{
  "slug": "paid-vs-blended-cac-decomposition",
  "title": "Paid vs. Blended CAC Decomposition Calculator",
  "heading": "Paid vs. Blended CAC Calculator",
  "category": "financial",
  "url": "https://www.revenuelab.fyi/toolbox/paid-vs-blended-cac-decomposition",
  "summary": "See how much organic and referral traffic is masking your real paid acquisition cost.",
  "description": "Blended CAC (total spend divided by total new customers) looks healthy when a strong organic or referral engine is doing free work alongside paid media, but it hides whether your paid channels are actually efficient on their own. This calculator splits new customers into paid-attributed and organic/referral-attributed buckets, then computes paid CAC (spend ÷ paid customers only) and blended CAC (spend ÷ all customers) side by side, along with the ratio between them. A wide gap — paid CAC at 3x blended CAC, for example — means your reported 'CAC is fine' story depends heavily on brand equity and word of mouth that paid spend isn't creating and can't easily scale. It also flags what happens to blended CAC if organic share erodes, which matters when you're forecasting the payback math for a funding round or budget request. Use this alongside a proper incrementality test, since organic conversions attributed via last-click can themselves be inflated by paid brand search cannibalizing what would have been a free direct visit.",
  "formula": "Paid CAC = paid spend ÷ paid-attributed customers; Blended CAC = paid spend ÷ (paid + organic customers); Dependency ratio = Paid CAC ÷ Blended CAC.",
  "dateModified": "2026-09-30",
  "run_url": "https://www.revenuelab.fyi/api/public/calc?tool=paid-vs-blended-cac-decomposition",
  "inputs": [
    {
      "id": "spend",
      "label": "Total paid marketing spend",
      "kind": "number",
      "hint": null,
      "default": 120000,
      "unit": "$",
      "min": 0,
      "max": null
    },
    {
      "id": "paidCustomers",
      "label": "New customers attributed to paid",
      "kind": "number",
      "hint": null,
      "default": 800,
      "unit": null,
      "min": 1,
      "max": null
    },
    {
      "id": "organicCustomers",
      "label": "New customers from organic/referral/direct",
      "kind": "number",
      "hint": null,
      "default": 1200,
      "unit": null,
      "min": 0,
      "max": null
    },
    {
      "id": "ltv",
      "label": "Average customer lifetime value",
      "kind": "number",
      "hint": null,
      "default": 450,
      "unit": "$",
      "min": 0,
      "max": null
    }
  ],
  "outputs": [
    {
      "id": "paidCac",
      "label": "Paid-only CAC",
      "format": "currency",
      "hint": null,
      "primary": true
    },
    {
      "id": "blendedCac",
      "label": "Blended CAC",
      "format": "currency",
      "hint": null,
      "primary": false
    },
    {
      "id": "ratio",
      "label": "Paid ÷ blended dependency ratio",
      "format": "decimal",
      "hint": null,
      "primary": false
    },
    {
      "id": "paidLtvCac",
      "label": "LTV:CAC on paid alone",
      "format": "decimal",
      "hint": null,
      "primary": false
    },
    {
      "id": "blendedLtvCac",
      "label": "LTV:CAC blended",
      "format": "decimal",
      "hint": null,
      "primary": false
    }
  ],
  "worked_example": {
    "inputs": [
      "Total paid marketing spend: 120000 $",
      "New customers attributed to paid: 800",
      "New customers from organic/referral/direct: 1200",
      "Average customer lifetime value: 450 $"
    ],
    "outputs": [
      "Paid-only CAC: $150",
      "Blended CAC: $60",
      "Paid ÷ blended dependency ratio: 2.5",
      "LTV:CAC on paid alone: 3",
      "LTV:CAC blended: 7.5"
    ]
  },
  "how_to": {
    "title": "How to use this",
    "steps": [
      "Enter total paid marketing spend ($).",
      "Enter new customers attributed to paid.",
      "Enter new customers from organic/referral/direct.",
      "Enter average customer lifetime value ($).",
      "Read your paid-only cac on the right — it updates as you type.",
      "Hit Share to keep the scenario or send it to someone."
    ]
  },
  "scenarios": [
    {
      "name": "Conservative",
      "description": "Lower-end numbers — what if things land soft?",
      "values": {
        "spend": 72000,
        "paidCustomers": 480,
        "organicCustomers": 720,
        "ltv": 270
      }
    },
    {
      "name": "Typical",
      "description": "Defaults — the most common real-world setup.",
      "values": {
        "spend": 120000,
        "paidCustomers": 800,
        "organicCustomers": 1200,
        "ltv": 450
      }
    },
    {
      "name": "Ambitious",
      "description": "Higher-end numbers — what if things really pop?",
      "values": {
        "spend": 192000,
        "paidCustomers": 1280,
        "organicCustomers": 1920,
        "ltv": 720
      }
    }
  ],
  "limitations": [
    "Results are estimates before tax, fees, and inflation unless an input explicitly covers them.",
    "Rates are treated as fixed for the whole period — variable-rate products will drift from this projection.",
    "This is educational maths, not financial advice. Check anything contractual with the lender or your accountant."
  ],
  "faq": [
    {
      "q": "What's a healthy dependency ratio?",
      "a": "Under 1.5x means paid is carrying its weight close to the blended average, which is a resilient position. Above 2.5-3x means the business's efficient-looking blended number depends heavily on non-paid channels, and a slowdown in organic growth would expose a much worse true paid economics story."
    },
    {
      "q": "Why not just report blended CAC to investors?",
      "a": "Blended CAC is fine as a top-line health metric, but it can't tell you whether increasing paid budget will scale efficiently, because organic doesn't scale linearly with paid spend. Decomposing the two tells you the real marginal cost of the next customer you'd acquire by spending more."
    },
    {
      "q": "How do I know which customers are really organic vs paid-influenced?",
      "a": "Last-click attribution over-credits whichever channel touched the customer last, often organic search after a paid ad created initial awareness. A media mix model or incrementality test gives a more honest split than raw attribution reports."
    },
    {
      "q": "Does this replace an LTV:CAC calculation?",
      "a": "No, it feeds into one. Once you know paid CAC and blended CAC separately, compare each against LTV to see the real payback economics of paid spend versus the blended story you might be telling internally."
    }
  ],
  "related": [
    "https://www.revenuelab.fyi/toolbox/mer-roas-breakeven-calculator",
    "https://www.revenuelab.fyi/toolbox/marketing-budget-payback-allocator"
  ],
  "license": "CC-BY-4.0",
  "citation": "RevenueLab — Paid vs. Blended CAC Decomposition Calculator (https://www.revenuelab.fyi/toolbox/paid-vs-blended-cac-decomposition)"
}