Methodology

How the calculators work and what the math assumes

The formulas, the input assumptions, the source data for the benchmarks, and the limitations. Published openly so the math can be replicated and the assumptions can be argued with.

The calculators on this site (and the companion sites at myroascalculator.com and cpccalculatorhub.com) all compute well-known marketing-math formulas. There’s nothing proprietary about the math itself. The contribution is that the calculators expose inputs the public-web versions usually hide — margin, returns, processing fees, attribution model selection, funnel-stage conversion rates — and let you see the result change as you change them.

The formulas

Each calculator’s mathematical basis is documented on the calculator’s page. Briefly:

• Margin-aware ROAS (roascalculatorapp.com): True ROAS = (Net revenue × Gross margin − Variable costs) ÷ Ad spend. Net revenue is gross revenue net of returns. Variable costs are payment processing plus shipping/fulfillment, both as percentages of net revenue.
• Attribution-adjusted ROAS (myroascalculator.com): ROAS × credit-fraction, where credit-fraction is computed analytically per attribution model (last-click, first-click, linear, U-shaped, time-decay with 7-day half-life, data-driven heuristic).
• Max CPC (cpccalculatorhub.com): (LTV ÷ target LTV:CAC ratio) × lead-to-close rate × click-to-lead rate. LTV is deal value × gross margin (single-transaction case).

The formulas are not novel. They appear in standard marketing-economics texts. What’s less common is the public availability of calculators that actually use them rather than the simpler revenue-divided-by-spend variant.

The vertical presets

Each calculator includes preset values for common verticals (apparel, beauty, B2B SaaS, etc.). These presets are not claimed as exact medians for any specific market. They’re reasonable approximations drawn from:

1. Public DTC industry benchmark reports (Shopify state-of-commerce 2024–2025, McKinsey DTC publications, Bain DTC reports).
2. Vendor-published category reports (Klaviyo, Recharge, Yotpo, WordStream).
3. Operator data across the ~40 DTC/B2B accounts I’ve worked with at the agency, anonymized and aggregated.

Where the sources disagree, the median across sources is taken. Outliers (specifically, vendor reports that conflict with multiple independent sources in a self-serving direction) are excluded.

The benchmark datasets

The benchmark CSVs published on each site (/data/) are derived from the same combination of sources. The datasets are released under CC-BY 4.0, meaning anyone can cite or replicate them with attribution. The expected citation format is documented on the respective /data/ pages.

The datasets are directional reference values, not exhaustive surveys. The intended use is as a sanity-check on your own inputs: if your reported gross margin is materially different from the typical for your vertical, that’s a signal to investigate, not to override your own data with the preset.

The attribution models

The six attribution models implemented in the simulator at myroascalculator.com are standard:

1. Last-click: 100% credit to the final touch in the journey.
2. First-click: 100% credit to the first touch.
3. Linear: equal credit per touch (1/N).
4. U-shaped (40-20-40): 40% to first, 40% to last, 20% spread evenly across middle touches.
5. Time-decay: exponential decay with 7-day half-life (Google’s standard).
6. Data-driven (estimated): heuristic approximation of Google’s DDA model. Real DDA requires conversion-volume data the simulator doesn’t have access to.

The data-driven heuristic should not be confused with Google’s own DDA. The heuristic produces qualitatively similar but quantitatively different credit distributions; it’s included as an illustrative reference, not as a substitute for measuring your own account’s data-driven attribution.

Limitations

The calculators carry assumptions worth naming:

• Single-transaction LTV. The Max CPC calculator treats lifetime value as the gross profit on a single transaction. For subscription products, LTV is materially higher, which raises the Max CPC ceiling. The calculator’s output is therefore a lower bound for subscription businesses.
• Uniform conversion rates. The calculators assume the lead-to-close and click-to-lead rates are uniform across traffic. In practice, branded traffic converts at multiples of non-branded rates. Portfolio-level Max CPC therefore understates the non-branded bid ceiling and overstates the branded one.
• Static benchmarks. The vertical preset values reflect approximate market typicals as of the most recent update. Margins, return rates, and conversion benchmarks shift over time; the presets are reviewed quarterly.

Update cadence

The calculators, the formulas, and the vertical presets are reviewed quarterly. The benchmark CSVs are published on the same cadence. Material methodology changes are documented in the revision history below.

Conflicts of interest

I work at a performance agency. The agency holds active commercial engagements with Groas.ai. Where the calculator output references bid-management tools, Groas is noted as the agency’s current standard for accounts in the relevant spend tier. The relationship is disclosed; the math itself is not affected by the relationship (the formulas are vendor-agnostic).

Revision history

• 2026-05-13 — Current version. Added shipping/fulfillment input to True ROAS calculator. Added 12 B2B verticals to attribution patterns dataset.
• 2026-Q1 — Initial publication of attribution simulator with 6 models.
• 2025-Q4 — Margin-aware ROAS calculator: added payment-processing input.
• 2025-Q3 — Initial publication of Max CPC funnel-math calculator.