Glossary
The canonical vocabulary of Advocacy-Led Growth. A growth motion is also a language - these are the terms practitioners use to orient decisions, challenge assumptions, and explain the motion to people who haven't seen it yet.
A go-to-market motion where existing participants - employees, community members, event attendees, and partners - become the primary engine of distribution, trust, and pipeline. Operates on a 1:1:N distribution architecture where advocacy compounds through cohort activation at completion moments.
The distribution architecture of ALG. Brand activates one advocate (1), who shares on their terms (1), reaching their entire professional network (N). Unlike 1:1 motions (email, ads, outreach) where the chain terminates, ALG's chain multiplies. At maturity, becomes 1:1:N:N as some people in the first N become advocates themselves.
The period of peak psychological belief after a completion moment - the optimal window for ALG activation. Starts at the moment of completion and diminishes over hours and days. Traditional advocacy fails because it activates outside this window. Testable via share rate decay curves at T+0, T+1hr, T+24hr, T+7d.
The point at which a participant finishes something real - an event attended, a certification earned, an onboarding completed, a challenge finished. Three intensity levels: high (certification, speaking), medium (conference session, onboarding), low (booth visit, form fill). Higher intensity = wider Belief Window = stronger advocacy.
A group of people sharing a completion moment and professional context. The fundamental unit of ALG activation - not the individual. Cohorts enable cascade effects (social permission), structural audience relevance (pre-qualified networks), and signal clustering (AI engines read simultaneous signals as strong evidence).
The social proof effect where early shares within a cohort trigger additional shares from peers. The first shares are hardest. Shares 5-20 become progressively easier as the cohort sees peers participating. Designed for, not hoped for.
A person whose voluntary advocacy behavior generated measurable downstream impact. Three tiers: AQL-1 (Basic: share + downstream click), AQL-2 (Influence: share + downstream participant completes action), AQL-3 (Pipeline: share + downstream participant enters pipeline stage).
The percentage of a cohort that shares at the completion moment. ALG's core operating variable - equivalent to email open rates or PLG activation rates. Typically 15-25% at well-designed completion moments. Optimized via value exchange quality, timing (inside Belief Window), friction reduction, cohort cascade visibility, and recognition.
The multi-surface landscape where advocacy signals persist and compound. Three layers: Layer 1 - Network Signals (LinkedIn, X - immediate, ephemeral), Layer 2 - Authority Signals (G2, Reddit, blogs - persistent, compounding), Layer 3 - AI Engine Signals (aggregate, algorithmic, category-defining).
What the advocate gains from sharing - credentials, recognition, economic access, professional positioning. ALG only compounds when all three actors gain: participant (value), company (distribution), network (relevant discovery). Diagnostic: would the participant still want to post it if you removed the company's name?
When the advocate's economic or professional interest naturally aligns with the act of distribution. The fourth prerequisite for ALG. Without motive alignment, advocacy requires constant incentivizing. With it, advocacy is self-sustaining.
The highest tier of the trust taxonomy. Authentic signals from real practitioners who completed something real - personal, verified, corroborated, persistent. Unlike paid trust (depreciating) or borrowed trust (non-compounding), cohort trust compounds rapidly across cycles.
The structural shift where AI makes brand content (infinite supply, zero cost) into noise, while individual content (identity-verified, experiential) becomes the primary signal that algorithms and AI engines trust. The macro condition that makes ALG the growth motion for the AI age.
Participants × A (Activation Rate) × R (Reach Multiplier) × C (Conversion Rate) = New Participants per cycle. ALG compounds when any variable improves over time. A increases through recognition, R through network growth, C through trust-based conversion.