advocacyled.com
The ALG Compound Formula - A x R x C Explained
Every growth motion has a formula. Email marketing has open rate x click rate x conversion rate. Paid ads have impressions x CTR x conversion rate. Sales has meetings x close rate x deal size.
Advocacy-Led Growth has its own: Participants x A x R x C = New Participants.
This formula does something the others don’t - it loops. The output (new participants) becomes the input for the next cycle. And each variable improves with repetition. That is why ALG compounds while other motions produce flat returns.
The three variables
A - Activation Rate
What it measures: The percentage of a cohort that shares at the completion moment.
A cohort of 40 people completes a certification. 10 share their credential. Activation Rate = 25%.
The activation rate is the first variable in the formula because nothing else happens without it. If nobody shares, there is no reach. If there is no reach, there is no conversion. A is the gate.
What drives A:
| Factor | Impact on A | Example |
|---|---|---|
| Value exchange quality | Highest impact | Personalized credential vs generic “share this” prompt |
| Activation timing | High impact | At completion (25% A) vs one week later (5% A) |
| Friction level | High impact | One-tap share (25% A) vs multi-step process (10% A) |
| Cohort cascade | Moderate impact | Visible peer sharing triggers additional shares |
| Completion moment intensity | Moderate impact | Certification (high A) vs webinar attendance (low A) |
Benchmark ranges:
| Completion type | First activation A | Mature system A |
|---|---|---|
| High-intensity (certification, speaking) | 15-25% | 20-30% (repeat advocates lift average) |
| Medium-intensity (event, onboarding) | 8-15% | 12-20% |
| Low-intensity (webinar, download) | 1-3% | Not worth optimizing |
The difference between first activation and mature system is repeat advocates. Someone who shared their certification last quarter has a higher probability of sharing this quarter because the behavior is practiced and the payoff is known. Over time, repeat advocates raise the blended activation rate.
R - Reach Multiplier
What it measures: The number of unique people each advocate’s share reaches.
An advocate shares their credential on LinkedIn. The post reaches 2,000 people in their professional network. R = 2,000.
R is not a single number - it varies per advocate based on their network size, the quality of their content, and algorithmic distribution. The useful metric is the average R across the cohort.
What drives R:
| Factor | Impact on R | Why |
|---|---|---|
| Advocate’s network size | Direct | Larger network = more impressions per share |
| Content authenticity | High | Personal content gets better algorithmic distribution than corporate-sounding posts |
| Engagement on the post | High | Comments and reactions extend reach through second-degree connections |
| Platform | Moderate | LinkedIn personal profiles reach 5-15% of connections organically |
| Timing of share | Moderate | Posting during business hours reaches a larger active audience |
Why R improves over time: Advocates who share regularly are building their professional networks. Each share attracts new connections who are interested in the same professional domain. By their third or fourth share, an advocate’s network is measurably larger than when they started - which means R increases with each cycle without any additional effort from the company.
C - Conversion Rate
What it measures: The percentage of reached people who become new participants - people who register for the next event, sign up for the next certification, or enter the product.
An advocate’s share reaches 2,000 people. 3 of them register for the next certification cohort. C = 0.15%.
C is the smallest number in the formula, but it is the most important for compounding - because C is what closes the loop. Without conversion, the chain terminates at reach. With conversion, reach produces new participants, and new participants produce new advocates.
What drives C:
| Factor | Impact on C | Why |
|---|---|---|
| Network relevance | Highest impact | A developer’s network contains other developers. The audience is pre-qualified. |
| Trust quality | High impact | Cohort trust converts at higher rates than brand trust |
| Call to action clarity | Moderate impact | ”Join the next cohort” converts better than “learn more” |
| Landing page experience | Moderate impact | The page the clicked link leads to must deliver on the advocate’s implicit promise |
| Offer alignment | Moderate impact | What the advocate shared must match what the visitor finds |
Why C improves over time: Two reasons. First, as more advocates share across multiple cycles, the signal density increases. A prospect who sees one person share a certification might be curious. A prospect who sees three people in their network share the same certification over six months develops genuine interest. Second, the brand’s credibility grows as the volume of authentic practitioner signals accumulates - which improves conversion rates across all channels, not just advocacy.
How the formula loops
The formula’s power is in its structure. Each cycle’s output becomes the next cycle’s input:
Cycle 1: 200 participants x 20% A x 2,000 R x 0.15% C = 120 new participants
Cycle 2: 200 new participants + 8 repeat advocates from Cycle 1 208 participants x 22% A (repeat advocates lift average) x 2,100 R (networks grew) x 0.16% C (trust density increased) = 155 new participants
Cycle 3: 200 new participants + 14 repeat advocates from Cycles 1-2 214 participants x 23% A x 2,200 R x 0.17% C = 184 new participants
Each variable improves slightly with each cycle:
- A increases because repeat advocates activate at higher rates
- R increases because advocates’ networks grow through professional activity
- C increases because accumulated signals build trust in the ecosystem
The improvements are small per cycle - a percentage point here, a hundred more reach there. But compounding works on small, consistent improvements over many cycles. By Cycle 8, the system is producing 2-3x the output of Cycle 1 with the same input investment.
The comparison with linear formulas
Email marketing has a similar formula: List Size x Open Rate x Click Rate x Conversion Rate = Results. But it doesn’t loop. The results don’t feed back into the list size. To grow the list, you need to invest separately in list acquisition. And the rates don’t improve with repetition - they typically decline as lists age and fatigue sets in.
| Property | Email formula | ALG compound formula |
|---|---|---|
| Structure | Linear (inputs produce outputs) | Looping (outputs become inputs) |
| Rate trajectory | Declining (list fatigue, inbox competition) | Improving (repeat advocates, growing networks) |
| Cost structure | Linear (per send) | Front-loaded (infrastructure built once) |
| 12-month forecast | Flat or declining yield | Increasing yield at decreasing marginal cost |
The same comparison holds for paid ads (impressions x CTR x conversion - no loop, rates don’t improve) and cold outreach (contacts x response rate x meeting rate - no loop, rates decline with market saturation).
Using the formula as an operating tool
The compound formula is not just a theoretical model. It is a diagnostic and optimization tool.
Diagnosing problems
When results are below expectations, the formula tells you which variable to investigate:
Low total output + low A: People are not sharing. Investigate the value exchange (is the share serving the sharer?), the timing (are you inside the Belief Window?), and the friction (how many taps from completion to share?).
Decent A + low total output: People are sharing, but reach is low. Investigate whether the right people are sharing (senior practitioners have larger networks than junior ones), whether the content is performing algorithmically (personal vs corporate-sounding), and which platform the shares are happening on.
Good A and R + low total output: Reach is strong but nobody is converting. Investigate the call to action (is there one?), the landing page (does it match what the advocate shared?), and network relevance (are the advocate’s connections actually in your target market?).
Optimizing investment
The formula also tells you where to invest:
If A is 10% and R is 2,000, improving A to 15% (through better value exchange and timing) produces 50% more output. Improving R from 2,000 to 3,000 (through targeting more senior advocates) also produces 50% more output. But improving A is typically easier and cheaper than improving R, because A is under your control (activation design) while R is partially dependent on who your advocates are.
The optimization priority: A first, then C, then R. Activation rate responds to design changes. Conversion rate responds to landing page and offer improvements. Reach multiplier is the hardest to influence directly - but it improves naturally over time as advocates’ networks grow.
The AQL connection
Each variable in the compound formula maps to an AQL tier:
- A determines your total AQL volume (how many advocates share)
- R determines your AQL-1 volume (how many downstream clicks each share generates)
- C determines your AQL-2 and AQL-3 volume (how many downstream participants and pipeline entries result)
When you report AQL metrics to leadership, the compound formula is the engine behind those numbers. The AQL is the measurement. The formula is the mechanism.
The patience requirement
The compound formula has an uncomfortable truth built into it: the returns are back-loaded. Cycle 1 produces modest results. Cycle 2 produces slightly better results. The dramatic improvement doesn’t show up until Cycle 4 or 5, when repeat advocates have accumulated and all three variables have improved through repetition.
This is why running ALG as a campaign kills the motion. A single campaign produces Cycle 1 results - which are always the weakest. The team concludes that advocacy produces modest returns and moves on. They never see Cycle 4, where the same investment produces 2x the output.
The compound formula is the mathematical argument for patience. The variables improve with repetition. The output grows with each cycle. But only if you give the system enough cycles to compound.
Build the activation infrastructure. Run it repeatedly. Measure A, R, and C across campaigns, not within them. The formula does the rest.