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.
<!-- version: 0.2.0 | outputs: memory/ideas/<slug>/distribution.json -->
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
- Identify which loop type(s) apply to the app concept.
- Estimate i (invitations per user) — consider: does the core UX prompt sharing? How often? To how many people?
- Estimate c (conversion per invitation) — consider: how compelling is the share artifact? Does the recipient need the app to view it?
- Compute k = i × c.
- 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:
- Uses a newly released Apple/Google platform feature (widgets, Live Activities, visionOS, AI APIs)
- Has exceptional design quality (would look good in an editorial story)
- Serves an underrepresented audience or emerging cultural moment
- Has a clear positive-impact or wellness angle
- 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 utility app wastes money.
Creator fit criteria
| Factor | Score: High | Score: Medium | Score: Low |
|---|
| Content generation | App produces visual or shareable output that IS the content (before/after, results, transformations) | App experience is interesting to narrate/demonstrate | App is invisible — nothing to show on camera |
| Audience alignment | Clear niche creator communities already talk about this problem space | Adjacent creator communities exist | No creator community maps to this product |
| Demo-ability | Can be demonstrated in a 30–60 second clip with visible value | Needs 2–3 minute explanation to convey value | Requires hands-on usage over days to appreciate |
| Authenticity | Creator would genuinely use the app (not just shill for money) | Creator could plausibly use it occasionally | Feels forced — creator has no real use case |
| Affiliate/monetization fit | App has a price point that supports affiliate commissions ($5+/mo or $20+ one-time) | Freemium with conversion — harder to attribute | Free app with no monetization — no creator incentive |
Scoring: Count High/Medium/Low across all 5 factors.
- high fit: 3+ factors scored High
- medium fit: 2 factors High, or 3+ Medium
- low fit: 2+ factors Low, or no factors High
Step 4 — Paid Channel Feasibility
Assess whether paid acquisition can work within indie budget constraints.
| Budget tier | Monthly ad spend | Viable paid strategies |
|---|
| Micro (< $200/mo) | Testing only | One platform, 2–3 ad creatives, learn CPM/CPI before scaling. Not a primary channel. |
| Light (< $500/mo) | Targeted campaigns | One platform with lookalike audiences. Can work if CPI < $2 and LTV > $6. |
| Moderate (< $2000/mo) | Real optimization | Multi-creative testing, retargeting. Viable if LTV:CAC > 3:1 on at least one platform. |
If budget_constraint from user profile is "low", cap paid feasibility at "marginal" regardless of other factors — the user cannot sustain the learning curve of paid acquisition.
Step 5 — Founder Distribution Edge
Cross-reference user_profile.md to identify whether the founder has a pre-existing distribution advantage:
| Advantage type | Impact |
|---|
| Existing audience (newsletter, social, YouTube) | Direct launch channel — reduces cold-start risk significantly |
| Community membership (active in relevant subreddits, Discord, forums) | Warm audience for validation and early adopters |
| Content creation skills (video, writing, design) | Can execute organic content channels without outsourcing |
| Technical SEO / ASO experience | Can capitalize on search-driven channels faster |
| Industry relationships | Potential for partnerships, cross-promotion, press |
| None identified | Must rely on product-led or paid growth — harder path |
Step 6 — Distribution Verdict
Compute the overall verdict by evaluating all dimensions together, then adjust for founder tier.
Raw verdict logic
| Condition | Raw verdict |
|---|
| k-factor ≥ 0.5 OR (ASO = high AND creator_fit = high) OR founder has existing audience | strong |
| k-factor ≥ 0.2 AND at least one other dimension scores medium+ | moderate |
| All dimensions low/marginal, no organic path, paid not viable at budget | weak |
Tier adjustment
The same distribution profile means different things to different founders. Apply this adjustment:
| Founder tier | Adjustment |
|---|
| beginner | Downgrade verdict by one level if the only viable channels require technical skill (SEO, paid optimization, ASO keyword research). Beginners need channels with fast feedback loops: TikTok organic, community posting, referral-based growth. Flag complex channels as "aspirational — learn first." |
| builder | No adjustment. Builders can execute most channels with some learning curve. Flag paid channels > $500/mo as risky given typical builder budgets. |
| growth | Upgrade verdict by one level if paid channels are viable and the founder has optimization experience. Growth-tier founders can unlock channels that are traps for beginners. |
If user_profile.md is unavailable, skip tier adjustment and note it as a gap.
Output
Write to memory/ideas/<slug>/distribution.json:
{
"organic_reach_potential": "high | medium | low",
"viral_loop_exists": false,
"viral_loop_type": "inherent | collaborative | word-of-mouth | incentivized | content-as-distribution | none",
"viral_loop_description": "",
"k_factor_estimate": 0.0,
"k_factor_classification": "viral-growth-engine | viral-assist | marginal | non-viral",
"paid_feasibility": "viable | marginal | not-viable",
"minimum_paid_budget_monthly": 0,
"paid_feasibility_rationale": "",
"platform_advantage": {
"aso_opportunity": "high | medium | low",
"aso_score_breakdown": {
"category_competition": 0,
"keyword_opportunity": 0,
"search_intent_match": 0,
"review_velocity_potential": 0,
"visual_differentiation": 0,
"total": 0
},
"featured_potential": false,
"featured_criteria_met": []
},
"creator_economy_fit": "high | medium | low",
"creator_fit_rationale": "",
"creator_fit_breakdown": {
"content_generation": "high | medium | low",
"audience_alignment": "high | medium | low",
"demo_ability": "high | medium | low",
"authenticity": "high | medium | low",
"affiliate_fit": "high | medium | low"
},
"user_distribution_advantage": "",
"user_advantage_type": "audience | community | content-skills | seo-aso | relationships | none",
"recommended_first_channel": "",
"recommended_first_channel_rationale": "",
"channels_ranked": [
{ "channel": "", "viability": "high | medium | low", "time_to_first_100_users": "" }
],
"distribution_verdict": "strong | moderate | weak",
"tier_adjustment_applied": "",
"distribution_verdict_rationale": ""
}
Notes
- The
recommended_first_channel should always be the highest-viability channel the founder can realistically execute given their tier. Don't recommend "TikTok organic" to someone who has never made a video; don't recommend "ASO" to someone who doesn't know what keywords are.
- If
competitors.json is available, check competitor distribution strategies — an app succeeding via a channel the founder can replicate is a strong positive signal.
- k-factor estimates are inherently speculative pre-launch. Treat them as directional, not precise. Flag any estimate above 0.5 as "optimistic until validated."