The most repeated statistic in web performance comes from a blog post that does not contain it. A 2006 post by a former engineer describes internal tests finding that small delays produced substantial and costly drops in revenue. No percentage. No sample. No duration.
The specific figure everyone quotes first appears later that year as a bullet on a university lecture slide, with no method attached. It has been repeated for two decades since.
Real experiments do exist, they were run by search engines, and they found effects an order of magnitude smaller. This page traces each claim and gives the thresholds that are actually published.
The claim with no number in it
Worth reading the original, because the gap between it and its reputation is instructive.
What the 2006 post says, verbatim. That in A/B tests, they tried delaying the page in increments of 100 milliseconds and found that even very small delays would result in substantial and costly drops in revenue.
What it does not contain. A percentage. A sample size. A test duration. A revenue figure. Any quantification whatsoever.
Where the number appears. On a slide from a December 2006 university guest lecture by the same author, as a bullet reading roughly “+100ms → -1% sales”. No note of method, no sample, no citation of any internal study.
What that makes it. A recollection, converted into a precise statistic on a teaching slide, then cited as a finding for eighteen years.
Whether it is false. Nobody can say. The underlying tests may well have found exactly that. The company has never published them, so the claim is unverifiable rather than disproven.
How to use it. Stop citing it as a statistic. If you want to make the argument, cite the experiments below, which are weaker in magnitude and vastly stronger in evidence.
The retailer claim, which is real and observational
The second most cited figure is better sourced and still not what people think.
Where it comes from. A February 2012 presentation to a web performance meetup by three named members of a large retailer’s team.
What it reports, verbatim. That for every 1 second of improvement they experienced up to a 2% increase in conversions, and for every 100 ms of improvement they grew incremental revenue by up to 1%.
The two words people drop. Up to. Both figures are ceilings, not averages.
The method. Real user monitoring, meaning observation of natural traffic. Not a controlled experiment with injected delay.
The line that shows why that matters. The same presentation reports that converted shoppers received pages that loaded twice as fast as non-converted shoppers.
Why that sentence is not evidence of causation. People who convert differ from people who do not in many ways that also affect page speed: device, connection, whether they are logged in, whether assets were cached from an earlier visit, and which pages they reached. Faster pages for converters is exactly what you would see even if speed caused nothing.
Controlled tests exist. They are older, far more rigorous, and report much smaller effects, which is presumably why they lost the citation war.
The design. Server delay injected artificially, one group experiencing it, a second group serving as control. That is a genuine randomized comparison, not an observation of natural variation.
The published abstract, verbatim. That experiments demonstrate increasing web search latency 100 to 400 ms reduces the daily number of searches per user by 0.2% to 0.6%.
The table, in full. 50 ms of pre-header delay over four weeks: not significant. 100 ms: -0.20%. 200 ms post-header over six weeks: -0.29%. 400 ms: -0.59%. 200 ms post-ads over four weeks: -0.30%.
A companion result from a competing search engine. A two-second slowdown changed queries per user by -1.8% and revenue per user by -4.3%, presented at the same 2009 conference.
What these establish. That latency has a real, measurable, causal effect on behaviour, in the direction everyone assumes.
And the magnitude. 100 ms costs about a fifth of a percent of searches, not one percent of revenue. The famous figure is roughly five times the measured effect of the only comparable controlled test, on a different metric.
One limitation to state. The published study reports relative percentages without absolute user counts, and it measures searches rather than purchases. Search behaviour and checkout behaviour are not the same thing.
The consultancy study, and the word everyone misreads
The most cited recent study is real, large, and about one tenth of a second.
The sample.37 brands across retail, travel, luxury and lead generation, in Europe and the US, with 30 million user sessions over four weeks.
The headline, verbatim. That a mere 0.1s change in load time can influence every step of the user journey, ultimately increasing conversion rates, with conversions growing 8% for retail sites and 10% for travel on average.
The misreading. People quote this as one second. It is one tenth of one second, which makes the reported effect far more impressive, not less. Quoting it as a second understates it.
The method, verbatim. That fluctuations in speed all occurred naturally and were not artificially created on any of the sites, analysed with a logarithmic regression model, with speed measured by an automated auditing tool and joined to each brand’s own analytics.
So it is observational. Natural speed variation, not assigned. Sites are faster and slower at different moments for reasons that may also affect conversion.
Who commissioned it. The final page states it: commissioned by the search company. The methodology section notes that the search company, the consultancy and an agency collectively approached over 70 brands to participate.
Its own stated limitations. That the results are based on a sample of 37 websites over four weeks and may not fully reflect the internet as a whole, and that it presents only mobile web data.
The framing changed in 2023, and the change is more interesting than the usual summary of it.
What existed before. A published “page experience system” in the list of ranking systems, described as assessing criteria including how quickly pages load, mobile friendliness, absence of intrusive interstitials and secure serving. The same page noted that the 2018 speed update and the mobile-friendly system had been absorbed into it.
What happened. Between February and April 2023, the page experience system disappeared from the published list of ranking systems.
What Google said about it, verbatim. That it was not a separate ranking system, and that it did not combine all these signals into one single page experience signal.
What that does and does not mean. It does not mean speed stopped counting. It means the marketing wrapper around several individual signals was withdrawn, and Google clarified retrospectively that the wrapper never described a real mechanism.
The current position on weight, verbatim from 2020 and never withdrawn. That Google will prioritize pages with the best information overall, even if some aspects of page experience are subpar, that a good page experience does not override having great relevant content, but that in cases where there are multiple pages with similar content, page experience becomes much more important.
And the sentence that ends most agency arguments. That good stats in the Core Web Vitals report or in third-party tools do not guarantee good rankings.
The honest summary. Speed is a differentiator among comparable pages, not a substitute for being the right answer. Google has said exactly that, consistently, for six years.
Largest contentful paint. Good at 2.5 seconds or less. Poor above 4.0 seconds.
Interaction to next paint. Good at 200 milliseconds or less. Poor above 500 milliseconds. It replaced first input delay on 12 March 2024.
Cumulative layout shift. Good at 0.1 or less. Poor above 0.25.
How all three are assessed. At the 75th percentile of page loads, segmented across mobile and desktop. The same numeric targets apply to both; the segmentation means the percentile is computed separately, not that the bar differs.
Time to first byte, which is not a Core Web Vital. The guidance is that most sites should strive for 0.8 seconds or less, with poor above 1.8 seconds, and states explicitly that because it is not a Core Web Vital it is not absolutely necessary to meet the good threshold, which is why those thresholds are described as a rough guide.
Page weight and request count. No official threshold exists for either. Auditing tools offer configurable budgets, which are a developer convention rather than a published standard.
Three Core Web Vitals at the 75th percentile. Time to first byte is a rough guide, by its own documentation. Source : Google Search Central and web.dev (2026)
What to actually do about speed
Five things, in the order that returns the most for the least.
Fix the 75th percentile, not your laptop. Your machine on office broadband is not the measurement. Pull field data and look at the quarter of visits that are worst.
Find your largest contentful paint element and make it arrive first. On most B2B sites it is a hero image or a headline blocked by a webfont. That single element usually accounts for most of the gap.
Cut what blocks rendering. Third-party tags, chat widgets, consent scripts and analytics all load before your content on many sites. This is where marketing usually caused the problem and can usually fix it.
Treat layout shift as a correctness bug. It has no upside, it is usually caused by images without dimensions or late-injected banners, and it is the cheapest of the three to fix permanently.
Stop after the thresholds. Beyond the good thresholds, further speed work has no documented ranking benefit and the conversion evidence is observational. Spend the next hour on the offer instead.
And the sentence to bring to any argument about this. Good scores do not guarantee good rankings, and Google says so. Speed is a hygiene factor with a defined bar, not a growth strategy.
The 100ms claim has no source. The 2006 post it comes from contains no percentage at all; the number first appears on a lecture slide with no method.
The 2% per second claim is real but observational, from a 2012 meetup talk, and both its figures are stated as “up to”.
The only controlled experiments are from 2009: 100 ms of injected delay cost 0.20% of daily searches per user, 400 ms cost 0.59%.
A competing engine found -4.3% revenue per user for a two-second slowdown, in the same year.
The big consultancy study is about 0.1 seconds, not 1 second, on 37 brands and 30 million sessions, with speed varying naturally.
It was commissioned by the search company, disclosed on its final page, and states it may not fully reflect the internet as a whole.
The page experience system was removed from the ranking systems list in 2023, with Google stating it was never a separate ranking system.
Good Core Web Vitals do not guarantee rankings. That is Google’s own wording, in current documentation.
Fix the 75th percentile to the published thresholds, then stop. Book a diagnostic, or see how we approach B2B websites.
Frequently asked questions
Does 100ms of latency really cost 1% of sales?
There is no published study saying so. The claim traces to a 2006 blog post which says small delays caused substantial and costly revenue drops, with no percentage at all. The 1% first appears on a lecture slide with no method attached.
What about the retailer that reported 2% per second?
That one has names, a date and a venue: a 2012 meetup presentation. But the method was real user monitoring, so it is a correlation on natural traffic. Converted shoppers received faster pages; that is not the same as speed causing conversion.
Is there any real experiment?
Yes, and it is rarely cited. A 2009 study injected server delays against a control group and found that 100 to 400 milliseconds of added latency reduced daily searches per user by 0.2% to 0.6%.
What did the big consultancy study actually find?
That a 0.1 second change in load time influenced the journey, with conversions growing 8% for retail on average. Note it is one tenth of a second, not one second, and the speed variation occurred naturally rather than being tested.
Who paid for that study?
It says so on its last page: commissioned by the search company. Its own limitations section states the results are based on 37 websites over four weeks and may not fully reflect the internet as a whole.
Is speed still a Google ranking factor?
The signals still count, but the framing changed. Google removed the page experience system from its ranking systems list in 2023 and stated it was not a separate ranking system and did not combine signals into one.
Do good Core Web Vitals guarantee rankings?
No, and Google says so directly: good stats in its own Core Web Vitals report or third-party tools do not guarantee good rankings.
What are the actual thresholds?
Largest contentful paint within 2.5 seconds, interaction to next paint under 200 milliseconds, cumulative layout shift under 0.1, all at the 75th percentile of loads, computed separately for mobile and desktop.