“Data-Driven Thinking" is written by members of the media community and contains fresh ideas on the digital revolution in media.
Today’s column is written by Hugo Loriot, partner at 55.
There is no question that the death of third-party cookies is turning the industry on its head by disrupting activation and measurement tactics.
The old programmatic mantra of “serve the right ad to the right user at the right time” may soon become “serve the same ad over and over to a random user at any stage of the funnel.”
But that doesn’t mean it’s necessary to switch back to contextual targeting and last-click attribution. There are other cookieless use cases to consider as marketers are forced to take a new approach to first-party data.
That also does not mean all forms of audience planning and measurement are dead. Demographic, in-market and affinity data from the likes of Google, Facebook, Amazon and Pinterest will still be available, because they are first-party by design, and unlike audiences you buy from data providers, they are free of charge. Retargeting and look-alike strategies will also weather the storm, as long as they rely on cookieless signals and CRM onboarding.
View-through attribution and omnichannel measurement will not go away, but they will only come from walled garden-specific first-party data integration and clean rooms such as Google’s Ads Data Hub and the Amazon Marketing Cloud.
To be successful in this new landscape, marketers will have to focus much more on consented known identities than unknown users. Today, most first-party use cases rely on matching a first-party anonymous cookie, such as an ad exposure or product page view, to a third-party anonymous cookie, such as a DSP, for retargeting, audience extension or attribution. Tomorrow, the only possible first-party use cases will match a known identity, such as a hashed email address, with a walled garden’s hashed email address through CRM onboarding.
This means marketers must move away from piling up first-party unknown identities and start building fully-consented, CCPA- and GDPR-compliant repositories of known identities. They have to redefine their performance media KPIs for demand generation, and to achieve CRM critical mass, they must clarify the payoff for sharing personal data.
Generating insights from first-party data
Growing the known first-party data pool is meaningless without adding a strong layer of insights. That’s why brands need to match, enrich and score their known first-party data with a Cloud for Marketing approach, which connects their DSP or ad server to the Google Cloud Platform, Amazon Web Services (AWS) or Microsoft Azure.
For example, predictive lifetime value calculation is useful for value-based look-alike modeling, because brands want to reach audiences that are similar to their best customers, not random newsletter subscribers.
Propensity models help predict a customer’s likelihood to positively react to cheap direct marketing communication and only use paid media clients that cannot be reached otherwise.
A product recommendation engine can be used to dynamically insert the next best offer in a programmatic ad or a short-form video.
To achieve this, it is necessary to leverage cloud environments. Website data from Google Analytics 360 or the Adobe Experience Cloud can be matched with CRM data from Salesforce at the most granular level in the cloud, either in Google BigQuery or AWS Redshift. The addition of a strong machine learning toolkit provides the right scoring, and the resulting enriched CRM data set can be uploaded to specific walled gardens for activation or measurement.
The post-cookie landscape is not a step back to blind contextual targeting and last-click attribution. Quite the opposite, it will require more T-shaped profiles that are able to navigate CRM, programmatic media and the cloud.
That’s why the fast-evolving regulatory and privacy landscape is both an opportunity and a threat. Marketers who just stick to the plan and wait for the cookie’s last call before changing mindsets will most likely face lower ROI with no clear vision on where to invest. Those who quickly realize that they have to rethink their data maturity assessment, start building stronger first-party data assets and leverage new walled garden-specific solutions will be successful.