Multi-touch attribution vs. lift analysis.
What do they do?
How are they different?
And in what situations and for what goals should you use one or the other?
Let’s explore.
Multi-touch attribution and lift analysis are two different approaches for measuring marketing. Together, they help cover each other’s blind spots.
Multi-touch attribution (abbreviated MTA) is a measurement method that tracks digital touchpoints across owned media and ad platforms and estimates their influence on revenue.
For example, in HockeyStack, we use MTA to track Linkedin ad impressions, website activity, clicks from ad platforms, engagement with live chat, outbound sales sequences, gifts received, and much more. Then we organize those touchpoints from first touch to closed/won in a chronological timeline called a “journey.”
From there you can use different attribution models to estimate the value of each touchpoint, analyze customer journeys independently, feed sales with buyer intelligence, and a whole lot more we’ll get to in just a second.
Lift analysis, on the other hand, estimates the incremental lift (results that would not have happened without a tactic) a campaign or marketing activity had on a variable like demos, sales, or subscriptions by comparing people who experienced the activity (treatment) vs. those who didn’t (control).
For example, want to know how much more likely subscribers are to book a demo than non-subscribers? Lift analysis compares people who booked a demo but didn’t subscribe (control) against people who booked a demo and did subscribe (treatment). The increase or decrease above or below the control group represents the incremental lift- either positive or negative.
Both multi-touch attribution and lift analysis have their benefits and limitations. But let’s start with attribution.
Multi-touch attribution is outstanding at a few surgical tasks, namely owned media, paid media, and segmentation.
First, MTA is great for zooming in on owned media like content and creative- in real-time.