bookforge-ai Viral Growth Loop Design
When to Use
The startup has selected viral marketing as a channel (via Bullseye) and needs to design, measure, or optimize a viral growth loop. Before starting, verify:
- The product has at least plausible sharing value (products that aren't inherently viral will not be rescued by viral mechanics — this is viral mistake #1)
- The user has metrics or can instrument metrics for invites and conversions
- Viral was genuinely selected, not defaulted to because "growth hacking"
Context & Input Gathering
Required Context (must have — ask if missing)
Product description: what the product does, who uses it, what makes it share-worthy (or why not) → Check prompt for: product name, category, sharing signals → If missing, ask: "What does your product do, and why would one user tell another about it?"
Current metrics (if any): signups per period, invites sent, invite-to-signup conversion → Check prompt for: numbers, "our K is", "conversion rate" → If missing: proceed with hypothetical design, note measurement needs
Observable Context
- Existing viral features: referral program, share buttons, invite flows
- Product communication patterns: how users already talk to others about the product
Default Assumptions
- K > 1 = exponential, K > 0.5 = meaningful contribution, K < 0.5 = not a primary channel
- Optimization focus on the single weakest variable (invite rate OR click-through OR signup)
- 1-2 engineers × 2-3 months minimum to implement viral properly
Sufficiency Threshold
SUFFICIENT: product description + current K measurement or instrumentation plan
PROCEED WITH DEFAULTS: product description known, assume viral is being designed from scratch
MUST ASK: product description is missing, can't recommend loop type
Process
Use TodoWrite:
- Step 1: Classify viral loop type (7 types)
- Step 2: Measure baseline K and cycle time
- Step 3: Decompose K to find the weakest variable
- Step 4: Design focused optimization (4 viral mistakes check)
- Step 5: Shorten cycle time
Step 1: Classify the Viral Loop Type
ACTION: Determine which of the 7 viral loop types best fits the product. See references/viral-loop-types.md for the full taxonomy.
The 7 types:
- Word of Mouth — organic (nothing engineered). Works when the product is genuinely remarkable.
- Inherent Virality — product requires multiple users (Skype, WhatsApp, Snapchat).
- Collaborative Virality — works alone but better with others (Google Docs, Figma).
- Communicative Virality — product messages carry branding ("Sent from my iPhone", Hotmail signature).
- Incentivized Virality — rewards for referrals (Dropbox extra storage, Uber credits, PayPal cash).
- Embedded/Widget Virality — share buttons, embed codes (YouTube embed, Pinterest pins).
- Social Virality — activity broadcast to social networks (Spotify on Facebook, Strava sharing).
Write the type classification with reasoning to viral-loop-design.md.
WHY: Loop type determines every downstream decision. An incentivized referral program that would work for a file-storage product would feel spammy on a B2B analytics tool. Getting type wrong is one of the 4 viral mistakes — "bolting on generic sharing mechanics without understanding how users are currently communicating". The type must match the product's actual usage pattern.
IF no type fits cleanly → that's a signal viral may not be the right channel. Return to Bullseye with new data.
Step 2: Measure or Estimate Baseline K and Cycle Time
ACTION: Calculate the viral coefficient:
K = i × conversion_percentage
- i = average number of invites per user (how many people each user invites)
- conversion_percentage = percentage of invitees who sign up
Worked example: users send 3 invites each, 2 of 3 invitees convert → K = 3 × (2/3) = 2. Starting with 100 users, next cycle produces 200, next 400, etc. Exponential.
Thresholds:
- K > 1: true exponential growth
- K > 0.5: meaningful contribution to growth
- K < 0.5: viral is not a primary channel
Also measure viral cycle time — the time between a user joining and their invitees joining. Shorter cycle time = faster compounding. YouTube's cycle time is minutes; slower products can be days or weeks.
Write measurements (or measurement plan if not yet instrumented) to viral-baseline.md.
WHY: Without baseline K, you're optimizing blind. Every intervention needs a before/after comparison. The K threshold decides whether viral is primary or secondary — K < 0.5 means viral should be a supporting channel, not the main one. Cycle time is often overlooked — two products with the same K but different cycle times have dramatically different growth curves (K=0.9 at 1-day cycle vs K=0.9 at 7-day cycle → very different compounding).
Step 3: Decompose K to Find the Weakest Variable
ACTION: Decompose K further: K = i × click_through_percentage × signup_percentage
Measure each component:
- i — how many invites are sent per user?
- click_through_percentage — how many invite links are clicked?
- signup_percentage — of clickers, how many sign up?
Find the weakest variable. That's the optimization target.
WHY: Focusing optimization on the wrong variable wastes weeks. If invite rate is healthy (people ARE sharing) but signup conversion is 2%, changing the invite flow doesn't help — the landing page is the problem. Decomposition reveals the actual bottleneck. "Not doing enough A/B tests" is another of the 4 viral mistakes — running tests on the wrong variable is effectively the same failure.
Step 4: Design Focused Optimization + Check 4 Viral Mistakes
ACTION: Run the 4 viral mistakes check before proposing changes:
- Not inherently viral product trying to add viral features — will the loop work at all? If the product has no plausible sharing hook, stop.
- Bad product trying to go viral — virality accelerates whatever the product is. A bad product + virality = negative reviews spreading faster.
- Not enough A/B tests — assume 1-3 of every 10 tests will yield positive results. Plan accordingly.
- Bolting on generic sharing mechanics — "just add Facebook Like buttons" without understanding user communication is the most common mistake.
If any of the 4 mistakes apply, fix that before optimizing.
Then design focused A/B tests for the weakest variable. Run 2-3 variants for 2-3 weeks at a time. Budget: 1-2 engineers × 2-3 months minimum for serious viral work.
WHY: The 4 mistakes prevent wasted optimization cycles. Running 20 A/B tests on the invite flow of a non-viral product produces nothing. Running 20 A/B tests on a healthy invite flow when the bottleneck is signup conversion also produces nothing. The mistakes are named to make them detectable.
Step 5: Shorten the Viral Cycle Time
ACTION: Map the full viral loop — every step between "user takes action" and "new user signs up". Count the steps. Remove any unnecessary step. For each remaining step, ask: "can this be faster?"
Tactics:
- Create urgency (expiring invites, time-limited rewards)
- Remove friction at every funnel step (single-click accept, pre-filled forms, social login)
- Trigger invites at the natural sharing moment (not later)
- Incentivize completion of the next step, not just the final conversion
WHY: Cycle time is the most underrated variable. Two products with K = 0.9 but cycle times of 1 day vs 7 days have dramatically different user curves after 30 days. Reducing cycle time by half is equivalent to doubling K for long-term compounding effects. Yet founders obsess over K and ignore cycle time.
Inputs
- Product description (with sharing hypothesis)
- Current viral metrics (if instrumented)
- Implementation resources (engineers × months)
Outputs
Four markdown/data files:
viral-loop-design.md— Loop type classification, mechanics, implementation planviral-baseline.md— Current K, cycle time, decomposed metricsviral-optimization-plan.md— Weakest variable, A/B test roadmap, 4 mistakes checkviral-cycle-time-map.md— Full loop steps with friction analysis
Key Principles
K is a formula, not a vibe. K = i × conversion_percentage. Founders who say "we're going viral" without calculating K are making a category error. WHY: Without numeric K, you can't tell if you're growing virally or just growing. The formula forces clarity.
Loop type must match the product's communication pattern. Generic share buttons on a product users don't naturally discuss is mistake #4. Watch how users ALREADY share the product before designing the loop. WHY: A loop that fights user behavior produces 0% conversion; a loop that amplifies existing behavior compounds.
Optimize the weakest link, not the favorite metric. Founders love to A/B test invite copy. If the bottleneck is signup conversion, invite copy changes nothing. WHY: Decomposition is the only way to find the actual bottleneck. Skipping decomposition is optimization theater.
Viral is not a rescue plan for a bad product. The 4 viral mistakes are explicit: if the product isn't inherently viral, or if the product is bad, virality won't save it — it will accelerate the decline. WHY: This is the most common founder error. Virality is leverage, and leverage works in both directions.
Cycle time matters as much as K. A 7-day cycle and a 1-day cycle with the same K produce radically different growth curves. Shortening cycles is often easier than raising K. WHY: Compounding is about iteration count, not just multiplier. Fast cycles compound more iterations per unit time.
Budget 2-3 months for serious viral work. "Expert teams need 1-2 engineers for 2-3 months minimum to implement and optimize a new viral channel." Viral is not a weekend project. WHY: Shortcuts on viral engineering produce broken loops that look right but don't compound. The time budget is the floor, not the ceiling.
Examples
Scenario: File-sharing SaaS adding a referral program
Trigger: "We're building Dropbox-for-teams. Want to add a referral program. How should it work?"
Process: (1) Loop type: Incentivized Virality fits (Dropbox's original model). Alternative: Collaborative Virality since teams use it together. Decision: combine both — team invites trigger collaborative flow, external referrals get storage credits. (2) Estimate K: assume i=2 (each user invites 2 on average), conversion 30% → K=0.6. Meaningful but not exponential. (3) Decompose: if click-through is 60% and signup is 50%, the weakest variable is signup — optimize that first. (4) 4 mistakes check: product is genuinely collaborative (not mistake 1), product works (not mistake 2), plan weekly A/B tests (not mistake 3), mechanics match how teams actually invite colleagues (not mistake 4). (5) Cycle time: trigger invite moment at "share file with external user" action (natural moment), reward appears at next login (fast).
Output: Clear implementation plan with incentive structure, estimated K baseline, and optimization priority on signup conversion.
Scenario: Consumer app with K=0.2 — is viral the channel?
Trigger: "We added a referral feature to our mobile game. Measured K over 30 days: K=0.2. What should we do?"
Process: (1) Loop type: check if current mechanics match the product. If users aren't naturally discussing the game with friends, the incentivized loop was bolted on. (2) K=0.2 is below the 0.5 threshold — viral is not a primary channel. (3) Decompose: low i (users aren't sending invites at all)? Low conversion (invitees click but don't install)? Decomposition reveals the problem. (4) 4 mistakes check: is the product inherently viral? For a mobile game, only if it's multiplayer or has leaderboards. If single-player, viral mechanics are fighting the product's nature. (5) Recommendation: return to Bullseye. Viral as supporting channel only, not primary.
Output: Honest assessment that viral isn't the channel, recommendation to re-run Bullseye with this data.
Scenario: B2B SaaS considering collaborative virality
Trigger: "We built a spreadsheet-like analytics tool. Think Figma for data. Should we make it viral?"
Process: (1) Loop type: Collaborative Virality is the clear fit — the product works alone but is 10x more valuable when shared with colleagues. (2) Baseline unknown, but plan the metrics: measure share action rate, external-user signup rate. (3) Decompose from day one: i, click-through, signup separately. (4) 4 mistakes check: product IS inherently collaborative ✓, product quality TBD, budget 2 engineers × 3 months, mechanics match how Figma does it (invite = real seat, not just a link). (5) Cycle time: optimize "share moment" UX so it happens naturally mid-workflow, not as a separate step.
Output: Loop type decision, Figma-inspired mechanics plan, instrumentation requirements for baseline measurement.
References
- For the full 7-type viral loop taxonomy with examples, see references/viral-loop-types.md
- For the 4 viral mistakes in detail, see references/viral-mistakes.md
License
This skill is licensed under CC-BY-SA-4.0. Source: BookForge — Traction: A Startup Guide to Getting Customers by Gabriel Weinberg and Justin Mares.
Related BookForge Skills
Install related skills from ClawhHub:
clawhub install bookforge-bullseye-channel-selection— Select viral deliberately, don't default to itclawhub install bookforge-traction-channel-testing— Baseline K and A/B test disciplineclawhub install bookforge-content-and-email-marketing— Referral emails are part of the viral loop
Or install the full book set from GitHub: bookforge-skills