Study finds clean data platform boosts performance of Meta’s machine learning
A year ago, PureCars acquired the AutoMiner platform with the goal of giving dealers clean customer data to improve their marketing efforts.
This week, the company released the results of a study in collaboration with Meta that tested the performance of dealership ads using AutoMiner’s data and Meta’s Advantage+ Audiences.
The verdict, PureCars said, was positive.
“We’re thrilled with the impressive results of our study with Meta,” PureCars CEO Aaron Sheeks said. “The combination of the AutoMiner’s clean customer data paired with Advantage+ Audiences has produced exactly the type of results our customers are searching for when leveraging first-party data in advertising.”
The study, conducted in November, tested two unique campaign types.
In the first, which looked at vehicle acquisition ads, the study paired AutoMiner custom audiences with Meta’s Advantage+ Audience’s machine learning to determine if it would be more effective than a custom audience list alone.
Meta’s ML attempted to prioritize the custom audience list first, but also used it to extend the audience to others that might take the desired action.
PureCars said that resulted in an 83% reduction in cost per lead, five times leads and 20 times more reach.
The second study focused on ads promoting new car sales, directing automotive inventory ads to the dealer website and On-Facebook lead gen formats using an equity list and Advantage+ Audiences to see which strategy performed better.
The study found that when budget permitted, the ads more than doubled the performance running the strategies concurrently. Meta’s ML prioritized the equity list and expanded the target to find similar users at a more efficient rate.
The result, PureCars said, was seven times more vehicle display page views, four times more leads and six times more reach.
The study, Sheeks said, represents “is just one example of how dealers can instantly improve their advertising performance simply by using our clean customer data.”