AdQuick’s Analytics Series

This week we're talking Causal Lift- how are you able to accurately tell if your lift in sales, app downloads, etc. came from your OOH campaign? Let's find out...

AdQuick’s Analytics Series

Week 2: Causal Lift Analysis

Welcome to week 2 of AdQuick’s Analytics Series!

This week we bring you a plethora of information about Causal Lift. Plethora… who uses that word?

We have a lot of information for you... What is causal lift, how does it work and what information do you need to run a causal lift analysis on your OOH campaign.
Let’s start with the basics…

What is Causal Lift Analysis?

Also known as causal impact analysis, this is a way to estimate the causal effect of your out-of-home marketing campaign on a desired outcome- like sales, app installs, web conversions, or in-store visits.

Photo: The Marketing Millenials

While enabling our sales team is great for us all, wouldn’t it also be nice to be able to answer questions like, "how many additional daily clicks or conversions were generated by an OOH advertising campaign?"

I saw that 👀

How It Works

Though it may not seem like it at first, it’s actually pretty simple. Stick with me.

We use a Bayesian structural time-series model as a method that looks at a historical sales pattern and builds a prediction of its future course. This allows us to compare what actually happened following our intervention.

Think “controlled group” vs “exposed group” in a controlled experiment. The difference represents the impact of your out-of-home media investment.

Let’s dig a bit deeper- where did the prediction come from?

Well, you want to use as many data sets as possible that are related to your desired outcome, but could not be affected by your campaign.

Some examples are an online search of your industry, competitors products, or sales data in multiple other markets.

This data is used to train the model that generates our prediction.

Then the estimates are run multiple times, which is what allows us to create a distribution of the causal effects. In turn, this allows us to quantify a confidence interval for our final estimate.

Because we can be sure your campaign (and not some unidentified variable) caused the lift, this allows us to calculate the ROI of your campaign much more accurately.

TL;DR- it’s your turn to hit that gong 🔔

Ok, that was pretty simple, right? If you have any questions, you can schedule a call and we’d be happy to go over Causal Lift in more detail.

Thanks for sticking with me, I know this was a plethora of information.

It means a lot 😉