Catalyst: the tool that proves your website optimisations are working 

You’ve made the optimisations. You’ve ticked the boxes. Now comes the question every SEO team dreads from a client: “But how do we know it worked?” 

To answer that question, we built Catalyst – a proprietary tool that takes the guesswork out of measuring SEO impact and gives you something you we can show to a client with confidence. 

The problem with “traditional” SEO measurement

Most SEO reporting looks quite similar. Traffic went up. Rankings improved. Click-through rate ticked higher etc. 

But organic search can be noisy and traffic fluctuates naturally throughout the year. A spike in clicks in March might have nothing to do with the work you did in February – it could simply be seasonal demand doing what it always does. 

Without accounting for that context, you’re not reliably measuring impact. You could just be measuring movement. 

In many ways, GEO and AIO visibility is becoming closer to PR than traditional SEO, it’s about becoming a citable source, not just ranking in search. This thinking has also contributed to the development of our new Brand Citation Score tool, which helps brands understand how visible and recommendable they are across AI-powered search experiences heading into 2026.

What Catalyst actually does

Catalyst builds a model of what “normal” looks like for your site – and then indicates when SEO work pushes performance beyond that normal range. 

Here’s how it works: 

• It ingests historical data from Google Search Console. This gives the tool a baseline of how your site has performed over time – impressions, clicks, click-through rate, average position – across whatever timeframe gives us a meaningful sample.
• It layers in Google Trends data to map seasonality. This is the step most measurement approaches miss. Demand for almost every product and service shifts throughout the year. Catalyst maps that seasonality and bakes it into the model, so the expected performance range accounts for the time of year – not just a flat historical average. 
• It models a normal performance range. With both signals combined, Catalyst creates a dynamic band of expected performance. Think of it as an upper and lower boundary of what you’d expect to see, adjusted for seasonal demand, based on everything that came before. 

• We mark the point your optimisations went live. Once you set that intervention date – whether that’s when new content was published, a technical fix was deployed, or a structural change was made – Catalyst tracks what happens next against the modelled range. 

• It shows you whether your work made a difference. If performance moves outside the expected range following your changes, that’s not noise. That’s signal. Catalyst tells you, with clarity, whether your optimisations have had a measurable impact. 

Why this matters

The agency world has an attribution problem. It’s easy to show a client that traffic is up. It’s much harder to demonstrate that your work is the reason why. 

Catalyst changes that conversation. Instead of presenting a graph and hoping the client joins the dots, you can show them a model that separates expected performance from actual performance – and pinpoint exactly where your work moved the needle. 

It also protects you when the picture is more complicated. Seasonality, algorithm updates, and competitor activity all create noise in organic data. Catalyst helps you isolate the signal that matters. 

Catalyst in action: title tag optimisation for a major online retailer 

Here’s a real example of Catalyst at work.  

The work was straightforward on the surface – we refined a selection of title tags across a major online retailer’s category pages. Simpler titles, cleaner structure, broader keyword coverage to improve visibility across a wider range of search phrases. Around 430 URLs in scope. 

We fed Catalyst three inputs – two years of Google Trends data to model seasonal demand in this category, and Google Search Console performance data from the two months before and after the changes went live. Catalyst built the expected performance model, set the intervention point, and tracked what happened next. 

The chart below shows the output clearly. The blue line is what Catalyst predicted would happen with no changes made – the normal range, adjusted for seasonal demand. The orange line is what actually happened (the grey shadow in the expected range of performance).  

From early November onwards, the observed performance begins to pull away from the expected range. By the end of the measurement period, Catalyst attributed a 21% increase in clicks month-on-month directly to the title tag work.

A note on methodology worth sharing: most of the uplift appeared in the latter part of the month. That suggests the full impact hadn’t yet been captured in this initial read. The plan is to rerun the analysis with two full months of post-change data for a more conclusive result – which is exactly how Catalyst is designed to be used. Not as a one-time snapshot, but as an ongoing measurement framework.

There’s a broader point here too. Google doesn’t always move quickly. Ranking changes following optimisations can take weeks to fully filter through, so the longer you allow the model to run post-intervention, the more complete the picture becomes. More data means a tighter, more definitive read on impact – and it gives your changes the time they need to actually land before drawing conclusions.

It’s not just for SEO

While Catalyst was built with search performance in mind, the underlying approach works for any change that influences website traffic.

A PR campaign. A TV spot. A major content push. A rebrand. If you want to understand whether a specific intervention moved the dial on your organic performance or other measurable stats, Catalyst can model it.

That makes it a genuinely useful tool across multiple disciplines – not just for justifying SEO retainers, but for understanding the cumulative effect of everything your marketing is doing.