NEW PMG PAPER
Here it is. The highly anticipated second and final installment of my two-part series on Google’s Causal Impact R package. Please try to control your excitement. In part 1, we took a general look at how the package helps marketers measure incrementality and ran through a quick toy example. Here’s a quick refresher on the […]
Incrementality. It’s one of the toughest things to tackle in digital analytics. “How many incremental conversions did that branding campaign drive?” “How many additional clicks did we drive by implementing that new Adwords feature?” The most common solution is a simple lift analysis: calculate the difference between the average conversions before and after the intervention. […]
You know what really grinds my gears? Using correlation to imply causation. It’s 2015 people, you should know better by now. When you Google “correlation vs causation,” about 5.3 million results are returned in a quarter of a second. The first one? “Correlation does not imply causation,” courtesy of Wikipedia. The only positive thing to come […]
Tired of waiting around to get relevant data for your standard A/B tests? At PMG we recently created and tested a new way to get statistically actionable insights. This type of testing not only cuts the time out of ad testing, but also allows you to test at least 5 different elements at one time so you can know where to go next.