Profit Drag is the hidden margin loss created when some customers consume more service, support, discounts, or churn risk than their revenue justifies.

Is this calculator exact?

No. It is a directional estimate for planning. Margin Levers turns the same assumptions into customer-level analysis once you upload real customer data.

Free calculator, no signup required

How much profit is your customer base quietly leaking?

Model hidden Profit Drag from margin gaps, service load, and churn pressure. Share the estimate with your team, then run the real customer-level analysis in `/try`.

Built for budget approval

Shows annual Profit Drag, recoverable range, Growth payback, and decisions needed to pay for the tool.

URL parameters make it safe for cold email without pretending you know private numbers.

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Crawlable example estimate

What the calculator estimates

Using the default assumptions of $5,000,000 ARR, 120 customers, 68% gross margin, 80% target margin, 18% below-target customers, $1,500 extra annual service cost per below-target customer, and 2.5% monthly churn, Margin Levers estimates annual Profit Drag of $172,250.

Recoverable range

$43,063-$103,350

Growth payback

24.5x annual plan cost

Decisions to pay back

1 customer decision

This example is directional. Real Margin Levers analysis replaces assumptions with named customer-level revenue, cost, and margin data.

Transparent assumptions

Every input is visible and adjustable. Change ARR, margin, or churn and see the impact instantly. Share the URL and the recipient sees exactly what you assumed.

Built to circulate

Download or email the estimate so your CFO or VP Finance can review the numbers before you commit to a full analysis.

Carries into your analysis

Your gross margin and cost assumptions transfer automatically when you run the real analysis — no re-entry required.

Cold email friendly by design

Prefill public estimates with URL params like `arr=5000000&customers=120`, then let the buyer adjust assumptions. The page creates curiosity without overclaiming private customer-level facts.