Marketing Attribution

Precision Preview is live in GeoLift by Recast, and it’s already changing how teams think about designing lift tests. Here’s the problem it solves: when teams design geographic-based incrementality tests today, they often size them using power analysis – a statistical exercise that answers the question “will I detect a […]

Is statistical significance the golden rule of marketing experiments? Is 5% the line where an experiment becomes (in)conclusive?  It’s an easy standard to reach for, which is why so many teams over-rely on it. But marketing experiments are noisy, expensive, and tied to real budget decisions that just can’t wait

We know this sounds obvious in theory, but it’s still relevant: randomized does not automatically mean fair. In practice, we see plenty of geo experiments that still rely on a simple 50/50 split and treat the result as if the design validated itself. That is a problem. When the number

A reader asked: “Why should I use a geo testing tool when I can just do the analysis in google sheets?” Eyeballing the data in a spreadsheet is a solid first step. Just know there are several ways spreadsheet analyses can bite you if you use them for more advanced

Many brands aren’t ready for MMM yet, and that’s okay. We routinely turn away companies because they’re not spending enough money, not spending across enough channels, or don’t have the analytics resources in-house to interpret and execute upon a complex model. If your mix is limited and annual paid media

“Storytelling with data” sounds great in theory – we all would love to have a single source of truth that always outputs one clear, actionable insight with no uncertainty. But in practice, most real-world data doesn’t work that way.  Especially in media mix modeling and incrementality work, the outputs are

If you’re treating p < 0.05 as the litmus test for your marketing experiments, it’s going to be really hard to build a solid measurement program. Many marketers still blindly follow this rule: But a p-value doesn’t tell you whether your campaign worked, how big the effect was, or whether

Every marketing measurement strategy comes with a cost.  Whether you’re running incrementality tests, MMMs, or lift studies — there’s always a tradeoff. Andrew Covato calls this the “measurement tax” — a concept he shared in our Recast Marketing Measurement Coffee Break. This tax comes in two forms: explicit and implicit.

Marketers love to talk about experiments. And experiments are great, but marketers who only focus on A/B tests and holdout studies are missing out on levers that can move the needle for their business. So here’s our premise: the sharpest marketing organizations need to think in bets instead. What is