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.

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 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."