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AI ECONOMICS · ATTRIBUTION

AI Feature Attribution

How much of your ARR is actually driven by your AI features? Net of infra cost, with cohort gross margin.

Try a preset

$
AI-feature users
AI cohort ARPU uplift
$
AI cohort churn delta

Result

ARR ATTRIBUTABLE TO AI
$139,534.88
NET AFTER INFRA
$43,534.88
AI COHORT GROSS MARGIN
66.2%
NON-AI GROSS MARGIN
80.0%
RETENTION LIFT
2.0%

ARR Attribution Split

Gross AI-attributable ARR vs net (after infra cost).

AI-attributable ARR (gross)
$139,534.88
AI-attributable ARR (net)
$43,534.88
Annual AI infra cost
$96,000.00
Methodology → Formula, assumptions, sources, and known limits.

How to use it

  1. Enter your total ARR, the fraction of users who use the AI features, the ARPU uplift those users show, your monthly AI infra cost, the churn difference for AI users, and your total user count. The tool separates your user base into AI and non-AI cohorts and back-solves the base ARPU, so it isolates how much of your revenue the AI features actually earn rather than letting AI take credit for revenue it did not move.
  2. Read the AI and non-AI user counts, the base and AI-cohort ARPU, the ARR attributable to AI (the uplift times the AI users), the annual AI infra cost, the net AI-attributable ARR after that cost, the gross margin percentages for each cohort, and the effective churn reduction. The net AI-attributable ARR is the honest number, because it credits AI only for the incremental revenue and then subtracts what the AI infrastructure costs to deliver it.
  3. Use the net figure to answer whether the AI features pay for themselves. It is common for an AI feature to show an impressive ARPU uplift that mostly evaporates once the infra cost is netted out, and this tool surfaces that gap directly instead of letting a gross uplift number justify the spend on its own.
  4. Read the churn-reduction output as a second, often larger, source of value. If AI users churn meaningfully less, the retained revenue can exceed the direct ARPU uplift, which reframes the AI feature from a revenue add-on into a retention investment. Test a conservative uplift and a higher infra cost together to see how robust the attribution is before you defend the feature's budget.
  5. Re-run as AI adoption spreads through your base, as infra costs change, or as you measure real cohort churn rather than estimate it. Pair this with the agent cost per validated customer tool for the acquisition-side economics, so you see both what AI features cost to run and what they earn across the full customer lifecycle.
Questions people usually ask
What decision is AI Feature Attribution designed for?

AI Feature Attribution helps teams arr attributable to ai features, net of infra cost, with cohort gross margin and retention lift. before committing budget, pricing, or operating changes.

How can I get decision-grade output quality?

Use validated baseline numbers, run downside and upside scenarios, and align assumptions with your real cadence and constraints.

Is this legal, tax, or accounting advice?

No. Outputs are business planning estimates and should be reviewed with qualified professionals when required.

Is this free and private?

Yes. Tools run client-side in your browser with no signup.

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