What UX Confusion Is Actually Costing You
A checkout page confusion score of 74 translates to roughly $900-1,400/month in missed conversions for a typical B2B SaaS. Here's how to run that calculation for your own product.
The hardest part of getting UX monitoring taken seriously isn't the technology. It's the prioritization conversation. Engineering time is expensive. A broken coupon field competes with feature work, technical debt, and whatever the CEO saw at a conference last week.
The only argument that wins that conversation reliably is money. Not "users are frustrated," but "$1,100 per month is sitting in this broken element."
Here's how to run that calculation.
The baseline conversion math
Start with three numbers: monthly visitors to the page, current conversion rate, and average contract value (or order value).
For a B2B SaaS with 2,000 monthly visitors to the pricing page, a 4% conversion rate, and $800 average first-month revenue, that's 80 conversions per month, $64,000 in monthly revenue from that page.
A confusion score of 74 on the pricing page, based on our dataset, correlates with roughly 18-25% suppressed conversion. Not caused by the confusion alone, but correlated strongly enough to use as a planning estimate.
At 18% suppression: you're converting at 4% instead of a potential 4.9%. That 0.9 percentage point gap on 2,000 visitors is 18 additional conversions per month. At $800 each, $14,400 per month.
At the lower end of the estimate (12% suppression), you're still looking at $9,600/month.
That's the range you bring to the prioritization meeting: "fixing this confusion cluster is worth $9,600 to $14,400 per month based on our current traffic and pricing."
Where the number comes from
The suppression estimate comes from the signal mix, not just the score.
A confusion score of 74 driven primarily by dead clicks predicts higher conversion suppression than a score of 74 driven primarily by scroll bounce. Dead clicks represent direct interaction failure: users are trying to do something and it isn't working. Scroll issues represent layout frustration. Both are bad, but the intent-to-action failure is more costly.
Element attribution tightens the estimate further. When 71% of the signal on a pricing page traces to a single element (a feature comparison column that looks like it should expand but doesn't), the fix is isolated. Fix that one element, and the suppression estimate applies almost entirely to that change.
When signal is distributed across 8 elements, fixing one element captures a fraction of the suppression. The estimate scales with attribution concentration.
The $800-1,200/month estimate from the bounce rate post
That post describes a specific case: a pricing page with 72% bounce rate, confusion score of 71, 84% of signal on dead clicks from a non-interactive column header.
The $800-1,200 range was calculated on lower traffic than the example above: around 500 monthly visitors at a higher conversion rate, with the suppression math working out to 12-18 conversions per month. At $65-90 average transaction value, that's $800-1,200.
Higher traffic, higher ACV products scale the number significantly. The point of the example is the method, not the exact figure.
The cost of not fixing it
Confusion compounds. A page with a confusion score of 74 that doesn't get fixed will likely see the score creep higher as users encounter the same issues and the baseline shifts. It also trains users to expect friction, which affects retention and referral behavior in ways that don't show up in the conversion calculation.
The direct revenue impact is the calculation to run. But the indirect cost is also real: a frustrated user who converts despite the friction tells fewer people, churns at higher rates in the first 90 days, and opens more support tickets about the issue they found confusing before they even signed up.
Using monitoring to track the fix
The value of running this calculation before fixing an element is that you can verify the result afterward. Pre-fix confusion score: 74. Post-fix score: 31. Conversion rate movement over the following 30 days maps against the estimate.
When the result matches the estimate, it builds the case for the next fix. When it doesn't, it refines the model.
This is what the number is for. Not to impress stakeholders with a big figure, but to have a falsifiable prediction that you can check against reality. That's how monitoring becomes part of the prioritization process rather than a dashboard someone looks at once a quarter.
Most UX problems have a dollar value. Measuring the confusion score is how you find out what it is.