July 9, 2021

Top 4 Cookieless Targeting Solutions

Targeting and identity solutions to prepare for a cookieless future

The digital sector has been harnessing data over the past decade mostly through the use of cookies, small text files that work like identification marks to track web pages as well as users who visit these sites.

Cookies help publishers and advertisers learn more about consumer audiences and aid in targeting consumers with relevant messages. Regulatory changes like the implementation of the GDPR (General Data Protection Regulation) and CCPA (California Consumer Privacy Act), not to mention various high-level data breaches in the past years, have raised even more concerns around data privacy, specifically consumers’ rights regarding how their data is collected, by who, and how it's used.

The programmatic industry is quickly evolving to bring consumer privacy front and center. Walled gardens like Facebook and Google are enforcing cookieless alternatives and plan to abolish the use of third-party cookies in the near future. Google recently announced it will postpone its third-party cookies deprecation until 2023 citing the need for further testing of its Privacy Sandbox.

So what do digital players need to do to start preparing for a cookieless future? Start implementing and testing cookieless solutions. In this article, we break down the top 4 cookieless identity solutions that advertisers, brands, and publishers can start enabling now to be prepared for a cookieless future when the time comes.

4 Cookieless tracking alternatives

1. First-party data

We know that first-party data is more accurate than third-party data. Brands and publishers, both big and small, must focus on using first-party data more strategically and in different ways.

Email addresses, in particular, will become the main focus for publishers. Login data is considered the highest standard of consumer data as it implies consent and accuracy, and it can be used as granular input for targeting and personalization. It’s even more valuable when consumers validate this data; for example, when a user signs up to receive emails or text messages in return for a discount or other benefit.

Let’s remember with first-party data collection advertisers and publishers are required to follow proper data privacy management, both from a regulatory standpoint, but also from a consumer engagement point of view. If brands aren’t engaging consumers in the right way with relevant information using first-party data targeting, then it’s pointless.

For large sites and networks that can request and implement logins for access to content, or those who with newsletters and frequent communication via email – sites like nytimes.com, spiegel.de, ilsole24ore.it, lefigaro.fr – first-party data will be the golden ticket.

Smaller publishers with loyal users who consume a lot of content may have a harder time scaling their first-party data growth. For first-party targeting to be effective and accurate, you need a lot of data to gain enough insightful information combined with AI and machine learning. One solution is joining a consortium or finding a first-party data partner to stay profitable.

Chief Technology Officer at Eyeota, Anand Das comments in his post for Smartbrief:
“Matching audience data with opt-in, first-party data from a partner, and from brand advertisers with registered users creates a clean and reliable database – but it will require a lot of customer data and machine learning to build models for matching profiles, manage accuracy for audience extension, look-alikes, to make it work. The more data, the more effective artificial intelligence and machine learning will be in ensuring unique and useful data. Taking this approach will eliminate duplication by looking at behavior, IP addresses and mobile IDs and making sure similar profiles are assigned to the right users.”

The key is to start early (as in now) in order to collect a meaningful amount of consumer data or establish partnerships to scale.

Types of data that will replace third-party cookies
Source: Statista - Survey of marketers and publishers from the United States as of 4th quarter 2020

2. Universal IDs

Universal ID, or shared ID, solutions attempt to map 1:1 what was previously done with cookies by using first-party data and offline data to create a user identifier (user ID). The Universal ID is created by consortiums (IAB, Advertising ID Consortium) and ad tech companies to identify users without having to sync cookies.

Unlike cookies which are based on probabilistic matching, most Universal IDs are created on the basis of deterministic matching. By using both first-party data (CRM) and offline data, a Universal ID can be constructed. One advantage of using universal IDs is that publishers and marketers can eliminate data loss and user duplication that happens when syncing cookie information across multiple platforms.

Here’s a great analogy by Jordan Mitchell, CEO at DigiTrust:
“You get one name tag and everyone you meet can read your name tag. In the current web experience, it’s like everyone can only read the name tag that they put on you. This means that you, as the delegate (or consumer), end up having hundreds of different name tags all over you. That is annoying for users.”

What are the benefits of Universal IDs?

Publishers and advertisers can display the right ads in the right context to consumers, and consumers themselves won’t find irrelevant ads that don’t appeal to them. “SSPs don’t have to pay to another network for data-syncing and storage. DSPs can participate in the auction more often and achieve advertisers’ campaign goals. Data providers get better match rates between the SSPs and DSPs.” Win win.

Drawbacks

Unified IDs do have some setbacks. First, various ID solutions are available from ad tech companies and consortiums but efficiency varies. Second, many Universal IDs are based on both first-party and third-party cookies making it vulnerable to Chrome’s eventual phaseout of third-party cookies.

3. Contextual targeting and semantic technologies

Modern contextual targeting is not your grandma’s context targeting. Contextual targeting today uses semantics, natural language processing of on-page text, the URL and meta information, combined with artificial intelligence and data processing in real time.

Couple that with programmatic buying, contextual targeting allows advertisers to safely rely on automated real-time purchase of individual ad impressions while perfectly matching ads to websites, articles and video content, rather than guessing who their target audiences might be.

Contextual targeting is privacy-safe and, with increasing data sophistication, more vendors will be investing in the technology.

Joe Manalac, senior agency sales partner at Oracle Data Cloud, elaborates on contextual targeting as a solution in his comment for The Drum:

“You can look at ‘contextual’ through a couple of different lenses: one where you're avoiding content and contexts that are unsuitable for your brand; and one where you have a contextual targeting strategy designed to achieve a particular KPI for your brand. There are multiple use cases for context and, in many ways, it’s a tried and tested solution, so it’s not surprising that context is moving more into the spotlight right now. Agencies are finding ways to use context campaigns to deliver goals traditionally achieved through ID-based targeting.

At Showheroes Group, we consider targeting techniques based on semantic concepts to be at least equal, with constant further development, even superior to the User-ID based techniques. We have relied on this approach ever since we implemented our business model and tech stack featuring the SemanticHero tool. Our success, growth and the feedback from our clients and partners speak for themselves.

Showheroes Semantic Matching Technologies increase the value of the publisher's inventory in terms of brand safety and compliance with data privacy regulations. Advertisers benefit from the user's subjective impression of "suitable video content" and "suitable advertising" which is actually reflected in better engagement rates and higher advertising effectiveness.

How does SemanticHero work?

Thanks to the knowledge extracted from analyzing images, audio, and video on the publisher’s websites, our AI-driven semantic matching technology – the SemanticHero – is able to choose the best-fitting content video from the platform’s video library whenever a request is issued through a ShowHeroes content unit. Not only is the choice of the content video based on semantic matching, also a corresponding ad will be chosen and inserted by our adserver.

“100% more full views and 3x more engagements are being generated for content videos delivered by our Semantic Hero engine when compared to videos in static playlists. We call this hyper-contextual targeting: when users find matching video content within an article, it is substantially more relevant to them than e.g. video recommendations based on user targeting. ShowHeroes semantic targeting offers best results for publishers, advertisers, and users at the same time while remaining 100% GDPR compliant and not relying on user tracking.”

ShowHeroes Group CEO, Ilhan Zengin, elaborates on semantic targeting as an alternative:

“The era of third-party cookies is coming to an end. What began as a shock for an entire industry has become an opportunity to make room for new technologies and approaches. Even without third-party cookies, users can be addressed according to their interests. In some cases even better. Particularly with video advertising, the context in which the commercial is running is highly relevant. Semantic targeting has been around for over ten years, but it's getting better all the time.

What we understand today, for example, is that users use and perceive the same content differently in different situations. So it's not just the content that's interesting, but also the contextual environment. We can now form semantic target groups without personal user data. We know with a high degree of probability that certain people have seen a certain commercial. And marketers can build on this knowledge.”

4. Cohorts and Data Clean Rooms

Data clean rooms are places where walled gardens like Google, Facebook and Amazon share aggregated rather than customer-level data with advertisers, while still exerting strict controls. 

Advertisers with first-party data will benefit the most from clean rooms as they offer a privacy-compliant methodology for matching first-party data.

How do clean rooms work?

First-party data from the advertiser is added to the same space in order to compare how it matches with aggregated data from other platforms. With this process, one can evaluate how different data sets match up, and then determine whether the right ads are reaching the same audiences and how many times.

What are the advantages?

Data clean rooms can deliver ad impression data at massive scale; however, within walled gardens, clean rooms are “programmed to receive data, but not to let it leave.” For multi-platform versions, clean rooms offer an ability to do multi-channel and multi-touch attribution at scale, across channels. Some clean room technology platforms have integrated audience graphs for matching, others leave that to the advertisers and partners to piece together.

Where does Google’s FLoC fit in?

Google’s Federated Learning of Cohorts (FLoC) inside the Chrome privacy sandbox launched in March 2021. It claims to be 95 percent as effective as third-party cookies, although the ad industry is skeptical. FLoCs aren’t strictly clean rooms, but they do tend to de-identify consumer data from clustering together based on attributes.

In Google’s product update on the Privacy Sandbox website March 30, 2021 - Privacy, sustainability and the importance of “and” - “[FLoC] rolled out as a developer-origin trial in Chrome” and a “new approach to interest-based advertising that both improves privacy and gives publishers a tool they need for viable advertising business models.”

The company goes on to state, “FLoC is still in development and we expect it to evolve based on input from the web community and learnings from this initial trial.” 

Then, the big announcement recently dropped by Google via Privacy Engineering Director Chrome, Vinay Goel’s post, An updated timeline for Privacy Sandbox milestones, stating, “it's become clear that more time is needed across the ecosystem to get this right” pushing back the third-party cookie deprecation in Chrome until 2023.

Why haven’t data clean rooms been adopted amongst all players? 

First, they aren’t cheap. By 2023, 80% of advertisers with media budgets of $1 billion or more will utilize data clean rooms, according to Gartner, which estimates there are 250 to 500 clean room deployments active or in development today.

Digiday journalist Seb Joseph explains in his WTF is a data clean room? article, “Google, Facebook and Amazon offer alternatives but logistical and political challenges that arise working with these giant platforms can put a strain on all parties. It’s also not within the walled gardens’ best interests to cede too much data to advertisers given how much value they derive from controlling their own data, particularly targeting data.”

A cookieless future: how ad tech will adapt

The ad industry is quickly adapting for a cookieless future and will prosper with smart, privacy-friendly targeting solutions as mentioned above. But cookieless readiness takes time. It’s fundamental that all industry players get started as soon as possible in order to reap success.

Publishers, advertisers, agencies, and consortiums also must work together to adapt to the changing market and essentially respond to the real message behind third-party data deprecation: consumer concern around privacy.

On the advertiser’ side, there is a real need for brand safety. Advertisers want their campaigns placed in safe and appropriate environments. Working alongside publishers on alternative targeting solutions will allow for their messages to hit the right audience in premium environments.

There will always be those who spend on cookie-based audiences until the last possible moment. But with sufficient pressure from the various data deprecation plans, every ad industry player is putting it in high gear to implement alternative targeting solutions that work. Now there’s just a bit more time to do more testing and receive feedback to find the right balance between data and performance.

Our advice is: don’t rely on a single cookieless solution. Whether you are a publisher or advertiser, depending on a single provider or solution could significantly impact your business with new regulations or policy updates. Large marketers can experiment in all pools with big budgets while some publishers might lean more on context, others on authenticated IDs.


If you’re interested in finding out more about how ShowHeroes Group contextual solutions can support your next campaigns or monetization efforts, please get in touch with us.


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