Dive into innovative solutions and alternatives for publishers to navigate the impending cookieless world. Hear from industry experts as we explore Seller-Defined Audiences, Contextual Targeting, Universal IDs, iOS vs. Android CPM dynamics, Differential Privacy, Privacy Sandbox, and more!
It’s no news that Google is set to phase out 3rd-party browser cookies in Q1 2024, and the industry continues to explore various proposals to address the shortcomings of previous alternatives to emerge with the best alternatives to help boost revenue especially for publishers. Notably, Google Topics has emerged as one of the prominent solutions, despite facing rejection due to inherent issues.
Several fascinating trends have emerged from this effort, including a heightened emphasis on first-party data, brand collaborations, and less obtrusive targeting approaches. In due course, publishers will need to acknowledge the imminent decline in targeting capabilities and adopt alternative strategies.
One contender that has gained attention is the concept of Seller-Defined Audiences, which was introduced by the Interactive Advertising Bureau (IAB) and recognized by Google a few months ago. While it holds promise, the question remains whether it can entirely replace cookies or if a fusion of various solutions will be imperative in the cookieless era.
As first-party data can be coming from a number of different things - from collecting login data like - age and sex, to what content this person browses like in contextual ads, and audience segments, to even - what viewability pool it goes into, SDA can be a powerful tool to several different publisher verticals, not only the ones with an email database.
"I'm hopeful that a contextual approach with seller-defined audiences can improve the situation. It combines the best of both worlds: publishers can curate content, and advertisers can target effectively. This fosters a healthier ecosystem compared to excessive tracking of individuals. Let's hope it works out." - James Jackson, Expert in business intelligence and product consultant stated in a podcast “2023 Trends and Predictions In Programmatic” released earlier this year.
As an alternative, contextual targeting is like a phoenix in the ad market - once a popular solution, it declined when better targeting practices appeared, but it’s gaining momentum again. This approach, rather than relying on user-specific data, concentrates on the content itself to deliver relevant ads while maintaining user privacy. Contextual targeting ensures that ads remain contextually relevant without relying on cookies by utilizing keywords and phrases on a web page.
This change positively affects the UX, as the ads they encounter are now more relevant and, consequently, more viewable. According to Kenneth Research, approximately 69% of consumers engage more with contextually relevant material, including advertisements.
In light of the impending crash of browser cookies and its potential impact on programmatic revenue, publishers are now focusing on contextual targeting. According to research by MarketsandMarkets, contextual advertising will be worth $297.68 billion by 2023.
Moreover, the report from GumGum reveals that contextually relevant ads generate 43% more neural engagement, and users are 2.2 times more likely to recall such advertisements than others.
When it comes to contextual targeting, publishers should consider the content of their page; they can index their websites first and then send along the relevant data to buyers. As we know from the past though - this can also have a negative impact on editorial decisions.
Page activity is essential so that publishers can transmit this information to buyers without retaining it. In addition, first-party data is integrated into the Wrapper, allowing contextual information to be extracted from the bid request process and then transmitted.
According to research, a substantial number of consumers do not dislike advertisements; rather, they are concerned about privacy breaches associated with data acquisition. According to a Clutch survey, 51% of consumers prefer to see advertisements from brands they trust.
This finding demonstrates that the problem is not with the data collection itself, but with how it is conducted. Personalized advertisements can ultimately benefit both marketers and consumers. This is where Universal IDs enter the picture.
Universal IDs are shared identifiers that facilitate identification across the ad tech supply chain without the use of cookies. Using deterministic matching, Universal IDs guarantee accurate user identification across multiple platforms. Some proposed Universal ID solutions include the Trade Desk’s Unified ID, DigiTrust by IAB, ID5, Publisher Common ID, etc.
They are also relatively easy to implement within prebid, and in some cases can be enriched with hashed login data.
One intriguing aspect of the evolving landscape is the trend in CPM rates between iOS and Android devices.
In 2019, Apple introduced Intelligent Tracking Prevention (ITP 2.1), which includes a series of new measures. Safari now clears most first-party cookies after seven days and automatically blocks all third-party cookies. As a result, device fingerprinting and long-tail measurement become exceedingly challenging. Harder targeting resulted in limited budgets being directed to iOS users, thus - often the price advertisers are paying for an iPhone user is half or even less of the one paid for an Android user.
However, with Google's recent decision to follow suit and depreciate cookies in Chrome, and the rise of identity solutions prior to that - the gap in CPMs between iOS and Android has become an area of interest.
Analyzing the data from monitored traffic, it is evident that Android devices, primarily using Chrome, currently enjoy higher CPMs. However, since January, which is notably the lowest month of the year - there has been a notable increase in CPMs for both iOS and Android. While Android CPMs have risen by approximately 30%, iOS CPMs have experienced a more substantial increase of nearly 60%. These observations, based on filtered US traffic, hints at the shifting dynamics between the two platforms and may just be the answered prayers for pubs.
Learn more about this data, explore worldwide opportunities, gain bidder intelligence, compare your performance, and obtain actionable insights to plan.
Differential privacy is another proposed method for securing user data across platforms. It is a statistical technique that focuses on creating aggregated data sets instead of collecting individual information.
Imagine a publisher’s platform that serves personalized ads to its users based on their interests and behaviors. In the past, this platform used third-party cookies to track users' online activities and precisely target ads to individuals. However, concerns about user privacy have prompted the platform to explore alternative methods.
In response, this new publisher platform has implemented differential privacy as a potential solution. Instead of tracking and storing individual user data, the platform now employs a statistical approach. When advertisers place ads on the platform, they receive aggregated data that provides insights into the overall performance of their campaigns, such as the number of ad impressions and the overall click-through rate.
For instance, if an advertiser's ad reaches 100,000 users, they would only receive generalized information like "70% of users viewed the ad," and "10% of viewers clicked on the ad." This way, advertisers gain valuable insights into their campaign's effectiveness without accessing specific details about individual users.
Differential privacy achieves this by injecting controlled noise into the data before sharing it with advertisers. As a result, the data is anonymized and prevents the extraction of sensitive information about any individual user's interactions with the ad.
This privacy-preserving approach, pioneered by Microsoft, has gained traction and is now embraced by major tech giants like Google and Apple, ensuring user data remains protected while still allowing marketers to gauge the overall success of their advertising efforts.
Alphabet Inc has introduced the Privacy Sandbox as a robust and transformative initiative to make Google cookieless. Given Google's significant reliance on third-party cookies, it is crucial for them to take the lead in providing an efficient cookieless advertising solution.
The Google Privacy Sandbox encompasses a range of projects that aim to enable user targeting without relying on cookies or cross-site targeting. These proposed solutions are not exclusive to Google; they also involve contributions from other partners. Among the popular initiatives within the Sandbox are FLEDGE and Topics API.
FLEDGE, now known as Protected Audience API, is designed to enable cookieless retargeting and audience creation, while Topics API focuses on interest-based advertising without tracking a user's web activity. However, both initiatives are currently under discussion and development.
In 2020, the IAB Tech Lab introduced Project ReArc, a focused initiative aimed at constructing an ad personalization framework that prioritizes user identity protection.
The primary objective behind Project ReArc was to establish technical standards for a Universal ID, as mentioned previously. The tech lab predicts that the absence of third-party cookies and mobile IDs will lead to a "default future state of digital media" where user data becomes 100% anonymous and non-addressable to third-party vendors.
Advertising direct addressability will now rely primarily on first-party data, exemplified by users willingly providing their own email addresses.
The IAB Tech Lab aims to develop robust technical standards and guidelines for the implementation of a universal identifier, ensuring the following conditions are met:
“The cookie’s death can lead to a better future for digital media globally. It’s an opportunity to change the practices, controls, and values surrounding personal data to favor consumers. IAB and IAB Tech Lab have already been hard at work, engaging our members to define practical solutions.” – Dennis Buchheim, EVP, and General Manager, IAB Tech Lab
Publisher cohorts are not formed by analyzing users' cross-domain browsing history. Instead, they are created by grouping consumers based on shared characteristics within a publisher's website. This categorization is achieved through direct interactions between publishers and their audience, enabling a one-to-one relationship that provides publishers with an in-depth comprehension of their users. Instead of relying on personally identifiable information or demographics, publishers collect first-party data from user behaviors, interests, assessments, viewing habits, submissions, and more.
By harnessing this type of first-party data, publishers can 100% recognize their audiences, enabling them to create highly targeted audiences at scale. As a result, advertisers can target these groups with highly relevant ads, and publishers can dynamically position consumers into multiple cohorts in real-time.
In addition, marketers have the option to merge their own first-party data for authenticated users with these cohorts in a privacy-compliant manner, enabling an even more effective and laser-targeting approach to audiences without the use of third-party cookies.
In response to the recent discourse on publisher cohorts and first-party data platforms, it's imperative to address the quintessential question on every publisher's mind: What can I do as a Publisher?
While small and medium-sized publishers may lack the resources, larger publishers keep on trying to develop their own first-party data platforms. These platforms encompass various ad tech tools and audience data platforms.
Some examples of ad tech tools that prioritize first-party data include Insider Inc's SÁGA too, on the other hand, publishers like Business Insider, Future plc, and Vox Media have established businesses centered around first-party data, creating platforms that package their consented audiences for advertisers. These platforms offer a privacy-complaint solution in light of third-party data depreciation.
As we look ahead, the advertising landscape is undergoing significant transformation. In the end - it’s going to be about - how advertisers are retargeting, whether they are buying similar audiences or just pushing ads next to relevant content.
Whether platforms such as Facebook and Google ads, which have more first-party data will prevail as opposed to publishers with their internal audiences. Is the market going to rely on one main solution, as it was with the third-party cookies, or a pool of different ones to choose from?
A side effect would be that more publishers might switch to the faster Prebid Server, as opposed to the slower but enriched third-party cookies Prebid Client, which is prevalent right now.
These developments are shaping the industry's future, as it actively prepares for the cookieless world.
The programmatic advertising sector - both for supply and demand, faces challenges, but also opportunities as the countdown to the demise of browser cookies continues. Privacy concerns demand careful consideration while exploring alternative solutions becomes crucial. Striking the right balance between revenue generation and quality content is a priority for all stakeholders.
To address these changes, leveraging AI and machine learning can empower publishers to provide consumers with a more positive experience. By offering ad content that truly resonates with users, publishers can enhance engagement and satisfaction. Simultaneously, contextual advertising in reputable publishers ensures brands avoid association with negative content, safeguarding their desired audience's positive perception.
Interested in what the future holds? Check out the latest AY Industry Report! Conducting worldwide in-field visits and holding conversations with 100+ Publishers across six countries and three continents combined with up-to-date AY Global Ad Revenue Index Data, Assertive Yield has released a detailed report on the current state of the market with predictions of newer trends and innovations emerging in the following quarters of the year. Find out more.
Cookieless browsers are web browsers that do not use third-party cookies, focusing on user privacy and alternative targeting and user profiling methods.
Browsers like Safari, Firefox, and privacy-focused ones like Brave and DuckDuckGo already limit or do not use third-party cookies.
Alternatives include first-party data, contextual targeting, Universal IDs, differential privacy, and solutions like Google's Privacy Sandbox.
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