What it does

Use when a user mentions "power user pattern detection", "manual workflow detection", "automation upgrade journey", or asks for related help. Detect users repeatedly performing manual workflows that the product can automate (5+ same task in 7 days, batch operations being done one-at-a-time, repeat exports) → surface the relevant power-feature contextually via in-app + recommendation surface, then invite to advocacy program if adopted.

You get

Attribute

AI-Derived Attribute produced by this recipe.

Segment

Segment produced by this recipe.

Content Asset

Asset produced by this recipe.

Recommendation

Recommendation Surface produced by this recipe.

Journey

Journey produced by this recipe.

Dashboard

Dashboard produced by this recipe.

How it works

1

Build Manual-Pattern AI Attribute

create_ai_attribute

Create an AI-derived attribute

Produces:Attribute

2

Identify Power-User Candidates

Create Segment

Build a segment

Produces:Segment

3

Build Contextual In-App Nudge

create_page_content

Generate in-app nudge content per detected pattern. Format: tooltip or floating card that appears WHEN the user is mid-pattern (e.g. on their 6th manual export). Content:

Produces:Content Asset

4

Build Power-Feature Recommendation Surface

create_recommendation

Configure a recommendation surface

Produces:Recommendation

5

Build Advocacy Follow-up Content

Generate Content

Generate follow-up email content sent 14 days after a user adopts a recommended automation feature. Content:

Produces:Content Asset

6

Build Power-User Journey

Create Journey

Build a journey wired to manual-pattern-detected segment. Touch 1 (real-time, on the 5th+ pattern repetition mid-session): in-app contextual nudge fires. Recommendation surface activates persistently. Touch 2 (Day 3, if user didn

Produces:Journey

7

Build Power-User Detection Dashboard

Build Dashboard

Compose a power-user detection dashboard: top 10 detected manual patterns by frequency (which features have the biggest discoverability gap — informs in-app UI redesign priorities), in-app-nudge-to-adoption rate (the headline metric — target: 15%+, baseline generic feature emails get 4-6%), 30-day retention of adopters vs. non-adopters (proves the journey

Produces:Dashboard

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Power User Pattern Detection

What it does

You get

How it works

Build Manual-Pattern AI Attribute

Identify Power-User Candidates

Build Contextual In-App Nudge

Build Power-Feature Recommendation Surface

Build Advocacy Follow-up Content

Build Power-User Journey

Build Power-User Detection Dashboard

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