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/ideas/<slug>/idea.md (business_model)
• memory/user_profile.md (budget constraint, ICP tier)
• memory/ideas/<slug>/pricing.json (target price or primary tier → revenue per user; trial-to-paid or freemium conversion)
• 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:

Lane selection

business_model = b2c uses the B2C channel table and the D30-based lifespan. prosumer, b2b-smb, and b2b2c use the B2B channel table, the churn-based lifespan, gross margin, and the three-case LTV. Record the lane in the output.

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):

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

B2B lane LTV

avg_lifetime_months = 1 / monthly_churn_estimate      (from retention.json)
gross_ltv           = ARPU_monthly × avg_lifetime_months
contribution_ltv    = gross_ltv × gross_margin

• gross_margin: 0.80–0.90 for software with light support; 0.65–0.80 when the product carries API, model, or storage cost per customer; state the assumption.
• Model three cases (ltv_cases): base (retention.json midpoint), optimistic (churn at the low end of the segment band with the condition that makes it true), pessimistic (churn at the high end, with the condition). Use contribution_ltv for every ratio. A channel is only "viable" if it clears 3:1 in the base case; report the pessimistic ratio beside it.
• ARPU uses the primary tier price; add expected expansion (per-client or per-seat overage) only when the pricing file documents it, and say how much.

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). "unknown" → Bootstrap, and write founder_budget_tier: "unknown" so the memo lists the gap.

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

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.

B2B lane channel benchmarks (heuristic ranges for solo founders selling to SMB, agencies, and prosumers)

Blended CAC for a bootstrapped B2B SaaS that reaches $10k MRR typically lands at $300–900 (heuristic from public Indie Hackers and founder posts); if the model produces under $150 blended beyond the warm network, the estimate is optimistic.

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?"

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.

What this verdict does and does not measure

The viability verdict measures unit economics only: whether an acquired customer returns more than it cost. It says nothing about whether those customers can be reached, how many of them exist, or whether the channel is open to this founder. Channel reachability is scored in distribution-analysis, market size in tam-sam-som-builder, and the two must be read alongside this file.

This matters because the combination is common: a business can post healthy ratios on channels that cap out at a few dozen customers. Record blended_ceiling_customers beside the verdict so the limit is visible at a glance, and state the pairing explicitly in viability_verdict_rationale whenever the verdict is viable but the ceiling is low.

Deriving blended_ceiling_customers: sum the realistic customer ceilings of every channel classified viable or marginal, excluding one-time launch cohorts, which are events rather than channels. Express it in customers so it can be read directly against the SOM in market_size.json. Where a ceiling is a range, use the midpoint. Where a channel is blocked rather than merely limited, contribute zero and say why.

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:

{
  "idea_slug": "",
  "modeled_at": "YYYY-MM-DD",
  "lane": "b2c | b2b",
  "ltv": {
    "estimated_ltv": 0,
    "arpu_monthly": 0,
    "average_lifespan_months": 0,
    "ltv_confidence": "high | medium | low",
    "ltv_assumptions": []
  },
  "gross_margin": null,
  "ltv_cases": {
    "base": { "monthly_churn": 0.0, "avg_lifetime_months": 0, "gross_ltv": 0, "contribution_ltv": 0 },
    "optimistic": { "monthly_churn": 0.0, "avg_lifetime_months": 0, "gross_ltv": 0, "contribution_ltv": 0, "condition": "" },
    "pessimistic": { "monthly_churn": 0.0, "avg_lifetime_months": 0, "gross_ltv": 0, "contribution_ltv": 0, "condition": "" }
  },
  "founder_budget_tier": "bootstrap | lean | moderate | serious | unknown",
  "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 }
  },
  "cac_by_channel_b2b": [
    { "channel": "", "estimated_cac": 0, "cac_basis": "", "ltv_cac_base": 0, "ltv_cac_pessimistic": 0, "payback_months": 0, "viability": "viable | marginal | not-viable", "scale_ceiling": "" }
  ],
  "skipped_channels": [],
  "viable_channels": [],
  "marginal_channels": [],
  "non_viable_channels": [],
  "recommended_first_channel": "",
  "recommended_first_channel_rationale": "",
  "payback_period_months": 0,
  "revenue_scenarios": {
    "conservative_12mo": { "customers": 0, "blended_arpu": 0, "mrr": 0, "assumption": "" },
    "base_12mo": { "customers": 0, "blended_arpu": 0, "mrr": 0, "assumption": "" }
  },
  "market_insights_adjustments": [],
  "viability_verdict": "viable | marginal | not-viable",
  "viability_verdict_rationale": "",
  "blended_ceiling_customers": 0,
  "blended_ceiling_basis": "",
  "critical_caveats": [],
  "sources": [
    { "url": "https://", "title": "", "accessed": "YYYY-MM-DD", "used_for": "" }
  ]
}

Fill cac_by_channel (object) in the B2C lane or cac_by_channel_b2b (array) plus ltv_cases and gross_margin in the B2B lane; omit the other. revenue_scenarios is optional but recommended in the B2B lane. sources lists benchmark pages, founder revenue posts, and CPC data consulted.

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.