Michael Kaminsky walks through using Geolift to measure whether Meta ads actually drive sales, not just report inflated numbers. You need daily revenue by geography for 90 days minimum, upload it to the platform, and it finds matched city pairs to test against. The tool tells you upfront if your experiment has enough power to detect results, and warns when confidence intervals will be too wide to trust.
Chapters
Let's say you're an e-commerce brand. You're spending $50,000 a month on Meta prospecting campaigns. The platform says you're generating a 4.7x return on ad spend. But would we have gotten these sales anyway? Your organic traffic is strong. Your brand awareness is growing. So you decide to test it. First, you need historical data. Revenue by geography and by day. At least 90 days, ideally a full year. If you can pull a report from Shopify or Google Analytics 4 that shows daily revenue by state or DMA, you have what you need. Then you upload that data into an experimentation tool. In this case, Geolift by Recast. The tool charts your data by geography so you can do a quick sanity check. Are there any obvious anomalies, missing data? If it looks clean, you move on. Now you design the experiment. You tell the tool a few things: that you're planning to increase spend, that you're measuring revenue, roughly what you think the channel ROI might be— that's incremental ROI, very important— how long you want to run the test, and how much budget you're willing to put behind it. The tool then runs simulations across your geographies. It's looking for regions where historically when one goes up, the other tends to go up too, meaning that they're good candidates for comparison. It spits out the candidate test configurations ranked by statistical quality. You can click between rows to see how well the synthetic control matches each set of test markets. The tighter the historical fit, the closer those two lines track before the experiment starts, the more confidence you'll have in the results. The tool might tell you something like, "If you increase spend by $15,000 in Austin and Boston for 4 weeks, you have an 80% chance of detecting a statistically significant effect, assuming the channel has at least a 3x true incremental ROI." That last part matters. The tool is telling you upfront what this test can and cannot detect. If the true effect is smaller than expected, you might not be able to see it, or the uncertainty ranges just might be too wide to be useful.
