Create a list of relevant keywords, gather the monthly search volume estimates for those terms and multiply them by an “organic click through rate” that simulates the number of clicks a site in a particular ranking position would receive. This allows you to predict the potential organic traffic to a particular page.
To make your prediction even more accurate, you can refine your keyword list for relevance by evaluating the competition for each keyword. This will help you identify which keywords are worth targeting and how much effort should be put in to rank for each one.
Additionally, you can use data from your own website analytics to determine the average click through rate of organic search traffic to your site and adjust your prediction accordingly. This will give you a better understanding of how much organic traffic you can expect to receive from each ranking position.
Create an Organic Traffic Forecast
You can either create a prediction for that organic traffic based on a singular ranking position, or average the organic CTR for the Top 3 rankings vs positions 4-10. This gives you a useful SEO forecast comparison of being just “on the first page” vs being “at the top” of the rankings. You can refine your prediction further by considering the impact of seasonality and trends on search volume.
For example, if you’re targeting a keyword related to a holiday, the search volume for that keyword is likely to increase in the weeks leading up to the event. You can leverage this insight by adjusting your organic CTR accordingly.
Additionally, consider the impact of external factors such as changes in Google’s algorithm or increased competition from other websites targeting the same keyword. These types of variables can have a major impact on your organic traffic prediction and should be taken into account when creating your forecast.
Make Your Organic Traffic Prediction More Realistic
You can add a level of “reality” to your prediction, by taking a reasonable % of those keywords overall, because it is unlikely your site will rank for EVERY keyword you have in your list. You can also consider the impact of “long tail” keywords, which are longer and more specific versions of a keyword that users are more likely to search.
A good example of this is if you’re targeting a keyword like “shoes”, you may want to include variations such as “mens black dress shoes” or “running shoes for women” in your list. These long tail keywords are typically less competitive and can give you an edge in terms of getting organic traffic.
Jeremy’s 2026 Refresh: Forecast Capture, Not Just Clicks
When I wrote this in 2023, the search volume × CTR formula was clean math: rank in a position, multiply by its known click-through rate, and you had a defensible traffic estimate. In 2026 that model has a giant asterisk on it called AI Overviews. Google’s AI summaries now sit on top of a huge share of informational queries, and they quietly eat the clicks the old CTR curves promised you. Pew Research found that users clicked a traditional result on just 8% of searches with an AI summary versus 15% without one — and only 1% clicked a link inside the summary itself. So a #1 ranking on an AI-Overview query is no longer worth what your spreadsheet thinks it is.
My fix is to add an “AI compression” haircut to the forecast: segment your keyword list by whether the SERP currently triggers an AI Overview, then discount the CTR on those terms by roughly 40-50% before you multiply. Transactional, commercial, and “who/where/buy” intent queries usually keep their clicks; broad how-to and definitional terms are the ones bleeding out. That’s also why I now forecast for visibility and citation, not just blue-link position — a theme Stephan Bajayo and I unpacked in his Unscripted SEO episode on why there is no “rank” in an LLM. Predict the traffic you’ll actually capture after the AI layer takes its cut, and your forecast stays honest.