Marketing Attribution

More marketing teams are now thinking about incrementality than ever before – which we’re obviously very excited about. They’re realizing that they need to move beyond multi-touch attribution and that there are very meaningful questions that platform-reported dashboards and last-click attribution just aren’t built to answer. Again, this is great […]

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

Imagine a brand runs a geo lift test. They shut off ads in three regions for a few weeks, compare the results to three control regions, and observe a lift in sales in the treated geos. The model spits out a stat sig result, and the team gets excited. The