Digital advertising in a post-cookie world

Jimmy McCann

Head of Digital Strategy

Analytics and Data Science

In a recent post, we looked at how the introduction of ITP and privacy laws meant that marketers have lost the ability to track the touchpoints and conversions in every journey, impacting our ability to understand how different channels and digital campaigns contribute to conversions.


The loss of cookies and online identifiers also directly impacts our ability to advertise to users. Ad platforms use third-party data, collected by cookies, to profile and measure users. With the ability to collect these cookies from Safari and Firefox already gone, and Chrome getting rid of third-party cookies in 2022, targeting users through third-party data will soon no longer be an option.


There are two major issues advertisers face with a loss of third-party data:


  • Reduced targeting capabilities
  • Inaccurate measurement

Reduced targeting capabilities


The death of third-party data affects targeting in two ways: the first is by significantly reducing the accuracy and granularity of audience profiling, as there is no longer data available on users. This makes it harder to narrow down your audience and target by certain interests, demographics, intent, etc.


The second is by massively reducing remarketing reach. When cookie data is cleared or blocked completely, users are removed from remarketing lists faster or prevented from being put on them in the first place.


Remarketing is an incredible way of reaching users further down the sales funnel and presenting them with personalized and engaging ads and messaging.

Without users being put into remarketing campaigns, they will either only be shown generic ads – which are less efficient at converting as targeted and personalized ads – or they may be missed altogether.


Both options result in campaigns that are less efficient at creating conversions.

Cookie tracking

Inaccurate measurement


Without third-party cookies, advertising platforms cannot see what actions users performed after seeing an ad. This means that they must find alternative solutions to track conversions. Advertisers can still understand what their users did from their ad campaigns if they arrive at their website/app via a click, using diligent ad campaign tagging and an analytics data source.


This means advertisers are unable to measure the true impact of campaigns, insights which are crucial in making informed budget decisions.

Advertising in a post-cookie world


As our ability to use third-party data dwindles, advertising will become dependent on first-party data and protecting the individual’s right to privacy.


Like it or not, there is no going back to tracking individual users across the web. As advertisers, we should accept that things are different now and focus our attention on what we can do using first-party data, instead of looking for any hacks or workarounds.

Targeting without third-party data


The value of first-party data in marketing has grown substantially over the years, for good reason: first-party data directly reflects your customers’ behavior, making it more accurate and more relevant than third-party data.


As we become less able to rely on third-party data, first-party data becomes even more valuable – from web analytics to offline data via your CRM, these are invaluable insights that reveal who is engaging with your business and how.


Bringing together all your first-party data within a central data warehouse such as BigQuery is key to building an in-depth picture of who your audience is. These audiences can then be pushed back into your advertising platforms, where they can be remarketed to, or used to create lookalike audiences that can be targeted.


In addition to your own first-party data, advertisers will be able to use publishers’ first-party data to target audiences. For example, Google collects first-party data on users. Google uses this data to profile users into in-market, affinity, customer match and lookalike audiences, which can be targeted in your Google Ads campaigns; Google’s Privacy Sandbox allows individuals data to be aggregated and anonymized, meaning advertisers can target groups of users without violating the privacy rights. We expect other publishers like Facebook to come up with their own solutions too.

Even with these new solutions, targeting will not be as granular as it was using third-party cookies to target people across the web. To further improve campaign performance, advertisers will need to incorporate contextual targeting to get in front of the right audience.


Before targeting abilities in advertising platforms became so advanced, advertisers would use contextual targeting to get in front of relevant audiences. As it becomes harder to target ads to individual users, contextual targeting will become (once again) an important factor in ensuring ads are shown to relevant audiences.

Contextual targeting involves placing ads within the right context by targeting inventory that matches the content and keywords of your ad, e.g. showing ads that match the content that the user is consuming.

The good thing about contextual targeting is that it is much less aggressive compared to cookie-based targeting. Many individuals express frustration at being followed by ads around the web – there is an ongoing debate by consumers that their devices are listening to them, which comes as a result of such granular and behavior-based targeting as they are consistently shown hyper-relevant ads. While this does generally increase conversions, it can also lead to ad fatigue and irritation.


When carrying out contextual targeting, it is important to first research and understand the key categories for your audience so that you can run effective contextual campaigns. Building out inclusion and exclusion lists, intent keywords, etc., allows you to narrow down your audience to those that are most likely to convert.

Digital advertising in a post cookie world

Measuring without third-party data


Without cookies and other online identifiers, reporting will be much less transparent and there will be a reduced ability to attribute conversions back to a user’s previous activity. Rather than focusing on everyone’s individual customer journey, the focus will be on the trends and patterns of combined customer journeys.

Google has already begun putting together solutions that use machine learning models and historical data to show marketers the customer journey, from first-click to post-conversion, even with touchpoints and conversions missing, which we talk about in this blog.


We also feel for more mid and upper-funnel activity, advertisers should be looking at the engagement actions this activity drives and valuing it. This way you can better quantify the value that this activity is driving.


Alongside this, marketers will need to consider undertaking their own cloud and machine learning projects on their first-party data too. Training an algorithm to analyze historical data from your analytics, CRM and ad platforms and find patterns and trends will help you to prove the value of your activity. It also allows for different metrics to be calculated, for example, lifetime value, which can be used to optimize your advertising campaigns.

Need help getting your data in order?


This webinar is part of our wider B2B Playbook that is designed to help B2B businesses with all aspects of their digital marketing from leveraging data, acquiring more traffic, creating assets that resonate and succeeding internationally.

In the data section we’ve compiled useful insights on everything you’ll need to know, from capturing data to building advanced machine learning solutions using your first-party data.

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