The shift towards cookieless advertising has been set in motion by growing concerns about privacy and data protection. Users have become more aware of how their data is used and have started demanding more transparency and control. In response, technology giants like Google have announced plans to phase out the use of third-party cookies on their platforms. The move has sparked a wave of innovation, with companies developing new ways to deliver targeted ads without relying on cookies.
In the digital world, cookies play a vital role in enhancing the user experience and providing personalised content. There are two types of cookies: 1st party and 3rd party cookies. While they may sound similar, they have distinct characteristics and purposes. Let’s dive into the differences between 1st party and 3rd party cookies, shedding light on their functionality and implications for user privacy.
Cookies are small pieces of data that are stored in a user's web browser when they visit a website. They play a crucial role in digital advertising, allowing advertisers to track users' online behaviour and deliver personalised ads. However, the use of cookies, especially third-party cookies, has raised privacy concerns. Users feel uncomfortable with the way their online behaviour is tracked and used for advertising. This has led to stricter data protection regulations and changes in browser technology.
Third-party cookies are set by a website other than the one a user is currently visiting. They are commonly used for tracking and behavioural advertising. However, their days are numbered. Browsers like Safari and Firefox have already blocked third-party cookies, and Google Chrome, the most popular browser, plans to follow suit in the second half of 2024. This move is part of a wider trend towards greater data privacy and user control.
A Mobile Ad Identifier (MAID) is a unique identifier associated with mobile devices, just like 3rd party cookies in browsers. It allows advertisers to deliver personalised ads and monitor user activity across various apps, which is different from cookies that are limited to specific browsers and domains. MAIDs, unlike cookies, are persistent and provide a more reliable method of tracking. They can function across multiple apps on a single device.
However, similar to the phasing out of third-party cookies due to privacy regulations like GDPR, MAIDs are also under scrutiny. For instance, Apple's iOS 14 update now mandates apps to obtain user consent before tracking them using the Identifier for Advertisers (IDFA). Both these changes reflect the evolving landscape of digital advertising, where user privacy is being prioritised.
The move away from 3rd party cookies doesn't mean the end of targeted advertising. Instead, it signals a shift towards new methods of tracking and targeting users. Cookieless targeting is an emerging approach that allows advertisers to deliver personalised ads without relying on third-party cookies. It's a more privacy-friendly approach that respects users' control over their data while still enabling effective advertising.
Here are various methods for cookieless targeting:
Google's Privacy Sandbox is currently the most well-known cohort-based approach being developed in the industry. Its objective is to provide the industry with an API that collects aggregated data on user profiles and aggregated campaign performance data for targeting, using predefined Topics, retargeting, and measurement, using Fledge method. The main goal of this initiative is to prevent AdTech platforms from tracking users' activities across different websites and devices, thereby avoiding scrutiny from privacy regulators.
However, the Privacy Sandbox approach comes with its own set of challenges. Firstly, it does not offer the same level of granularity that advertisers are accustomed to in Google's advertising platforms, leading to potential performance issues. Additionally, by relying on a common aggregated dataset, platforms and their clients will lose the ability to build their competitive advantage. This could result in more market share being driven towards big tech companies that already have established walled gardens.
Another challenge is the potential for fragmentation. It is uncertain whether other browsers will adopt the Privacy Sandbox approach. Mozilla, for example, has explicitly stated that they have no plans to implement it into Firefox. If this approach was not adopted by other browsers (e.g. Safari, Firefox, Brave), it will only cover Chromium-based browsers and result in an incomplete attempt to address the issue that it aimed to solve.
In summary, while Google's Privacy Sandbox offers a potential solution to privacy concerns and tracking issues, it also presents several challenges. These challenges include performance limitations, loss of differentiation, potential market concentration, fragmentation, and questions of fairness in rule-setting. It remains to be seen how the industry will navigate these challenges and whether Privacy Sandbox will be widely adopted by other browsers.
Many people consider first-party data to be the optimal choice in a cookieless environment. Publishers, especially, are advised to gather as much data as they can and develop strategies to enhance the volume of data they collect. This is done to increase the value of their audience to advertisers in the post-cookie era.
Publishers might create their own closed ecosystems, similar to the approach taken by renowned walled gardens like Google and Facebook. However, this approach fails to address cross-website frequency capping for brands and only provides a subpar solution for publishers operating on a large scale. Small and independent publishers with a niche target audience will suffer from not being able to monetise their inventory at the value they deserve.
Data clean rooms enable secure sharing of data between two parties without physically transferring any data. Within a clean room environment, data owners can merge their datasets in a computationally secure manner and leverage aggregated insights to enrich their own data. These solutions serve as a viable substitute for third-party cookies.
However, they necessitate brands to possess a substantial dataset. Consequently, implementing this method often leads to a limited number of matched data rows between brands and publishers, prompting publishers to employ look-a-like modelling to expand their audience and maximise media budgets. Unless your brand has a substantial number of rows in your data set, this approach tends to transform into probabilistic targeting indirectly.
In the post-cookie era, contextual advertising, a traditional method of ad targeting, is making a comeback. It operates by displaying ads relevant to the content of the webpage a user is browsing, making it a privacy-conscious alternative to third-party cookies.
Over the years, this method has seen great improvement, better semantic understanding and categorisation of the content help brands target relevant context as well as appear in brand-safe content.
However, it's important to note that while contextual advertising delivers targeted ads, it doesn't offer personalisation and can't replace identification solutions entirely.
Shared or universal identifiers have been created to identify users and share information within the advertising value chain for targeting, frequency capping and measurement purposes.
Identity solution providers collect signals from publishers to generate an ID that media owners share in the bid stream to monetise their traffic and enable advertisers to reach their target audience and measure the performance of their campaigns. The main challenge that shared IDs face is adoption. The value of an ID is strictly correlated to their adoption rate. Today there is a large variety of IDs available in the market but just a few of them have achieved the scale needed to prove their value.
Furthermore, the sharing of users’ data is strictly regulated in regions such as Europe, meaning that universal identity providers need to ensure that the signals they collect to generate an ID are gathered with the user’s consent and that they are only shared with authorised platforms. It is, therefore, crucial for ID vendors to implement technologies and mechanisms that ensure that users’ information is collected and shared legitimately. For this reason, many consider Universal IDs to be the ultimate replacement for cookies.
In the evolving landscape of digital advertising, it's clear that strategies need to pivot and adapt to the challenges and opportunities of a cookieless future. Whether you're blessed with an abundance of first-party data or grappling with its scarcity, solutions exist to navigate this terrain effectively.
And stay tuned – in our next article, we'll take a closer look at Universal IDs in Europe, equipping you with essential terminologies to bolster your understanding in this new field.
