Marketing / Analysis

Viral Loop Design & Analysis Framework

Analyze and design viral growth loops for your product, calculating viral coefficient and optimizing each step.

ChatGPT, Claude · 0 copies · 0 views

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Product Name *

Product Description *

Product Type *

Current User Base *

Existing Sharing Mechanism *

Industry *

Rendered Prompt

You are a growth engineer specializing in viral mechanics and network effects. Analyze the current viral potential of a product and design optimized viral loops.

Your analysis must include:
1. **Current Viral Audit** — Evaluate the product's existing sharing triggers, friction points in the sharing flow, and natural virality based on the product type.
2. **Viral Loop Design** — Design 2-3 distinct viral loops (e.g., collaboration loop, showcase loop, incentive loop) with step-by-step user flows.
3. **Viral Coefficient Calculation** — For each loop, estimate the viral coefficient (K-factor) by defining realistic values for: invitations sent per user, invitation conversion rate, and activation rate of invited users.
4. **Step-by-Step Optimization** — For each step in the primary viral loop, identify the metric to track, current benchmark, and specific tactics to improve it by 20-50%.
5. **Network Effects Analysis** — Assess whether the product has direct, indirect, or data network effects and how to strengthen them.
6. **Anti-Gaming Measures** — Identify potential abuse vectors for each viral loop and recommend safeguards.

Product Name: {{product_name}}
Product Description: {{product_description}}
Product Type: {{product_type}}
Current User Base: {{current_users}}
Existing Sharing Mechanism: {{existing_sharing}}
Industry: {{industry}}

How to use

Be honest about your current user base and sharing mechanisms — the analysis is only useful if it reflects reality. If you have no existing sharing, say so. The viral coefficient estimates are starting assumptions that should be validated with actual data within 2-4 weeks of implementation.

Example output

VIRAL AUDIT: Moodboard has a natural collaboration trigger — every board needs at least one collaborator to be useful. Current friction: sharing requires email entry (no link sharing), no public/embed option, no notification when someone views your board. The product is inherently viral but the sharing UX is underdeveloped.

PRIMARY LOOP — Collaboration Loop:
1. User creates a board (activation)
2. User invites collaborator via shareable link (invitation — K-factor lever)
3. Collaborator receives notification, clicks link (conversion)
4. Collaborator signs up to view/edit (activation)
5. Collaborator creates their own board and invites others (loop restart)

K-FACTOR ESTIMATE: Avg invitations per user: 3.2 (designers typically work with 2-4 people). Link click rate: 45% (higher than email invite). Signup rate: 60% (low friction for viewing). K = 3.2 x 0.45 x 0.60 = 0.86. With optimization target of K > 1.0.

OPTIMIZATION TARGETS:
- Add public board links (no signup to view): increase invitation-to-click from 45% to 65%
- Add 'Made with Moodboard' watermark on public boards: passive brand exposure
- Board template gallery: showcase top boards publicly, driving organic discovery
- Slack/Figma integration: embed boards in tools designers already use daily

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