The War for Your Digital Identity
About ten years ago, I could (and did for public health marketing) buy lists of targetable ad segments like “Parents of Kids Ages 11-13 with a Primary Care Doctor.” These lists were created from advertisers who often ran media for no other reason than to get user data, and then used the universal Google Ad ID (GAID) or Apple’s ID for Advertisers (IDFA) to tie this info together. These mostly-cookie-based lists would then be repacked and sold with the labels above as best as the seller could create, very similar to how financial products are packaged and repackaged a million times over.
The obvious privacy implications here led to rules like GDPR and CCPA, which in turn led Google and Apple to deprecate GAID and IDFA, nearly instantly breaking the data chain that was largely responsible for personalized advertising’s industry growth. But with the lesson learned, the process of personalized marketing wasn’t destroyed, just shifted to the first party. Rather than letting some random company organize likely customers for you that you then license for a fee, organizations now must build up their own lists of customers using a wider array of first party data signals (your own ads, apps, mobile sites, forms, emails, point-of-sale systems, etc).
Fast forward a few years, and the big companies have not only done this well internally, they’re realizing this new data architecture is a veritable gold mine now that there’s no outside competition trying to model and sell “likely Disney fans” other than Disney themselves.
This quickly led to an explosion of new media networks. In the last week or two alone we’ve seen announcements of new media networks from Chase Bank, United Airlines, PayPal (including Venmo), Costco, and others. These media networks allow advertisers to target Chase retail banking customers or United in-app movie watchers directly, with more far data fidelity than trying to recreate those segments using the pre-canned audience definitions in Google or Facebook Ads.
The technology that allows this process is a data clean room, where your first-party media data is combined with another organizations through a double-blind process so you can see, for example, that 50% of your Star Wars Fans on Disney+ overlap with Chicken Bake Purchasers at Costco, but without seeing the exact user details on Costco’s end to get that information. CVS and Pinterest, Disney and Indeed, Snowflake and Snapchat, etc are all examples of these burgeoning types of partnerships
Fast forward a decade from now. Large companies across the board have monetized their user base, and consortiums like the EU Telco partnership start to become more common as they share basically the same underlying data. Banks create partnerships, as do airlines. Big box retail and mid-tier restaurant chains build point-of-sale-based systems. Rather than dealing with every individual company, cross-industry data connectivity systems pop up within tools like Snowflake or the Trade Desk to manage larger campaigns just like they did in the original era of programmatic growth.
In the prior era, it was Google and Apple who’s universal IDs led to the explosion in digital capability. The downside of using those universal tools was the instability and often inaccuracy of the cookie data that third parties were quite literally scraping together. In the new era where companies are creating their own first party data systems, the data fidelity should be far higher, with the downside being less overall user reach than the big tech platforms.
The clean room partnerships are exploding precisely to help expand the reach of targeting advertising. As they do, the basis for reach being on first party user data will naturally favor those firms with the highest amount of first party data, and will likely take the place of big tech as a user data lynchpin in the not-so-distant future.
But who will it be? Is there a better basis to help understand user behavior than credit card usage overall? What about telco subscriptions? Or maybe your retail purchases specifically?
The winner will simply be the data standard that can help other companies produce the best ROI, which will tell us something real about which habits matter most to predict our future actions. Only time will tell. As they say – watch this space.