Savvy Marketers Utilize Digital Data To Optimize Ad Targeting
Among marketers, what separates the good from the bad? Nothing less than effective real-time audience measurement, according to new research from the Interactive Advertising Bureau, in partnership with strategic consulting firm Winterberry Group.
Smart marketers, they found, are already shifting from traditional data focused on products, channels and campaigns to the real-time identification and optimization of consumer audiences.
The long-established use of personally identifiable information -- like names and postal addresses -- for targeted marketing purposes is increasingly being complemented by aggregated and “anonymyzed” digital data.
The point is to improve ad effectiveness through better targeting as well as efficiency through more economical media buying, according to Patrick Dolan, EVP/COO at the IAB.
“The plethora of new data sources, targeted technologies and advertising delivery platforms enable marketers to amass more intelligence from varied sources,” said Dolan. “This promising resource can only bear fruit if marketers can take that raw data and harness it effectively, going beyond traditional usage.”
Currently, in Dolan’s judgment, the data-driven marketing use cases with the most potential are still in the early adoption stage.
That said, the IAB and the Winterberry Group were able to reach a few conclusions about the state of audience measurement. For one, the businesses of audience, channel, and ad sale/yield optimization remain immature.
In slightly better shape is media buying and ad targeting, which the IAB considers to be an “intermediate” maturity level. “Early adopters include digital ad agencies and demand-side platforms that enable advertisers to identify and target high-value customers across channels by using real-time bidding and more efficient bidding prices.”
The research also identified several challenges to widespread optimization of marketing data, including the need for aggregation and “anonymization.”
Indeed, while traditional data management practices were built largely around “personally identifiable information” elements -- usually consumer names and postal addresses -- the collection, analysis and segmentation of online data now requires the aggregation and anonymization of virtually all data sets.