What is Site Speed Revenue Impact?
This estimates the revenue at risk from slow page loads, using the widely cited heuristic that conversion rate falls roughly 1% for every additional 100ms. It exists to translate a technical metric into the only language that reliably wins prioritisation arguments. Be honest about what the heuristic is: a directional figure drawn from large-scale studies at specific companies, not a constant that holds across every site. The relationship between speed and conversion is real and well evidenced; the exact coefficient for your site is not knowable without testing it.
Why it matters
- It gives engineering and product teams a revenue-based reason to prioritize performance work, not just a technical score.
- Site speed issues silently tax every session, unlike a single broken feature — the cumulative impact is often larger than expected.
- It's one of the few CRO levers that doesn't require any design or copy changes, just technical optimization.
The formula
Revenue at Risk = Monthly Revenue × (Current Page Load Time − Target Load Time) × 0.1
- Monthly Revenue
- — revenue this page or site earns in a month
- Current Page Load Time
- — what it loads in today, in seconds
- Target Load Time
- — what you are aiming for, in seconds
How to use this calculator
- 01Check current page load time in Google PageSpeed Insights or a similar tool.
- 02Set a realistic target load time — under 3 seconds is a common benchmark.
- 03Enter monthly revenue attributable to the page or site.
- 04The result estimates revenue currently at risk from the speed gap.
Worked example
A site currently loads in 4.5 seconds, targets 2.5 seconds, and generates $50,000/month in revenue.
- Current load time 4.5s against a 2.5s target — a 2 second gap
- At roughly 1% conversion loss per 100ms, 2,000ms implies about 20%
- Revenue at risk = $50,000 × 20% = $10,000 per month
- Directional only: the coefficient is an industry heuristic, not a measured constant for your site
Closing this 2-second gap is estimated to be worth roughly $10,000/month — a strong case for prioritizing performance work.
Industry benchmarks
Compiled August 2026
Fast site
Under 2.5s load time
Minimal speed-related revenue risk.
Average site
2.5s – 4s
Moderate risk, worth investigating.
Slow site
4s+
Often a significant, underestimated revenue drag.
Mobile on a real connection
Frequently 2–3× the lab figure
Test on throttled mobile, not office wifi.
Common mistakes
- Treating the 1%-per-100ms figure as an exact law rather than a directional industry-cited heuristic.
- Measuring load time on a fast connection/device that doesn't reflect your actual customer base.
- Fixing speed on desktop only while mobile — often the majority of traffic — remains slow.
- Assuming the relationship stays linear at every scale — going from 8 seconds to 6 matters far more than going from 1.2 to 1.0, and this model treats them as equivalent.
How to improve your site speed impact
Prioritize mobile load time specifically
Mobile is typically both the larger traffic share and the slower experience — fixing it first usually has the bigger revenue impact.
Re-test after each fix
Recalculate this estimate after each optimization to track cumulative revenue impact, not just technical score improvement.
Fix the money pages before the marketing site
Speed work on product and checkout pages converts directly into orders. The same engineering effort spent on a homepage that people pass through in two seconds returns very little.
Measure checkout drop-off →Frequently asked questions
Is the 1%-per-100ms figure exact?+
No — it's a widely cited industry heuristic from various large-scale studies (Amazon, Google, and others), useful directionally rather than as a precise law for every business.
Does this apply equally to all page types?+
Impact varies by page — checkout and high-intent pages are typically more speed-sensitive than top-of-funnel content pages.
Which load time measurement should I use?+
Prefer field data from real visitors over a lab score — Core Web Vitals in Search Console reflects actual devices and connections, while a synthetic test on a fast machine routinely reports half the time your customers experience.
How do I prove the effect for my own site?+
Compare conversion rate across your existing speed distribution — visitors already load your pages at a range of speeds depending on device and connection. Segmenting by that gives you a coefficient grounded in your own data rather than someone else's study.
Related calculators
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