By Dagan Price, AI Solutions Lead at IDHL
Believe it or not, nearly every SEO forecast I come across is built with someone else’s numbers using the same old formula (search volume x click-through rate x conversion rate x average order value). You have used it, I have used it and it is in nearly every SEO pitch deck, yet three of those four figures often have little to do with the business being forecast.
We’re estimating off everyone else’s numbers, that’s the issue, with search volume modelled, averaged and grouped with keyword variations. The click-through-rate (CTR) curve is from an industry benchmark and average order value (AOV) is often the only figure from the business itself and, funnily enough, the only one nobody ever argues with.
The challenge here is not forecasting itself, it is building the forecast from assumptions the business can recognise and defend.
Gap: Stop forecasting with borrowed averages
I built a different approach that you can start using right away, using only Google Search Console and GA4: Gap x Value x Odds, or GVO, because we all love another acronym. The Gap is the clicks you are not getting, the Value is what one click is worth to your business, and the Odds reflect whether you will get them and how much will land in year one. Multiply the three to produce a commercially grounded forecast.
Start with your own CTR curve by exporting three months of non-brand queries from Search Console, round each query’s average position to the nearest whole number, then group queries by position and calculate the average CTR for each group.
Choose a realistic target position, then subtract your curve’s CTR at the current position from its CTR at the target position. And then multiply that difference by average monthly impressions to calculate the Gap. This grounds your forecast in how searchers have responded to your own results, rather than a borrowed industry CTR curve.
Here’s what surprised me in my worked example… the industry curve assumed an 11% CTR for position three, but the site was actually achieving 6.1%. That means the industry benchmark inflated the click assumption by around 80% before any conversion or revenue figures were added.
Value: Put a price on the next place up
Next, the Value part of the equation. You’ll need to work out what each click is actually worth using the conversion rate for the traffic you are forecasting, rather than a sitewide average.
For eCommerce, multiply the conversion rate by average order value and for lead generation, multiply enquiry conversion rate by lead value. You don’t know the value? Agree a sensible estimate with sales or finance. Then multiply Gap by Value per click to estimate the gross opportunity before applying the Odds.
Interestingly, using theoretical eCommerce data across 200 non-brand commercial queries, moving from position eight to seven was worth an estimated £7,746 and moving from five to four was worth £29,047. The closer you get to the top, the more valuable each position becomes and the easier it is to prioritise the opportunities with the greatest commercial return.
Odds: Account for what you can realistically achieve
Increasingly, we’re seeing search results change faster than annual planning cycles can accommodate. Ahrefs found AI Overviews reduced clicks to top-ranking pages by 58%, which shows why even a forecast built on your own data can be wrong. We do not control Google, and rankings do not land in one month, which is why the Odds part of this formula combines both your historic win rate and your year-one ramp.
In Search Console, find the non-brand queries that ranked between positions five and ten a year ago. The proportion now at or above your target position is your win rate. Then apply a ramp factor, because rankings build over the year rather than landing at once. Average the share of the full effect you would have in each quarter (5%, 40%, 75% and 100%) and you get a 55% ramp. In my example, a 45% win rate and that 55% ramp reduced a £266,016 gross eCommerce opportunity to £65,839. It is a big difference, but a number you can defend.
When I presented the model at BrightonSEO, one of the most asked questions was how much historical data you need for it to work. Ideally, 12 months, although any Search Console data can help build your own CTR curve.
Rebuild the curve quarterly
Published CTR curves are often out of date as soon as they appear. Your own curve reflects what customers are seeing now, so rebuild it quarterly and let the forecast move with the search landscape.
Together, Gap x Value x Odds turns an SEO forecast from a promise into a range the business can interrogate and defend, built on its own data, transparent assumptions and realistic odds.
