One of the biggest problems we're solving at Clean Commit this year: how to A/B test a landing page without wrecking your ad metrics. Here's the thing most people miss. When Meta learns your landing page, it optimises for how customers react to it and drives traffic accordingly. Then you run a test. You change a bunch of variables and the algorithm gets confused. Your cost per impression and cost per click climb. Even if the page itself performs fine, the ad metrics behind it usually tank. There's no real way around that on a traditional testing platform. The only workaround we've found: spin up one landing page per variant and marry each to its own campaign. Control page, one campaign. Variant page, another campaign. Want more variants? More campaigns. Then you pull the numbers from each and work out the result by hand. It's not apples to apples. Different visitor counts, different impressions. But it's the closest thing to a fair test, because the algorithm gets to optimise for each page on its own. The bonus is you see how the page performs and how the campaign performs. That matters, because CRO doesn't happen in a vacuum. We rarely work with a growing brand that gets less than 50-60% of its traffic from paid. Paid is the way forward whether we like it or not. So if you're only testing page performance and ignoring what happens to your campaigns, you're looking at half the equation. It's a big problem. We're building a process around it now, and at some point we'll probably turn it into a product. How are you handling this? Curious how others are combining their testing tools to solve it.