What it does
Use when a user mentions "milestone advocacy asks", "lifecycle advocacy", "natural advocacy moments", or asks for related help. Detect natural advocacy moments via AI attribute — first value achieved, expansion completed, renewal closed, high-engagement-streak, customer-milestone-hit — fire contextual advocacy ask matched to moment (review / referral / case-study / speaker opportunity). The Captivate Collective Lifecycle Advocacy framework.
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
Build Advocacy-Moment AI Attribute
create_ai_attributeCreate an AI-derived attribute
Identify Advocacy-Ready Users
Create SegmentBuild a segment
Build Per-Ask Content Variants
Generate ContentGenerate advocacy-ask email variants per recommended_ask. Review ask:
Build Advocacy Recommendation Surface
create_recommendationConfigure a recommendation surface
Build Advocacy Journey
Create JourneyBuild a journey wired to advocacy-ready segment. Touch 1 (Day 1 after moment detected): advocacy-ask email matched to recommended_ask. Touch 2 (Day 0 of touch 1): activate recommendation surface in-app. Touch 3 (Day 7, ONLY if user engaged with touch 1 OR rec-surface): light follow-up with concrete next step (
Build Advocacy Program Dashboard
Build DashboardCompose an advocacy program dashboard: advocacy-moment detection volume per week, ask-to-action conversion rate by ask type (which advocacy asks actually convert — referrals usually beat reviews in volume but reviews beat referrals in long-term value), top advocates (users who have completed 3+ advocacy actions — this is the inner-circle list to nurture specially), and downstream attribution (reviews influencing G2 score, referrals creating new pipeline, case studies appearing in sales decks). Reframes CS work from
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