Cookieless Solutions: Pros and Cons of 6 Cookie Alternatives

There is little doubt that Google’s decision to end third party cookies is going to have a marked effect on the advertising industry.

According to IAB/Ipsos, in their State of Data report, the decision has put up to $10 billion of sell-side annual advertising revenue in jeopardy and our own research points to the fact that 34% of marketers think that the move will have a negative impact on their business.

But how do you effectively prepare for the cookieless future?

In this blog, we consider the pros and cons of 6 potential cookieless solutions including:

1. The replacement cookie solutions from Google
2. Focus on first-party data
3. Adopt contextual advertising
4. Create user identity graphs
5. Leverage digital fingerprinting
6. Use AI and Machine learning to replace cookies

The state of play on the removal of cookies

It is a fluid process right now, with Google moving the goalposts, but here is the latest on this:

• Concerns around user privacy and security, as well as the impact of legislation like GDPR and CCPA means Google has taken the decision to remove third party cookies from their Chrome browser
• The fact that Chrome had a market share of approximately 67% in 2022 (according to data from Statista) means the decision has huge implications for the advertising sector. With cookies on Chrome being a huge enable of targeting for ads such as display and re-targeting to date
• Google has now pushed the timeline for the deprecation of third-party cookies back to Q4 2024

6 cookieless solutions you can consider right now

So, against this backdrop how can you plan for what is coming next?

Here are 6 solutions for you to consider, which don’t rely on third-party cookies:

1. The replacement cookie solutions from Google

When Google announced that it was planning to remove third-party cookies it also committed to providing alternative solutions under the umbrella of its Privacy Sandbox Initiative.

According to Google:

The Privacy Sandbox initiative aims to create technologies that both protect people’s privacy online and give companies and developers tools to build thriving digital businesses.

And as part of this, Google won’t stop tracking altogether. But they are proposing a number of cookieless solutions including:

Google Topics API

Google Topics is designed for prospecting and involves assigning Chrome users a set of interests based on the websites they visit.

At a basic level, the browser gathers information about the Topics of the websites being visited for example high level topics such as ‘autos’, ‘dogs’ or ‘mens’ clothing’ are identified.

Each week Google Topics associates five of these topics to the user and includes a wildcard topic which is designed to ensure the privacy of the individual.

Advertisers are then able to show targeted adverts to the user against a list of 350 interest groups.

Google Topics is still in testing and development so here are some pros and cons based on what we know right now.

Pros of Google Topics API

• Google Topics is simpler to understand, implement and evaluate when it is compared to previous suggestions like the discontinued FloC proposals
• In theory it offers advertisers a privacy-safe way to deliver targeted (and re-targeted) advertising based on behaviours and specific interests. Although this is being challenged.
• Gives users a degree of control over preferences and the ability to opt-out

Cons of Google Topics API

• One of the major bugbears of Topics is the limited scope for targeting that it provides. At just 350 Topics to choose from it is well short of the 30,000 topic classifications which were first touted in the FloC proposals
• It is inevitable that a restricted topic set will also drive up competition for space which could drive up CPAs
• There are no guarantees around adoption of Google Topics at scale by other browser providers
• It is also worth noting that the Tag Group of the W3C has declared that Google Topics is not privacy compliant in their view

FLEDGE API

One of the areas most affected by the removal of cookies is re-targeting, which relies heavily on the data from cookies – and is a key part of the current armoury for a high percentage of brand owners.

Google’s proposed replacement in this area is their FLEDGE API. It works on the basis of adding Chrome users to different interest groups based on their browsing habits – something that happens on an individual browser basis.

Users are then able to opt-in on the browser to specific interest groups and can be served relevant ads based on this.

Again, FLEDGE is still a work in progress.

Pros of FLEDGE API

• Offers the potential for privacy safe re-targeting and customised advertising without the use of third party cookies
• Provides the ability for advertisers to generate profile groups based on DSPs or AdTech generally which provides some control over targeting

Cons of FLEDGE API

• One of the biggest drawbacks with Fledge is the fact that is relies on Chrome users opting in to receive targeted advertising. This could be problematic when you consider the low opt-in rates associated with similar initiatives like the Apple iOS 14.5 change
• Early signs are that FLEDGE lacks momentum amongst AdTech vendors

2. Focus in on first-party data

One of the side effects of Google’s decision-making around third-party cookies is that there is a renewed focus and buzz around the potential of first-party data. Which has been partly pushed into the background by the relative ease of access to third party data for behavioural targeting purposes.

There is a wealth of data and hidden insights on your customers dotted around your internal systems – from marketing automation systems and other MarTech tools, to CDP platforms and social media tools.

It can provide detailed information on the people who actually buy your products – how they engage with emails and your website, buying history and the sort of conversations they are having with your customer contact points.

By harnessing that data it is possible to profile and segment your audience into similar cohorts to the ones that Google is proposing in its Privacy Sandbox. And get targeted about the way you talk to them.

Given the right consents in place it also potentially avoids the privacy issues that plague other cookie-based solutions.

Pros of using first-party data

• Reduces your reliance on third party data which can leave you susceptible to changes in the market like the removal of cookies
• It is insight based on your customers not prospective buyers and it is unique and not available to your competitors. Which opens up more potential for personalisation and relationship-building
• Done properly it takes privacy concerns out of the equation
• Once you are up and running first-party data collection can potentially reduce costs around data – although you are likely to want to supplement it where you have gaps

Cons of using first-party data

• It is not an insignificant task to effectively harness first-party data. It takes time and effort to generate and maintain it
• It can be a real strain on your resources and budget if implemented properly
• Siloed approaches to technology and the way that teams are structured can be a roadblock to getting the single view you need on this type of data
• Not quite as easy as switching on third-party data solutions that are still available just now

3. Adopt contextual advertising

As the potential in behavioural based targeting of ads looks set, in the best case scenario, to be severely blunted by cookie changes contextual advertising looks like being one of the potential winners in the short term.

With contextual advertising, the focus is on delivering ads that make sense for the user, based on the content on a web page. Contextual advertising can be really powerful in its own right, but we believe that the combination of context and data is what will really help brands make a quantum leap.

So, using first party data to fully understand and profile your audience and then using that insight to target them more effectively on websites that align with their interests.

Pros of contextual advertising

• Removes the need for cookies and is more privacy friendly as a result
• Can be relatively simple to set up and less complex than other solutions
• Delivering adverts in the right context increases the relevance for the target audience. It is seen by only those who really need to see it

Cons of contextual advertising

• The level of competition in your market – and how much you are willing to pay relative to the competition for premium space – can impact your ad costs
• There is an argument that says that contextual advertising can be disruptive for the user – however, done well you can overcome this by telling a highly relevant story in your ads

4. Creating user identity graphs

Essentially, User Identity Graphs aim to bring together both PII and non-PII data into one single data store.

Then using a combination of deterministic and probabilistic matching this enables you to stitch together customer data from a range of different sources to create a single customer profile.

The focus here is on trying to bring together a picture of the customer journey from different touchpoints which are both internal on your websites and apps and third-party websites too.

This data can then be used to deliver highly personalised advertising experience through channels like programmatic advertising.

Pros of identity graphs

• It can handle a high degree of complexity in customer journeys and offers the potential for real-time personalisation of adverts at scale
• Can deliver personalisation of content on a real-time basis which can significantly help with conversion
• Can be scaled relatively easily across devices and channels

Cons of identity graphs

• This approach can leave you open to issues around privacy – or even accidental privacy breaches – relating to issues like the correct anonymization of PII data
• Creating your data set leaves you open to a lack of transparency and potential data issues with third party data providers
• Getting this type of solution up and running can be costly

5. Digital fingerprinting

Digital fingerprinting is a technique which allows you to profile and target web users by combining certain attributes of a device in order to uniquely identify it.

For example, it is possible to use ‘hit’ level data about a device – for example hardware type, browser version etc – to profile it and then match that data to subsequent web visits.

To identify users and join together data from multiple sessions to get a feel for behaviour and customer journeys.

Pros of digital fingerprinting

• Digital finger printing and can provide quite accurate data
• This can take the form of daily updated data which provides rich profile information on behaviour and offers the potential to track across platforms and websites

Cons of digital fingerprinting

• There are very real privacy concerns with this type of approach
• Major browsers like Safari, Chrome and Firefox have already made changes that make the process difficult and limit the ability to do this

6. Using AI and Machine learning to effectively replace cookies

This is an approach we have been taking with our clients for some time.

The reality is, if you are going to have to move away from cookies, your only real option is to look at using a predictive technology, like AI, to replace them.

The driving force for this was a realisation that cookies actually do a pretty poor job of analysing complex customer journeys and result in 80% of data being incorrectly attributed.

The problem with solutions like Google Analytics and Adobe which rely on cookies is that, by virtue of default attribution models like First Click and Last Click, they are firmly focused on the lower end of the conversion funnel – and activity like re-targeting (because that is where they can measure).

However, by using AI and Machine Learning techniques, our own attribution solution, Corvidae, is able to assess the impact of the effectiveness of media much higher up the funnel including the impact of activity like Paid Social and Display.

This means you can swap broken cookie data for AI-driven attribution. And remove ineffective marketing spend and convert new customers for the lowest possible price with Corvidae.

Pros of using AI and Machine Learning

• Provides a true cookieless attribution solution
• Rebuilds your broken marketing data to get a true picture of the impact of media effectiveness right across the customer journey
• Allows you to identify what is and isn’t working in your marketing mix at a channel, campaign and individual creative level
• Futureproofs your marketing attribution analytics

Cons of using AI and Machine Learning

• Investing in AI and Machine Learning can require a shift in mindset within the company, and with marketers in particular. Understanding that your measurement, up until this point, has been fairly inaccurate can comes as a shock for most. So, the move to an AI-driven attribution method can take time to get everyone on board and learn a new way to approach measurement.

Buyer’s Guide to Selecting the Right Attribution Solution

If you are reviewing your options around attribution solutions in light of the cookie changes right now then download a copy of our eBook, Buyer’s Guide to Selecting the Right Attribution Solution, to learn:

• What is attribution and why is it so important?
• Some common misconceptions about attribution technology
• Helpful tips for choosing the right solution for your needs