Cac Modeler

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:

LTV Estimation

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

LTV Formula

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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):

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

LTV Confidence

CAC by Channel

Indie Budget Tiers

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

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:

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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

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":

LTV:CAC Ratio Thresholds

After computing LTV and per-channel CAC, classify each 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?"

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payback_months = CAC / ARPU_monthly

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

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

Viability Verdict

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:

jsonCopy code
{
  "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.