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Skill: cac-modeler

Purpose

Determine whether this indie developer can realistically acquire users profitably given their budget and the competitive landscape. Many ideas fail not because of bad products but because CAC exceeds LTV at indie scale. This skill builds a complete unit economics picture: LTV from retention and pricing, CAC from channel benchmarks calibrated by market_insights signals, and a payback timeline that tells the founder how long they need to fund growth before the business sustains itself.

Input

• Idea slug
• memory/user_profile.md (budget constraint, ICP tier)
• memory/ideas/<slug>/pricing.json (target price → revenue per user)
• memory/ideas/<slug>/retention.json (D30 retention, churn risk → estimated lifespan)
• memory/ideas/<slug>/distribution.json (viable channels, k-factor, ASO opportunity, creator fit)
• memory/ideas/<slug>/competitors.json (competitor pricing and scale signals — optional)
• memory/market_insights/<niche>-*-<YYYY>-<MM>.md (trend data — use all available platform files)

Using Market Insights

Trend analysis files provide critical calibration for CAC estimates. Extract the following:

Field	How it informs CAC
trend_velocity	Rising markets have lower organic CAC (more discovery demand) but higher paid CAC (more advertisers bidding). Declining markets have the inverse.
top_signals (TikTok hashtags, Reddit threads, App Store categories)	Validate which organic channels have real activity — a niche trending on TikTok means TikTok organic CAC is at the lower end of the range
monetization_evidence	If competitors are already running ads (visible in trend narratives), paid CPMs in that niche are likely elevated. Adjust paid CAC estimates upward.
Platform narrative (Reddit pain points, creator engagement)	Identifies which communities are already activated — lower cost to reach an audience that's already discussing the problem

LTV Estimation

LTV is the foundation. Without an accurate LTV, CAC ratios are meaningless.

LTV Formula

LTV = ARPU_monthly × average_lifespan_months

Where:

• ARPU_monthly (Average Revenue Per User per month):

  • Subscription: target_price × (1 - churn_rate_monthly)
  • Freemium: target_price × freemium_conversion_estimate
  • One-time purchase: price / 12 (annualized for comparison)
  • Consumables: average_monthly_spend (estimate from category)

• average_lifespan_months (derived from retention.json):

D30 retention	Estimated avg lifespan	Rationale
≥ 25%	12–18 months	Strong retention; users who survive D30 tend to stay long
15–24%	6–12 months	Decent; typical for well-executed niche apps
8–14%	3–6 months	Below average; expect significant churn in months 2–3
< 8%	1–3 months	Disposable; most users gone within a billing cycle

If retention.json is unavailable, use category median D30 benchmarks:

Category	Median D30
Social / messaging	15–25%
Health & fitness	10–18%
Finance / budgeting	12–20%
Productivity / tools	8–15%
Games (casual)	5–12%
Education	6–12%
Lifestyle / habit	10–18%
Creative tools	12–20%

LTV Confidence

Data available	Confidence
pricing.json + retention.json with D30 data	High
pricing.json only (using category median retention)	Medium
Neither (using category defaults for both)	Low — flag prominently

CAC by Channel

Indie Budget Tiers

Define the founder's budget context before estimating per-channel CAC:

Tier	Monthly ad/marketing spend	Who this is	Implication
Bootstrap	$0–$100/mo	Beginner or side-project builder	Paid channels are off the table. Must rely entirely on organic.
Lean	$100–$500/mo	Builder with some runway	Can test one paid channel with tight creative constraints.
Moderate	$500–$2,000/mo	Growth-tier or funded builder	Can run proper paid campaigns with A/B testing on one platform.
Serious	> $2,000/mo	Rare for indie; growth stage	Multi-platform paid, retargeting, influencer budgets.

Map from user_profile.md: budget_constraint = "low" → Bootstrap. "medium" → Lean. "high" → Moderate or Serious (ask if ambiguous).

Channel CAC Estimation

For each channel, estimate CAC using the funnel model:

CAC = cost_per_impression / (CTR × install_rate × activation_rate)

For organic channels, "cost" is time-valued at $0 but the skill reports the effective CAC — the opportunity cost of the founder's time, normalized per acquired user.

Channel benchmarks with market_insights adjustments

Channel	Base CAC range	Adjust down if	Adjust up if
ASO organic	$0.50–$3.00	ASO opportunity = "high" (from distribution.json); niche category with low competition	Saturated category; market_saturation = "high" from competitors.json
Content / SEO	$1.00–$8.00	Niche has high search volume with low-quality top results; rising or rising-fast trend velocity	Competitive keywords dominated by established brands
TikTok organic	$0.50–$5.00	Niche is trending on TikTok (visible in market_insights top_signals); app produces shareable output (content-as-distribution loop)	Low TikTok engagement for this category; no visual hook
Reddit / community	$0.50–$4.00	Active communities discussing this problem (from Reddit market_insights); founder is an active community member	Small or inactive communities; product is hard to discuss authentically
Paid social (Meta)	$3.00–$40.00	Broad audience, visual product, low CPM niche	Competitive niche with high CPMs; narrow targeting required
Paid social (TikTok)	$2.00–$25.00	Trending niche (lower CPMs due to content volume); strong creative hook	Niche with limited content; poor demo-ability
Influencer / creator	$2.00–$25.00	Creator economy fit = "high" (from distribution.json); micro-influencers available in niche	Low creator fit; only macro-influencers relevant (expensive)
Word of mouth / referral	$0.00–$2.00	k-factor ≥ 0.3 (from distribution.json); inherent or collaborative viral loop	k-factor < 0.1; no natural sharing mechanic
Press / Product Hunt	$0.00–$5.00	Novel concept with clear narrative; uses new platform feature	Crowded launch day; "me too" product

Press/Product Hunt provides a one-time spike, not sustained acquisition. Model it as a fixed user cohort (typically 500–5,000 installs), not a recurring channel.

Channel Relevance Filter

Not all channels apply to every idea. Skip channels that score "not applicable":

Skip condition	Channels to exclude
App has no visual output or demo hook	TikTok organic, influencer
budget_constraint = "low" (Bootstrap tier)	Paid social (both), influencer (unless micro/barter)
No relevant online communities exist	Reddit / community
viral_loop_exists = false AND k_factor < 0.1	Word of mouth / referral
Utility app with no narrative angle	Press / Product Hunt

LTV:CAC Ratio Thresholds

After computing LTV and per-channel CAC, classify each channel:

LTV:CAC ratio	Classification	Meaning
≥ 5:1	Excellent	Strong unit economics. Scale this channel aggressively.
3:1–5:1	Healthy	Viable and sustainable. Standard target for indie apps.
2:1–3:1	Marginal	Barely works. Viable only if the founder can optimize over time or retention improves.
1:1–2:1	Unprofitable	Losing money after overhead. Not viable unless LTV increases significantly.
< 1:1	Cash burn	Every user costs more than they ever return. Do not use this channel.

A minimum of one channel at ≥ 3:1 is required for the overall viability verdict to be "viable."

Payback Period Calculation

Payback period answers: "How many months until a user has paid back their acquisition cost?"

payback_months = CAC / ARPU_monthly

Report the payback period for the recommended first channel and any channel with LTV:CAC ≥ 3:1.

Payback period	Assessment
≤ 1 month	Excellent — cash flow positive almost immediately
1–3 months	Good — sustainable for a funded indie builder
3–6 months	Acceptable — requires patience and runway
6–12 months	Risky — the founder needs alternative income during this period
> 12 months	Dangerous — cash flow negative for over a year. Not viable at indie scale unless lifetime deal covers upfront cost.

For Bootstrap tier founders, any payback period > 3 months is a red flag — they likely can't sustain the cash gap.

Viability Verdict

Condition	Verdict
At least 2 channels with LTV:CAC ≥ 3:1, at least one organic	viable
Exactly 1 channel with LTV:CAC ≥ 3:1, OR organic channels at 2:1–3:1 with improvement potential	marginal
No channel achieves LTV:CAC ≥ 2:1, OR only paid channels viable but founder is Bootstrap tier	not-viable

If market_insights show trend_velocity = "rising-fast", add a note that organic CAC may improve as the market grows (more search volume, more platform promotion of trending content). This is speculative but worth flagging.

Process

1. Load all inputs: pricing.json, retention.json, distribution.json, competitors.json, user_profile.md, and all matching memory/market_insights/<niche>-*-<YYYY>-<MM>.md files.
2. Extract market_insights calibration signals (trend velocity, platform activity, monetization evidence, competitor ad activity).
3. Determine founder's budget tier from user_profile.md.
4. Compute LTV using the formula, pricing data, and retention data. Note confidence level.
5. For each applicable channel (after relevance filter), estimate CAC using benchmarks adjusted by market_insights and distribution.json signals.
6. Compute LTV:CAC ratio per channel. Classify each.
7. Compute payback period for viable channels.
8. Rank channels by LTV:CAC ratio. Select recommended first channel — must be executable by this founder at their budget tier.
9. Determine viability verdict.
10. Write output.

Output

Write to memory/ideas/<slug>/cac.json:

{
  "ltv": {
    "estimated_ltv": 0,
    "arpu_monthly": 0,
    "average_lifespan_months": 0,
    "ltv_confidence": "high | medium | low",
    "ltv_assumptions": []
  },
  "founder_budget_tier": "bootstrap | lean | moderate | serious",
  "cac_by_channel": {
    "aso_organic": { "cac": 0, "ltv_cac_ratio": 0, "classification": "", "payback_months": 0 },
    "content_seo": { "cac": 0, "ltv_cac_ratio": 0, "classification": "", "payback_months": 0 },
    "tiktok_organic": { "cac": 0, "ltv_cac_ratio": 0, "classification": "", "payback_months": 0 },
    "reddit_community": { "cac": 0, "ltv_cac_ratio": 0, "classification": "", "payback_months": 0 },
    "paid_social_meta": { "cac": 0, "ltv_cac_ratio": 0, "classification": "", "payback_months": 0 },
    "paid_social_tiktok": { "cac": 0, "ltv_cac_ratio": 0, "classification": "", "payback_months": 0 },
    "influencer": { "cac": 0, "ltv_cac_ratio": 0, "classification": "", "payback_months": 0 },
    "word_of_mouth": { "cac": 0, "ltv_cac_ratio": 0, "classification": "", "payback_months": 0 },
    "press_product_hunt": { "cac": 0, "ltv_cac_ratio": 0, "classification": "", "payback_months": 0, "one_time_cohort_estimate": 0 }
  },
  "skipped_channels": [],
  "viable_channels": [],
  "marginal_channels": [],
  "non_viable_channels": [],
  "recommended_first_channel": "",
  "recommended_first_channel_rationale": "",
  "payback_period_months": 0,
  "market_insights_adjustments": [],
  "viability_verdict": "viable | marginal | not-viable",
  "viability_verdict_rationale": ""
}

Notes

• The recommended_first_channel must be achievable by the founder at their current tier. Don't recommend paid social to a Bootstrap founder. Don't recommend Reddit community marketing to someone with no community presence. Cross-reference user_profile.md distribution advantages.
• If retention.json is unavailable, LTV confidence drops to medium at best. Flag this prominently — CAC ratios are only as good as the LTV estimate, and LTV depends entirely on retention.
• When distribution.json shows a strong viral loop (k-factor ≥ 0.3), the effective CAC for word-of-mouth should account for the viral multiplier: effective_CAC = base_CAC / (1 / (1 - k)). A k-factor of 0.5 halves the effective CAC.
• Press/Product Hunt is not a channel strategy — it's a launch event. Model it as a one-time cohort (estimate 500–5,000 installs) and do not include it in recurring channel viability.
• If all market_insights files are past their stale_after date, note that CAC benchmarks may have shifted and recommend refreshing trend analysis.