Adapting to the changing Ad Tech Environment

Late last year, Google finalized their plans for the depreciation of third-party cookies taking place in 2024, adding to the existing complexities of the Ad Tech environment. Since January, for testing purposes, Google has restricted third-party cookies to 1% of its users by default. Their plan is to completely restrict third-party cookies for 100% of users from Q3 of this year.

This cookiepocalypse has left many companies in a state of panic as numerous advertisers are dependent on third-party cookies for their processes. In fact, a 2023 study by Adobe found that 75% of marketing leaders still rely heavily on third-party cookies.

Soon, we can expect that third-party cookies will be phased out completely. Given this change, how can we, as companies, adapt to this new ad tech environment?

In this article, we would like to look at some common AdTech scenarios or use cases that require cookies and explore the changes caused by the deprecation of cookies. We are broadly categorizing these use cases into the following buckets:

Website Functioning and Basics

The testing phase for organizations trying out Google's Privacy Sandbox will continue till early Q3 2024, after which third-party cookies will be phased out entirely. Here are some steps, as suggested by Google themselves, that you can take to ensure that your websites run even without third-party cookies:

1. Audit the third-party cookies on your website, which will include identifying third-party cookies and locating their sources. Browser dev tools can be used to identify and locate third-party cookies. A guide to auditing cookies on your website can be found here. 
   

2. Test your site's performance without third-party cookies. Cookie blockers can be used to disable third-party cookies. A new way to test your website is to use the third-party cookie phase-out flag, which accurately depicts how Chrome will act post-Q4. A guide to using this flag and testing for breakage can be found here.
   

3. Next, you will need to create and implement a solution to minimize site breakage in a cookie-less environment. A good way to go about this is to first gain a proper understanding of the services on your website that are dependent on first-party cookie data vs third-party cookie data. Here, it is important to look at the intended use cases for your websites to see how they function without third-party cookies. Google suggests that this is a good time to get rid of unnecessary tags and embeds and improve page load performance. To help understand where users experience problems and how breakage is being fixed, these breakages can be reported by creating an 'issue' on the third-party deprecation breakage repository.
   

4. Once the services dependent on first-party cookie data vs third-party cookie data have been identified, we may potentially see that certain use cases may no longer work. To minimize site breakage, it might be necessary to transition to certain advertising alternatives. Google also offers certain alternatives, including – using CHIPS, Storage Access APIs (SAAs), and Related Website Sets (RWS) for organizations having related websites with different domain names. CHIPS for example, allows developers to opt a cookie into partitioned storage, with separate cookie jars per top-level site. Without partitioning, third-party cookies can track users and combine information from many unrelated sites. As unpartitioned third-party cookies are getting phased out, CHIPS, the Storage Access API, and Related Website Sets will be the only way to read and write cookies with cross-site contexts, such as iframes.
   

Targeting and Re-Targeting

Targeting audiences will become difficult without third-party cookies. So, companies have to evaluate and adopt alternative methods to targeting, including predictive analytics, better personalization, and conversational marketing. Some use cases for targeting, like Exclusion Campaigns, Look Alike Campaign, etc, which are common in the Adtech world, are either not possible or need to be run differently.

One very common initial step to approaching the cookiepocalypse is to properly understand and leverage your first-party cookies and the data that is collected from these cookies.

In general, clear and transparent notices about the data collection and providing value in exchange for data will be winning strategies because many users don't mind personalized advertising if they are not kept in the dark about the data that is collected to drive personalization.

Increased Content Based Marketing is also beneficial. For example,, Westwing, an online-only furniture retailer based in Germany, as part of the transition from third party cookies spends a major portion of its budget on content creation. They focus on content that is very closely tied to the company's products. The content-driven user engagement generates helps in developing deeper bonds with customers. Based on the current example, content-based marketing generates a higher return on marketing investment than paid advertising for Westwing.

Another growingly popular alternative has been contextual advertising. This concept uses machine learning (ML) to obtain contextual clues from the webpage, such as the content of the web page or the domain. This type of advertising does not track user data; instead, it focuses on the content and other context surrounding the page or transaction to deliver the right kind of ad. For example, a user watching a video about dogs might see an advertisement about pet products.

Another alternative to explore is strategic partnerships with companies selling complementary products. For example, a manufacturer of tires could partner with a retailer to combine browsing history data with shopping cart data. This will help in driving insights. For example, tracking the Tire products the user researched on the manufacturer's website and the products the user ended up buying on the retailer's website. Insights from such partnerships can help inform the types of campaigns and marketing incentives that are needed to increase the conversion rate and encourage repeat purchases. Data Sharing with marketing partners can be done through data clean rooms. This can ensure neither party reveals personal information, but both parties can access the shared data to build audience segments and other insights.

Advertisers are also exploring the option of using persistent identifiers. The Trade Desk, Zeotap, and other players are working to establish universal IDs anchored by identifiers such as email addresses. Universal ID functions as a master first-party 'cookie' but one that is persistent and valid across all data-collection channels. This ID can then be used for relevant second or third-party data enrichment. However, this solution will require transparent disclosure and consent from the users to be privacy compliant.

Campaign Dynamics

Another key area that will be impacted by the demise of third-party cookies is frequency capping. To put it briefly, frequency capping is a technique that limits the number of times an ad is shown to the same user or device. This is done because over-exposure to the same ads can lead to negative impressions towards the brand. However, this technique is heavily dependent on third-party cookies.

Advertisers will have to look for alternative methods to limit the exposure of ads so that users can maintain a positive impression of brands. One such alternative is flexi-capping. Flexi-capping involves using the available ID signals or identifiers that are accessible along with contextual data, such as time of day, weather, etc, to enable the capping of ads. Publisher-specific/site-specific IDs and IP addresses are also used for this technique.

Looking further, we see that previously, marketing measurement tools relied heavily on the data collected from cookies. However, in a world where third-party cookie data is not accessible, how can companies successfully measure the success of their marketing activities?

Measurement and Reporting

Advertisers and their partners in the advertising ecosystem mostly relied on aggregated data and econometric models to assess the return on their advertising investments. Recently, there was a drive to get granular and understand the impact of advertising at an individual user level. Obviously, this helps to better understand the users' journeys and provides more clarity around ROI calculations. However, in the absence of cookies and device identifiers, this will become very difficult.

Measurement will change in the following ways.

1. More reliance on first-party and data from reliable partners for measurement. If there is a persistent or universal ID, then it will also be possible to use the data from website visits, app downloads, etc, to build an ID graph.
   

2. Rely more on high-level metrics around brand awareness, such as customer satisfaction and loyalty. 
   

3. Using machine learning and probabilistic models to analyze the outcome of marketing activities.

Sources of data for measurement: Person-level data gathered in compliance with applicable privacy regulations will be augmented with relevant aggregated data, such as geospatial information (for example, changes in advertising exposure and conversion patterns by geography over time).

This is no doubt a massive change and can even prove to be quite a hurdle for many companies. However, It is important to recognize the opportunity that lies here. It is so crucial to understand our users and their needs. Users appreciate a personalized experience; what they don't like is cross-site tracking and collection of data without consent. Users are willing to share their data if they understand its positive impact on a personalized experience, and most importantly, they are willing to share their data if we ask for their consent in the first place and treat this data responsibly.

Ultimately, it is also important to understand that the path ahead is not straightforward. Companies are faced with multiple choices and opportunities, and they will need to figure out the solutions that best work for them. Additionally, and very importantly, marketing, Adtech, and privacy teams will need to start having proactive dialogue with each other on the changes and the path forward. As companies shift towards more first-party data and other cookie-less alternatives – they will have to ensure that any new solution that is implemented is also privacy-friendly and compliant.

At Meru, we use our years of experience in Adtech to bring you solutions that can help you thrive in the Adtech world despite the constant evolutions. We help you configure cookie consent mechanisms, manage universal opt-out, monitor the sale/sharing of information to third parties for advertising purposes, and much more. Reach out to us today via our website to help us help you successfully navigate the world of Ad technology.