Are website audit revenue estimates real?

"That 4,000 a month figure is made up." Fair thing to say, and about most audit tools it is correct. An estimate is either arithmetic you can reproduce on paper or it is decoration. So here is the Revslip version worked end to end, including the one input we are guessing at.

Are website audit revenue estimates accurate?

Only as accurate as their weakest input. Revslip multiplies four numbers: your traffic, the gap between your conversion rate and a target, your average order value, and a mobile weighting. Three of those come from your own analytics. The fourth, the target, is assumed, and it swings the answer further than the other three combined.

traffic × CVR gap × AOV × mobile weight = monthly leak

• Traffic. Measured. Pulled from GA4 or Search Console once connected.
• Average order value. Measured, if Shopify or your analytics is connected. Assumed if not.
• Mobile weight. Measured. The share of your traffic on phones, applied when the problem only exists there.
• The target rate. Assumed. This is the soft one, and the rest of this post is about it.

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Where does the number in most audit tools come from?

Usually one statistic. Most speed-to-revenue calculators apply a 7% conversion loss for every second of load time. That figure traces back to a single Aberdeen Group report from November 2008. Almost nobody who repeats it has opened it. We did, and what is inside does not support the way the figure now gets used.

Appendix A says the method was an online survey of more than 160 enterprises, supplemented by interviews. The respondents were IT developers and architects (23%), senior management (19%), IT managers (19%). Nobody randomised anything. Nobody counted a purchase. People were asked what they believed a delay did, and the average of their answers became Figure 1.

Two more things never survive the retelling. The report is about business-critical applications inside enterprises, not shops. And it puts the point where performance "begins to suffer" at 5.1 seconds, so the 7% describes a slide from an already slow page, not the first second past the two-second mark today's calculators punish you for. Its sponsors, it adds, were solicited after the fact.

Worth the reminder before you trust ours: Revslip shows its arithmetic per finding.

Which of the four inputs is Revslip guessing at?

The target conversion rate, every time. Traffic, order value and mobile share are read off your own data. The target is a judgement about what your page could reasonably do, and there is no neutral source for it, because published benchmarks hold several very different numbers depending on which population you pick.

We went through that spread in is my conversion rate bad. Take one dataset. Contentsquare's 2026 benchmark, built on 99 billion sessions across more than 6,000 sites, reports new visitors converting at 1.7% and returning visitors at 2.9%. Same report, same year, same methodology. Desktop converts 74% higher than mobile, while mobile carries 69.9% of the traffic.

So "the industry average" is not a number. It is a range with a factor of nearly two inside it, and whoever picks a point in that range is deciding most of your estimate for you.

The input we are guessing at Revslip's free estimate uses a target we selected. Change that one assumption and the euro figure moves by multiples, which is why the number is offered as arithmetic you can rerun with your own target rather than as a forecast.

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How do you check the number by hand?

Open a spreadsheet and reproduce it in four lines. If a tool gives you a figure you cannot rebuild from your own analytics in under five minutes, the figure is doing marketing rather than measurement. The longer walkthrough lives in how to calculate conversion loss. Here is the short version with real arithmetic.

1. Traffic. 18,000 visits a month, from GA4.
2. Current rate. 1.1%, so 198 orders.
3. Target rate. 1.7%, the new-visitor figure above, so 306 orders. The gap is 108 orders.
4. Order value and device. 108 × 54 euros = 5,832. The problem is mobile only, and mobile is 70% of the traffic, so 4,082 euros a month.

Now rerun line three with 2.9%, the returning-visitor figure from the same table. The gap becomes 324 orders and the answer becomes 12,247 euros a month. One assumption, one source, one year, and the estimate triples. That sensitivity is the thing audit tools hide, and it is the reason we publish the formula instead of the conclusion.

Prefer the numbers to the spreadsheet? Run a free audit and every finding arrives priced.

Does the estimate survive the fix?

Often not, and this is where the two best sources on the subject disagree. Aberdeen's survey implies a clean relationship between a change and a result. Ronny Kohavi, then running experimentation at Microsoft, reported the opposite from randomised tests: about a third of ideas moved the target metric, a third did nothing measurable, and a third made things worse.

The disagreement is explained by method. Aberdeen asked people what they thought happened. Kohavi's team split traffic and counted, across 300 experiment treatments a week at Bing, and published the success rate in a KDD keynote. Self-report flatters. Experiments humble. Any estimate that quietly assumes the fix lands at full value is taking the flattering side.

Which is why the free figure and the paid figure are different animals. The free one is an estimate bounded by a traffic assumption. On a paid plan the snippet and the integrations follow each change through to what happened to sales, so the number is measured rather than projected. Turning the estimate into a measurement is its own job, and we wrote the method up in tracking whether a fix worked.

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When is the euro figure the wrong thing to look at?

When you are under a few hundred sales a month, a 0.6 point conversion gap is well inside normal week-to-week noise, which is the same reason you cannot A/B test at that volume. Pricing it implies a precision nobody has. Read it as a ranking of what to fix first, not as money you are owed.

Two limits worth naming plainly. Revslip reports revenue, not profit, because it does not know your margin, and a 4,000 euro leak on 20% margins is a different decision than on 70%. And it does not check your stock levels, your delivery promise against competitors, or your fulfilment, all of which can cost more than anything on the page. Separating what the audit measured from what it judged is a separate check, written up in do AI website audits hallucinate findings.

Our own evidence carries a bias too. Revslip has audited more than 134 businesses across roughly 200 conversion signals, and every one of them arrived by typing their own URL into a free audit tool. That is a sample of people who already suspected something was wrong, which is exactly the population where leaks are easiest to find.

Questions people ask about audit revenue figures

Three come up more than the rest, and all three are really the same question: who chose the numbers, and can I change them. The short answer is that Revslip chose one of the four, tells you which, and lets you swap it for your own.

Is the free estimate the same as what I get on a paid plan?

No. The free estimate prices each finding using the formula above with an assumed target. Paid plans connect GA4, Shopify or your CMS and track each change against real sales.

Why euros instead of a percentage?

Because a percentage does not survive a conversation with whoever controls the budget. A ranked list with money attached lets you argue for the fix worth most and skip the ones not worth the developer time.

Can I substitute my own target rate?

Yes, and you should. Your best-performing page is a better target than any published benchmark, because it already proves what your traffic and your offer can do together.