The Machine-Led, Cookie-Less Future of Online Advertising
Online advertising is heading toward a major shift in trajectory — A complete rethink of a model that is the product of a Wild West era in the industry’s growth. Users have been gradually becoming more aware of their online privacy and recent regulations are setting the foundations for a very different future. Brands need to be thinking about these shifts now and preparing accordingly. One thing is for sure, a future-proof data and media strategy has privacy at its core with AI and machine learning leading the way.
Until now, marketers have relied on third-party cookies to track users’ journeys across the web, implementing cross-site, behavioral retargeting with data-driven attribution models. Simply put, third-party cookies are how that ad for those new headphones finally convinced you to click and purchase while you read the evening news, and how the underlying economic model accounts for all the parties involved.
An HTTP cookie (web cookie, browser cookie) is a small piece of data that a server sends to the user’s web browser. The browser may store it and send it back with later requests to the same server MDN Web Docs
A brief history
Over recent years the general public has become far more aware of their online privacy and data footprint due to multiple high profile hacking scandals, releasing personal data into the hands of criminals, and the public outrage at the Facebook-Cambridge Analytica data scandal of 2018.
Governments have been intervening and major regulations have been introduced in both Europe and the US. The General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA) are examples of such regulation, focusing on data protection and privacy. This shift puts the power back in the hands of users, not only providing their consent to share data, but their choice of what, how, and when.
Data privacy was in the news again recently with Apple announcing new privacy features for devices at the Worldwide Developers Conference . These updates allow more fine-grained control over data sharing preferences, as well as labels for all apps in the App Store, clearly informing users of what data applications are requesting, and how they plan on using it.
Web browsers are also moving away from third-party cookies. Mozilla’s Enhanced Tracking Protection (ETP) and Apple’s Intelligent Tracking Prevention (ITP) block third-party cookies by default in Firefox and Safari. In January 2020 Google went one step further, announcing Chrome would remove support for third-party cookies altogether in two years.
So it’s clear: the ad-tech business model needs to be re-thought. There are several ways publishers, advertisers, and marketers can begin implementing forward-thinking strategies today, preparing for the larger shift coming in the not-too-distant future.
One form of online advertising that is less prevalent these days is contextual advertising. This model is based on the idea of rendering advertisements relevant to the content and context of the page a user is currently engaging with. If you are reading up on how to implement your latest DIY project, it would make sense for brands to pay publishers to serve you ads for furniture, tools, and perhaps, if you are anything like me, services of professionals. This model does not track users across websites, as the advertising is simply based upon the context of the page.
Facebook has arguably led the charge in people-based advertising, built upon the foundation of unique identifiers tied to a user rather than a browser or device. This technology does not rely on third-party cookies, but rather allows brands to advertise directly to individual users in locations they choose to authenticate and engage. Companies like Segment are innovating in the crowded ad-tech space, based on flowing data tracking models to and from a variety of systems, tied to users through unique identification. The importance of this product category is not going unnoticed, highlighted by the recent Twilio acquisition of Segment for a modest $3.2B!
Google’s Privacy Sandbox
Google introduced Privacy Sandbox , a set of open standards and APIs to allow systems to query user profiling data in real-time. It all takes place in a sandboxed environment, private to the user and the website they have chosen to provide their data to. This model would enable marketers and engineers to create highly personalized, isolated experiences and advertising models without the need for third-party cookies, another example of the rise of marketing engineering.
AI and Machine Learning
Artificial intelligence and machine learning are thriving areas of research and the backbone of countless exciting new innovations and business models. Industry upon industry is being revolutionized by AI and machine learning, and it’s likely that the machines will shape the future of online advertising as well. Google recently announced a number of updates to its Analytics product, the clear leader in website analytics, tracking the vast majority of the internet. The updates, known as GA4, introduce a number of enhanced tracking and reporting features across the board. However, perhaps most interestingly, GA4 sees the introduction of a more enhanced machine learning insights model.
Artificial intelligence and machine learning models can provide an ever-evolving and constantly improving understanding of customers. Providing insights and predictions based on learnings from data fed through the models, machines will help us pave over the gaps in datasets, due to users’ growing flexibility in privacy and data sharing, with algorithms and math. And AI will provide more intelligent data-driven insights and enhanced predictions that will see the online advertising world become even more programmatic, automated, and run by the machines.
Start preparing now
It’s important that brands begin preparing for this future and look to build data & analytics strategies with privacy, transparency, and flexibility for customers at their core. Below is a three-point plan for brands who are looking for guidance on where to begin this daunting journey.
GDPR / CCPA Compliance
GA4 App + Web Property
Install the new Google Analytics 4 property. This is as simple as a few clicks of a button and allows you to take advantage of the new GA4 features right away. This includes a user properties model with identification (more on that below), as well as enhanced reporting features and the exciting new machine learning model. Configure the new property as soon as possible so you can begin collecting data and strengthening your machine learning models for the cookie-less future.
User Profiles & Identification
If you don’t already then you should begin to consider a user identification strategy. This is far easier for marketers working on e-commerce, products, and software-as-a-service tools, with daily active users and an authentication system. However, for marketers working on websites focused on content strategy, media, and driving users to conversion, it can be a little harder. What information do you gather from your users that could enable you to identify them within your data strategy? Do you collect email addresses for gated content? If you can find a way to identify users across devices then you can begin to build up profiles based on their engagement. GA4 offers a user profiling system, as do more specialized tools such as Segment and Mixpanel. Whatever your choice of platform, focus on finding a way to identify and track users at the individual level, allowing you to get more granular with your cohort analysis.
The world is changing, and our best practices need to evolve accordingly. It may seem daunting but fear not, this will not be a sudden change. If we act early we have time to prepare for the machine-led cookie-less world of online advertising.
Book a 30-minute consultation with a growth strategist to discuss how we can help you manage the cookie-less future of online advertising.