Third-Party Data Is A Bad Habit We Need To Kick

The Sell Sider” is a column written for the sell side of the digital media community.

Today’s column is written by Kendell Timmers, vice president of advertising data at The New York Times.

I recently looked myself up on BlueKai’s registry tool to see what audiences I belong to within their third-party data sets. While some of the results were correct (Hello, two kids and a mortgage!), many were contradictory or wrong.

In addition to being a young man, I’m apparently both a homeowner and a renter, I belong to four separate household income brackets (including the lowest and the highest), and I am both the female and male head of household.

I’m not picking on BlueKai in particular ­– it should be applauded for making it comparatively easy for me to see how I’m classified. The point is that any advertiser looking to zero in on young, unencumbered men is also going to mistakenly find me.

The accuracy of third-party data is overrated, and we have passed the point of diminishing returns on further modeling. Third-party segments can, over time, approximate an audience that might be more interested in an advertisement, but it is not a truth set, and there are limits to how targeted marketers can get.

Although I’ve seen many talented data scientists attempt to use third-party audience figures to create sophisticated targeting models, I have not witnessed much success. What I’ve seen has been based more on cookie or ID activity – such as the number or recency of audiences – rather than the supposed characteristics of the person on the other side of the screen.

The details are only going to get murkier and privacy questions will become thornier as consumers shift to mobile, despite the promise of online-offline matching services and universal IDs. A universal ID, after all, is just another way to connect two separate sets of dubious audience characteristics, through a link that itself is sometimes wrong. Not only is the data only mildly accurate, but the advertising industry’s continued use of it could motivate a very valid backlash from consumers concerned about their privacy.

Publishers need to find new, nonexploitative ways to connect advertisers to potential customers. In the pursuit of “relevant” ads, we might jeopardize the ability of advertising to meaningfully fund a business.

Enter context. Back before data management platforms and cookies came to dominate the landscape, simple context indicators were the primary form of targeting. Buying advertising based on a domain whitelist is a form of contextual targeting, as is targeting by sections of a newspaper or website. As the myth of behavioral audiences grew (“Find the right people, wherever they are online”), innovation in context declined and, for the most part, it remains a fairly blunt tool today.

The next wave of targeting innovation has already started, with machine learning allowing publishers to create extremely granular and data-driven classifications of their content, organized by custom topic clusters, virality and even emotional tone. These targeting methods are blind to the reader’s characteristics and focus entirely on the connection between the advertisement and the surrounding article.

Rather than an accumulation of unreliably interpreted signals, context is based on what the person is looking at and thinking about right now, which, for many advertisers, is far more relevant. A focus on minute classification of content could be the key to publisher survival in a post-GDPR (and CCPA, and whatever other acts are coming down the pike) landscape.

It’s a win-win-win: Publishers regain control of the segmentation and classification of their impressions and cut out costly middlemen peddling questionable data. Consumers get a privacy-friendly environment where they don’t need to second-guess what they did to prompt a certain ad. And advertisers have safe targeting methods that can often beat conventional behavioral targeting.

Kicking a habit has never been so appealing.

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  1. Yariv Drori

    A myth… it’s about time someone called it for what it is. Excellent article.

  2. Phil Ripperger

    Point well made Kendall. The industry’s focus on the potential efficiencies delivered through data and audience-based targeting has diluted the value of context.

    That said, all 3rd party data are not created equal. I have seen many analyses validating the quality of 3rd party segments in terms of delivering on KPIs (e.g. sales lift, registrations, engagement, etc.). In some cases they even outperform publishers’ audiences.

    And when you factor in the need for reach with many brands and campaigns, it can be challenging to achieve the required scale based on available and relevant contextual inventory.

    I would suggest that we resist the urge to dismiss a category of data and instead give careful consideration of the data source (and modeling methodology, if appropriate) coupled with campaign objectives.

  3. While I agree on the benefits of contextual advertising I think it’s important to note here that many of the top tier 3rd party data providers have accurate offline data. The issue is how that offline data is synced with online cookies and mobile IDs. This is were most of the inaccuracies we see arise but no one is really questioning the accuracy of the onboarders who help merry the offline data with online targetable identifiers.

  4. The industry has just evolved beyond what the Browser Cookie can accomplish. In fact it’s doing exactly what it was intended to do (in 1995)…it infers who the user is based on their browsing behavior. The issue is that the internet has exploded, use of the internet has exploded, devices per individual has exploded and now the Cookie world is littered with duplication and the issue is, which cookie is the right one? I think there are fewer scenarios now where “in-market” is even relevant.

    There are solutions out there though that can do cookie-less onboarding of offline data, you just have to search them out.

  5. Mike Skladony

    Agreed and we all know who the dominant onboarder is that completely gets away with a questionable solution. Also important to note the difference between the offline to online 3rd party data and the cookie inferred 3rd party data.

  6. Might things be different in B2B? Are you guys calling publisher data “2nd party” or is it sometimes 2nd party and sometimes 3rd party? In B2B tech, our permissioned data drives tremendous ROI when advertising is used in concert with other techniques.

  7. Nick: From the B2B perspective, I think you certainly got a lot right in your piece. The world you describe is already here in B2B tech; oddly enough a lot of folks don’t understand that.

  8. The reason you fall into different audiences that are contradictory are because every 3rd party data provider has a different methodology for what counts as a man/woman/homeowner/whatever. It all depends. Interesting article nonetheless.