Post-GDPR, Purch points to 70 percent consent rates and the sweet spot for contextual targeting
Per this publishing/performance marketing platform, here’s how consent for data-based targeting and context for non-data targeting may thrive.
Both user consent for data-based ad targeting and contextual advertising for non-data-based targeting may be gaining new life following the launch in May of the General Data Protection Regulation (GDPR).
Some adtech observers have contended that contextual advertising can take the place of data-based audience targeting, because it does not require consent and could cost less.
In a recently released report, for instance, two UK firms reported that a test they conducted showed virtually no difference between contextual ad targeting — based on content and site type — and data-based audience targeting based on user attributes.
It depends, Purch Chief Revenue Officer Mike Kisseberth told me recently.
His company offers a publishing and performance marketing platform. He said they had been expecting that no more than five to ten percent of European Union-based visitors to the company’s web site would grant permission for their personal data to be used for marketing purposes, as required by GDPR.
To date, the current user consent rate for EU-based visitors to the Purch site is about 70 percent.
The reason for this huge consent rate is not yet entirely clear, he said, but it does indicate that there is broad acceptance among Purch’s visitors for data-based targeting.
Buyer intent
At the same time, Kisseberth pointed out that there are times when contextual targeting beats data-based targeting. Contextual targeting, based on the surrounding content or site/app type where the ad is placed, does not require consent under GDPR because no personal data is employed.
If it’s a choice between contextual targeting and targeting with third-party data, Kisseberth said, “I lean toward contextual as higher value” — if the context indicates buyer intent, he added.
In other words, he said, an ad for a Dell XPS 13 laptop on a web page containing a review of that computer model is going to get a higher return than an ad on a random site delivered there because of third-party data that inferred a visitor to that random site would be interested in the Dell XPS 13.
An ad about that computer model on a page with news about that model or computers in general would also probably have good results, he noted, but there is “some crossover point” where contextual advertising might not be as good as third-party data that professes to target users who are in the market for a laptop purchase.
If the web page content were, say, reviews of the best chewing gum, then third-party data targeting may well stand a better chance of reaching users looking to buy that laptop.
But two important variables affect that assessment, Kisseberth pointed out.
Third-party data ‘not going away’
First, advertisers can’t be certain of the quality of third-party data, and are often dependent on the data providers’ assertion of the quality.
Second, he said, “contextual relevancy will derive a higher value for a product that is a considered purchase versus an impulse buy.” In other words, contextual targeting needs to take into account the effort the customer will undertake to make a purchasing decision.
Impulse buys or purchases made with low levels of consideration — such as purchases of detergent — might have better results using third-party data. In that case, the advertiser is wise to test and utilize data showing the best results.
The problem, of course, is that, if a campaign using a certain audience segment based on third-party data gets good results, the advertiser isn’t certain that the next audience segment with similar third-party attributes will as well.
The use of third-party data could involve a variety of additional factors that the advertiser can’t control, even if they are described as the same attributes. For instance, the third-party data provider may have different definitions for an attribute like “loves outdoor sports” than other providers, but the brand knows what that attribute means for its own customers and lookalikes of those customers.
Which, of course, is why lookalike audiences based on your known customers can be so effective, because the brand has a better control of the customers’ set of attributes when it tries to find others of the same kind.
For Purch, Kisseberth told me, “it’s clear that using [first- or third-party] data to target audiences is not going away,” even when consent is required, “but contextual relevancy [with intent-based content for considered purchases] trumps audience targeting.”
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