“Data-Driven Thinking” is written by members of the media community and contains fresh ideas on the digital revolution in media.
Today’s column is written by Ryan Green, vice president of marketing and innovation at Coegi.
In a cookieless world, attribution is old news.
Let’s be honest: Multi-touch attribution (MTA) has always been challenging to pull off. Even before Facebook and Apple walled off their data, there wasn’t enough impression-level data to build an accurate model. In fact, MTA models often yield eerily similar outputs to last-touch attribution, suggesting that budgets should shift toward retargeting and branded search tactics. That isn’t always the right recommendation.
To future-proof, it’s time to focus on incrementality: the measure of supplemental business results that a campaign drives in aggregate.
An incremental measurement model
The model I propose incorporates a custom measurement framework that uses multiple, weighted data sources to define your media’s impact on brand goals. The core KPIs fed into the model should be a combination of media data, business data and advanced measurement studies. These data points should then be weighted based on their significance and plugged into a customized formula.
Here is an example of what this formula can look like:
Lift in Unaided Brand Awareness (45%) + Location Visits (20%) + Clicks (10%) + Sales (25%) = Brand Health Score
It is much more accurate to use this incrementality approach than to analyze stand-alone moments in time. Just think: One challenge with attribution is it only gives credit for current campaigns. But many brands are still reaping the rewards of work they did years ago. For instance, the “Got Milk” campaign from the 1980s still resonates today. Attribution doesn’t account for this historical value, but incrementality can.
The goal of an incrementality model should be to analyze where you’ve moved quarter-over-quarter or year-over-year compared to baseline.
Advertisers often don’t know what channels, tactics, audiences or messages are driving success over time. It’s the classic “I know half of my advertising is working, I just don’t know which half” argument. This model helps unlock the answer to this question.
If you run brand lift surveys, for example, and ask the right qualitative questions of your audience, you can start to understand where you are making initial impact toward awareness and favorability.
Across channels that support stronger impression-level data, you can have even more accurate modeling of conversion activities. But without an integrated view of the campaign as a whole, it is too easy to get lost in the weeds of vanity media metrics, which provide more noise than signal.
Marketing complexity calls for incrementality
No single KPI can adequately identify incremental growth. The more marketing strategies and channels there are at play, the harder it becomes to attribute a conversion or a bump in brand lift.
Instead, let’s start looking at incrementality in the media mix, leveraging custom scoring models to evaluate leading indicators of success that are predictive and driving smart optimizations.
Follow Coegi (@CoegiPartners) and AdExchanger (@adexchanger) on Twitter.
