side-by-side curve comparison) - Card 2: Insights to source recipe: feature-usage-heatmap-by-cohort, displayMode: chart, vizType: heatmap Row 4: Engagement segmentation and support pulse (heightPx: 400, two cards at widthUnits: 6): - Card 1: Insights to source recipe: active-vs-passive-users, displayMode: chart, vizType: stacked_area - Card 2: Insights to source recipe: support-tickets-vs-churn-correlation, displayMode: chart, vizType: line (dual-axis) Annotations: - Row 1's four KPIs together answer: are users active (WAU), are they sticky (DAU/MAU), are they actively producing (Producer Share %), and how do they feel (NPS). Each is a different lens; reading them together is the key. - The board's value comes from reading the four rows together: Row 1 reports headline state, Row 2 reports feature engagement depth, Row 3 reports retention drivers, Row 4 reports segmentation and support pulse. Taxonomy notes: - All 8 source recipes are taxonomy-grounded. - nps-tracking depends on Feedback submitted events with survey_type = "nps": see that recipe's notes.

    Install Intempt and run it

    1. 1

      Sign up

      Create a free Intempt workspace.

    2. 2

      Connect your data

      Add the sources this recipe reads from.

    3. 3

      Ask Blu

      Type /product-health-dashboard in Blu once this recipe is live there.

    4. 4

      Review and approve

      Blu builds each step and waits for your OK before anything goes live.

    Related recipes

    Intempt TechnologiesDiscovery call qualification fieldsGenerates an AI summary for Discovery calls from meeting data using the qualification framework in the summary recipe.View recipe Intempt TechnologiesPost demo nurtureKeeps a demo warm for 90 days with a playbook, a case study, an ROI calculator and a customer story, and pulls the AE in early if they bite.View recipe Intempt TechnologiesReturn rate by categoryShows which product categories get returned most, and which ones have got worse since last quarter.View recipe

    More recipes by Intempt Technologies

    Discovery call qualification fieldsGenerates an AI summary for Discovery calls from meeting data using the qualification framework in the summary recipe.View recipe Post demo nurtureKeeps a demo warm for 90 days with a playbook, a case study, an ROI calculator and a customer story, and pulls the AE in early if they bite.View recipe Product swap in a sceneReplaces the product in a shot you already have with a different one, keeping the scene, lighting and shadow the same.View recipe

    Got a play worth sharing?

    Publish it to the Intempt Collective Marketplace and earn when other teams run it.

    Submit a recipe