/cac-modeler

name: cac-modeler
description: Models LTV, CAC by channel, LTV:CAC ratios, and payback period for an indie developer. Uses market_insights to calibrate channel CPMs and competitive intensity. Includes indie budget tier definitions and viability thresholds.

<!-- version: 0.2.0 | outputs: memory/ideas/<slug>/cac.json -->

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 (Me