When you’re analysing data, you shouldn’t necessarily treat it at face value, shares Dixon Jones.

Dixon says: “You need to think about what you are trying to measure.

Buyer behaviour is changing dramatically with the advent of AI. Users are going to be sending out their little AI bots to ask questions on your website instead of going there themselves.

If you're trying to buy a vacuum cleaner, you might ask, ‘What's the best vacuum cleaner for pet hair?’ Of course, Shark, Dyson, and Hoover all want to be in that list of recommendations, but it's the AI that's going to do the investigating.

Potentially, an AI is going to go and find these brands, have a look at those brands, compare the information about pet hair, and then come back and provide an answer to the user that says, ‘X brand is slightly better than Y brand for this particular type of pet hair.’ At that point, the user doesn't need to click on any of the websites, but they will still buy a Shark, a Dyson, an Electrolux, or whatever it may be. They'll have made their decision.

All of the metrics that we've been using for the last 20 years have measured the visitors that come to the website. That's been a key performance metric: has your visitor come from search, from pay-per-click, from direct, affiliate, etc.? That's been the mentality, but that doesn't really work in an AI-driven world.

Firstly, that’s because AI is doing the search for you. Secondly, the AI doesn't typically trigger a visit on most web analytics systems. Most web analytics systems are JavaScript-based: the web page loads, it triggers a JavaScript call, and that will record the visitor. However, LLMs are really lazy when it comes to crawling the site. They just want the text. If the text doesn't appear, they can't be bothered to call the JavaScript.

Often, it won't even come up as a click in your systems, so you're going to have to change the way you measure success.”