/distribution-analysis

name: distribution-analysis
description: Evaluates organic reach potential, paid feasibility, platform distribution advantages, creator economy fit, and founder edge for a B2C app idea. Includes viral coefficient estimation, ASO scoring rubric, and tier-adjusted verdicts.

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Skill: distribution-analysis

Purpose

Distribution is the most underestimated factor in indie app success. A mediocre product with great distribution beats a great product with no distribution. This skill evaluates all realistic paths to users and adapts its verdict to the founder's tier — a channel that works for a growth-stage operator can be a trap for a beginner.

Input

• Idea slug
• `memory/user_profile.md` (ICP tier, distribution advantages, budget constraint)
• `memory/ideas/<slug>/idea.md` (app concept, key features, differentiator)
• Optional: `memory/ideas/<slug>/competitors.json` (competitor distribution signals)

Distribution Dimensions

| Dimension | Questions to Answer | |---|---| | Organic reach | Can this spread without paid spend? Is there a viral loop? What's the estimated viral coefficient? | | Paid feasibility | Can paid ads break even at indie scale? What's the minimum viable budget? | | Platform advantage | Is there an ASO moat? App Store featured potential? Category competitiveness? | | Creator economy fit | Can influencers or creators promote this authentically? Does the app produce shareable output? | | User's distribution edge | Does the user have an existing audience, community, or channel expertise? |

Process

Step 1 — Viral Coefficient Estimation

The viral coefficient (k-factor) predicts whether an app can grow organically through user referrals. Estimate k = i × c where:

• **i** = average number of invitations/shares per user
• **c** = conversion rate of each invitation

Viral loop identification

Evaluate the app concept against these loop types:

| Loop type | Description | Typical k-factor | Example | |---|---|---|---| | Inherent | Product is useless alone, requires inviting others | 0.5–1.5 | Multiplayer games, shared lists | | Collaborative | Better with others but works solo | 0.2–0.6 | Workout trackers with friends, shared budgets | | Word-of-mouth | Users talk about it because it's remarkable | 0.1–0.4 | Apps that produce "wow" output (AI art, unique insights) | | Incentivized | Users get a reward for referring | 0.1–0.3 | Referral credits, unlocked features | | Content-as-distribution | App output is inherently shareable on social platforms | 0.3–0.8 | Photo editors with watermarks, personality quizzes, wrapped/recap screens | | None | No natural reason to share | 0.0–0.05 | Utility apps (calculators, timers) |

Estimation rubric

1. Identify which loop type(s) apply to the app concept. 2. Estimate **i** (invitations per user) — consider: does the core UX prompt sharing? How often? To how many people? 3. Estimate **c** (conversion per invitation) — consider: how compelling is the share artifact? Does the recipient need the app to view it? 4. Compute k = i × c. 5. Classify:

| k-factor | Classification | |---|---| | k ≥ 0.7 | **Viral growth engine** — organic growth is a primary acquisition channel | | 0.3 ≤ k < 0.7 | **Viral assist** — referrals supplement other channels meaningfully | | 0.1 ≤ k < 0.3 | **Marginal virality** — some word-of-mouth, not a growth driver | | k < 0.1 | **Non-viral** — growth depends entirely on other channels |

> k ≥ 1.0 means every user brings in at least one more user on average — true exponential growth. This is rare for indie apps; be skeptical of estimates above 0.8 unless the app has an inherent or content-as-distribution loop.

Step 2 — ASO Potential Scoring

App Store Optimization is the highest-leverage free channel for indie developers. Score ASO opportunity on a 3-tier rubric:

ASO scoring rubric

| Factor | High (3 pts) | Medium (2 pts) | Low (1 pt) | |---|---|---|---| | **Category competition** | Niche category, top 10 achievable with <500 ratings | Moderate category, top 50 achievable | Saturated category, dominated by incumbents with 100K+ ratings | | **Keyword opportunity** | High-volume keywords with low-rated top results (< 4.2 stars, < 1K ratings) | Keywords exist but top results are solid (4.5+ stars) | All relevant keywords dominated by well-known brands | | **Search intent match** | Users actively search for this exact solution (tool/utility intent) | Users search for the category but not this specific angle | Discovery-dependent — users don't know they want this | | **Review velocity potential** | App has natural prompt moments for asking reviews (completed task, achievement) | Some prompt moments but not in core loop | No natural review prompt; must interrupt to ask | | **Visual differentiation** | App icon and screenshots can stand out (unique aesthetic, bold output previews) | Decent but similar to competitors | Looks like every other app in the category |

**ASO score**: Sum of all factors (5–15 points).

| Total | ASO opportunity | |---|---| | 12–15 | **high** — ASO should be primary acquisition channel | | 8–11 | **medium** — ASO is viable but won't be the sole driver | | 5–7 | **low** — ASO alone won't generate meaningful installs |

Featured potential checklist

An app has App Store featured potential if it meets **3+ of these 5 criteria**:

1. Uses a newly released Apple/Google platform feature (widgets, Live Activities, visionOS, AI APIs) 2. Has exceptional design quality (would look good in an editorial story) 3. Serves an underrepresented audience or emerging cultural moment 4. Has a clear positive-impact or wellness angle 5. Is a premium/indie app (Apple editorially favors paid apps and small teams)

Step 3 — Creator Economy Fit Assessment

Evaluate whether influencers and creators can authentically promote the app. Not all apps are "creator-friendly" — forcing influencer marketing on a