Posts about Data & Measurement
Attribution is overrated
Attribution: why it matters and where marketers go wrong Attribution is overrated — or at least, that was our hypothesis going into episode 14 of The Hypothesis. In this episode, we dive into the complexities of attribution in marketing. What does it really mean? How can marketers assign value to their activities amidst flawed data and imperfect systems? Join us as we break down the concepts, discuss common pitfalls, and explore better approaches to measuring marketing effectiveness.
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Incrementality is the only important marketing metric
Incrementality is the only metric that matters — or at least, that was our hypothesis going into episode two of our video series, The Hypothesis. In this episode, we delve into the challenges and considerations of using incrementality as your primary method of understanding marketing effectiveness. Our chat includes seasonality, how long these tests take, and the level of sophistication required for different advertising programs. Learn about the importance of regular testing, questioning assumptions in marketing measurement, and how to start your own incrementality tests. Watch the full video below or listen to the episode here to get this conversation in its entirety. More of a skimmer? Keep reading for our top takeaways.
2 minute readDoes in-platform reporting show business results?
In-platform reporting doesn't show business results — or at least, that was our hypothesis going into episode two of our video series, The Hypothesis. In this episode, we get into why the data from in-platform analytics is easy to manipulate, how too many data points can hurt your reporting (or data puke as Eric so eloquently calls it), and where to use in-platform reporting. Watch the full video below or listen to the episode here to get this conversation in its entirety.
2 minute readMeasuring incrementality with geo experiments
TL;DR: Geo experiments can show us the real sales impact of ad campaigns but should be designed and interpreted carefully. These experiments assess how advertising influences sales without compromising privacy, dividing regions into test and control groups. It's crucial to consider regional variables, historical data shifts, and test duration when designing experiments — validating results through multiple tests is essential. This form of measurement can help us with over-reliance on cookie-based attribution.
7 minute read
All about hypothesis testing: an expert Q&A
We juggle a lot as growth marketers. Different tools, platforms, campaign types, strategies, creative options, and business objectives — just to name a few. But for all the testing we do to stay ahead of the curve, how do we test for the effectiveness of what we choose to focus on? Enter hypothesis testing.
6 minute readAn expert Q&A about data privacy
Sometimes (ok, lots of times) us marketers deal with a whole lot of conflicting information and rapid changes. This is absolutely the case when it comes to the potentially cookieless future. We don’t know everything, but we do know these two things to be true:
5 minute readPreparing for a cookieless future
TL;DR cookies are pieces of information about a user’s characteristics and behaviors that the user’s web browsers store as code every time they visit a website. Currently, Chrome alone has 63% of browsers globally so when they remove cookies, it’s a BIG deal. Cookies are the foundation of digital marketing targeting, tracking, and impactfulness — due to consumer privacy concerns, Chrome is getting rid of them. Likely by mid-2024 (but TBD, Google keeps pushing it back so keep an eye on the timeline here).
8 minute read
5 ways to measure brand campaigns
TL;DR brand awareness campaigns offer some great long-term benefits over always focusing on bottom of funnel tactics but can often be tough to measure. Here are 5 ways to measure the impact of your next brand campaign:
6 minute read
Beyond attribution: understanding the incremental impact of your marketing
TL;DR The world of marketing measurement is complex and traditional attribution methods often fail to share the whole story. We believe that incrementality is a more reliable determiner of revenue outcomes than many attribution models. Incrementality is all about measuring the real impact of our ads – the stuff that wouldn't have happened without them. We measure incrementality in two ways: experiments and testing media mix modeling To get started, we suggest running small experiments and begin building an understanding of how different media channels contribute to top-line revenue.
8 minute read