Google’s announcement on phasing out the support for third-party cookies within the next two years has led to a lot of speculation around its impact on the ad tech ecosystem. An obvious question to raise is how it will impact data-driven personalized marketing going forward?
Data have, for years, been celebrated as the “New Oil of the Digital Economy”. And, just like oil powers, a significant share of the analog economy, data powers a considerable percentage of the digital economy. But like oil, data also comes with potential negative externalities in the shape of both privacy concerns and the ever-present danger of abuse. Too little effort has been invested in securing the long term sustainability of data-driven personalized marketing, e.g. “renewables” of the data economy.
Part of the media industry had high hopes that stringent enforcement of the General Data Protection Regulation (GDPR) would result in a return to the “good old days” of doing online business without personalized marketing. It is, however, doubtful given that the digital economy has radically transformed both consumer behavior as well as marketing channels, which have resulted in a profound shift from mass marketing to individualized communication. Data did not cause the change per se. It was merely a catalyst. Allow me to explain.
In the age of mass marketing on TV, radio etc., mass consumer brands had the upper hand because they thrived (comparatively) in an economy where audience targeting was rudimentary. Compared to niche brands, they did not ‘waste’ as much ad spend outside target groups simply because most people were in the target group. Hence the growth of mass consumer brands.
Digital economy opened the long tail – both because niches grew global in size and because they could be reached in an economic way. Hence, individual preferences can now better be served.
The digital economy created a shift away from a few mainstream products (think Budweiser) and markets toward a vast number of global niches in the long tail (think microbrews). The implication is that marketing budgets increasingly have been shifting away from blockbusters, that are well suited for traditional mass media- and marketing (like TV and omnibus media) towards niche markets that require personal marketing to succeed.
The shift to long-tail consumption has also meant a shift in marketing demand. Shortly put; data-driven personal marketing is rapidly replacing mass marketing.
Intent and interest data combined with programmatic platforms became a catalyst for data-driven targeting – e.g. personal marketing. The early adopters in this marketing disruption were initially Google, who entered the market with real search intent, closely followed by Facebook, who joined the market with demographics, likes, and interest profiles.
Both are content aggregators, and both have built a compelling business-case by being able to:
- Deliver reach within narrow intent/interest/demographic target groups
- Self-service, end-to-end solution for SMBs
- Collect consent at scale
By offering these capabilities, Google and Facebook made it cost-efficient for SMB’s (as well as major brands) to purchase advertisement inventory towards specialized niche consumer markets.
And let us not forget that advances in technology also played a crucial role in advancing personalized marketing. In particular, the separation of impressions and targeting data through programmatic platforms. Before the internet, advertisers would pay one price that included both the impression (a page in the paper) and data (the papers reader profile). Hence, publishers could command a premium because they ‘owned’ a target group.
Now, impressions and data are no longer linked. Almost every impression is auctioned away through programmatic platforms, for a low price no matter what type of publisher. At the same time, the buyer, with most data about the viewer, charges the premium.
Publishers often know the least about the audience on a specific impression, so they tend to become low-value CPM pipes. In other words, the publishers’ historic value capture model has been disrupted (see box below). Publishers deliver big X (critical democratic institution and eyeballs) but capture very little of the value (Y).
What makes a business valuable? There’s basically a very simple formula. If you have a valuable company, two things are true.
- Number one, that it creates “X” dollars of value for the world.
- Number two, that you capture “Y” percent of “X.”
The critical thing people always miss is that “X” and “Y” are completely independent variables. So “X” can be very big and “Y” can be very small. “X” can be an intermediate size and if “Y” is reasonably big, you can still have a very big business.
So to create a valuable company you have to basically both create something of value and capture some fraction of the value of what you’ve created.
A roll-back to the “good old days” of mass marketing is, in my view, extremely unlikely:
- The digital market economy is shifting away from mass-communication and blockbuster products for good. Personalization is here to stay.
- Technology that de-coupled impressions and data is also unlikely to be rolled back due to the inherent fragmentation of media consumption (again long-tail). Cross-media buying is more crucial than ever (aggregator role).
- Abandonment of third-party cookies has little effect on the Walled Gardens of the US tech giants. Once a data ecosystem becomes large enough, it becomes a sustainable first-party data-economy in itself.
- GDPR will have little bearing on the tech giants’ walled gardens since user-consent is easy to collect if you have an attractive consumer-service with a market position that leaves consumers with very few alternatives.
The current situation leaves many traditional publishers with a very real problem at hand, given that the majority of them are too small to build their competing walled garden(s). Either due to lack of size (readers) or lack of resources to develop technology at scale. Transforming an entire organization from a content producer to an innovative tech-company is an expensive and challenging task, especially when you play catch-up.
It’s a fundamental problem that needs a solution. While many industries in the past have succumbed to market disruption and technological advances, it is not an option this time around. A genuinely free and independent press is a vital component of any healthy democratic society. The principal value of journalism is that it imposes transparency, and thus accountability, on those who wield the greatest governmental and corporate power. And that requires funding. The reality right now is that the traditional publishers’ revenue bases are eroding. Ad revenue as well as subscription revenue. We are witnessing an era where cutbacks and layoffs amongst the traditional publishers are becoming the new normal.
This is why delivering data-driven personalized marketing in a post-cookie-world is a problem that must be solved, which is why AudienceProject for years (in close cooperation with publishers) has been working on bringing several supply-side solutions to the market.
A Road forward
To better understand what constitutes a future approach to audience targeting in today’s digital environment, we need to start with the problem.
The problem at hand
Relying on third-party cookies for targeting has been a profoundly challenging methodology for years. While cookies by many are seen as omnipotent technology to gather detailed personal data, the reality is quite different.
Firstly, advertisers have a long tail of choice. More and more digital media channels have, over time, become available for advertisers:
- Programmatic display advertising
- Search (Google Ads etc.)
- Social media (Facebook, Instagram, Snapchat, Twitter etc.)
- Direct IO advertisement and sponsorships
- Digital video (YouTube etc.)
Tracking users and devices cross-channel to build targeting profiles by third-party cookies have been increasingly challenging due to different platforms using different sets of identifiers and due to the rise of the walled gardens.
Secondly, there has been an explosion of different connected devices which consumers use, with the same person often owning multiple devices:
- Smartphone web-browser
- Smartphone in-app browser
- Smartphone native app
- Laptop web-browser
- Tablet web-browser
- Tablet in-app browser
- Tablet native app
- Smart TV
- Gaming console
Each of the above operates with a different set of identifiers (if an identifier is even present). Again, we see the rise of walled gardens and more and more devices operating with proprietary or no ID systems at all. But walled garden data is not an option for many types of cross-media campaigns. Data consistency does matter. The advertiser needs assurance that they are not buying the same “high-income reader” 20 times across 20 different first-party data pools, each modelled after a different methodology.
Thirdly, there is the rise of regulation like ITP, GDPR and ePrivacy consent and ad blockers. The total share of devices that have consented cookies for targeting purposes is small.
Fourthly, even if we still ignore all of the above, the average ad tech third-party cookie has a half-life of twelve days. That’s it. Twelve days after a cookie’s inception, 50% of the cookies from the same cohort are dead.
Unfortunately, the ad tech industry has been trying to solve the challenges above with even more tracking technology like fingerprinting and adding even more cookie-based identifier systems. Resources have increasingly been invested in creating “new” technologies that would allow the industry to maintain the “old” ability to track individuals across as many online interactions as possible. Privacy has not exactly been the first item on the agenda, which is also why the industry at large is experiencing a privacy backlash today.
In my opinion, the industry just kept doing so because it’s an easy sell. Everyone can relate to the story about omnipotent cookies tracking every user’s online behavior to build omnipotent targeting segments.
A future approach
Any targeting solution going forward must, as a bare minimum, adhere to the following principles:
- Adhere to GDPR/Privacy legislation whenever handling data that constitutes personal data.
- Offer robust industry-standard consent management systems.
- Be able to operate without third-party cookies and/or cookies entirely.
- Deliver high quality predictions without access to third-party long-tail data.
- Offer cross-media coverage with uniform data segments.
The immediate answer many have turned towards is the use of first-party data delivered by walled gardens. While a few US tech companies have been able to offer such services due to their immense size and market coverage, very few publishers have been capable of matching the offer. They often lack the footprint of the US content aggregators.
An often-overlooked challenge is also that the predictive signal for generating segments can be very sparse across mainstream media – mainstream media is named “mainstream” for a reason. Once you lose access to the long tail of third-party signals, building narrow specialized intent segments becomes increasingly tricky.
And even first-party data pools suffer from a significant gap in coverage. Cookies are not omnipotent. And you can’t force every user on every device to be continuously logged in to your service (unless of course, you have an attractive monopoly service).
In recent years, AudienceProject has helped numerous publishers overcome many of these challenges through our ‘Full Reach’ offering. ‘Full Reach’ is AudienceProject’s promise to deliver an audience segment on every impression and video view.
We have successfully enabled many of our clients to build and deploy segments that can operate independently of third-party cookies, which have allowed them to monetize traffic from Apple devices affected by the increasingly aggressive rollout of ITP. The very same methodology can be applied to handle the upcoming abandonment of the third-party cookie.
Our ‘Full Reach’ offering operates with multiple new advanced predictive models, each specifically suited to build different types of audiences. Our service will automatically pick the right kind of probabilistic model required for serving a specific ad to a particular audience which results in significant improvements in both reach and precision compared to (potentially) stale DMP information tied to cookie-based device identifiers. ‘Full Reach’ operates as a real-time model, processing all available data-points to make classification decisions instantaneously.
Instead of evangelizing ONE model, we believe in using the best suitable solution for any given impression.
A real-life case
Learn how a German sales house has been able to improve their advertiser offering, by using our ‘Full Reach’ technology. Learn more.
This article is 3 of 4 in our series: Life beyond third-party cookies
About this article series
Google decision to phase out support for third-party cookies in Chrome has led to concerns on how the ad-tech ecosystem will be influenced. In this series of articles, AudienceProject addresses those concerns and explains why Google has made the right move.