30 second summary:

  • After passing landmark laws to protect consumer privacy, Google announced its intention to phase out third-party cookies by 2022
  • Companies that use these cookies for detailed consumer data are now forced to rethink their strategies for targeting specific audiences
  • Some companies are turning to walled gardens by publishers, while others are more focused on contextual advertising
  • Coegi’s Sean Cotton examines the challenges and opportunities marketers face in the absence of third-party cookies and possible alternatives that can help them get to the heart of the target audience

After passing landmark consumer protection laws, Google officially announced its intention to expire third-party cookies in Chrome browsers by next year. This is certainly a victory for the conscious consumer who is wary of selling data to advertisers, but it can also lead to business confusion when the cookie jar disappears. But these companies should be excited rather than alarmed. While the death of third-party cookies is an obstacle, it is also an opportunity: when alternatives to third-party cookies emerge, advertisers may be better equipped with targeting and acquisition methods.

Third-party cookies weren’t always perfect, and their quality largely depended on factors such as the data provider’s methods, the latency and timeliness of that data, and the associated acquisition costs. Although these pre-built audiences were sometimes out of date, advertisers were able to scale their audiences quickly. The impending exit will put pressure on marketers to rethink their targeting strategies.

What are the alternatives to third party cookies?

Publisher Walled Gardens (where publishers trade free content for first-party data) are a solid starting point for advertisers looking for alternatives to third-party cookies. These audiences won’t be cheap, but it will be possible to find publishers with audiences that are highly aligned with your own customer base. Because these data sources are generally authenticated, they are also an accurate source of modeling data that you can use when creating your own user databases.

Many purchases these days begin with online research, so savvy marketers are also looking into contextual advertising as a third-party cookie alternative. By assigning the sales funnel for your product or service, you can identify opportunities for targeted advertising while your audience researches. However, it is important to be precise at the same time. Make sure you use negative search terms and semantic detection to prevent your brand or product from showing up in potentially embarrassing or unsafe placements. (Just think of the word “shot” which could refer to anything these days from COVID-19 to health and wellbeing to debates around the second change.)

There is still time for a smooth transition from your reliance on cookies, but you shouldn’t wait much longer to get started. If you’re looking for new ways to get your message across to a precise audience, these strategies are a good place to start:

1. Rely on third-party data

Third-party data (e.g. data provided in publisher-walled gardens) can quickly target advertisers to replace third-party cookies. This type of data can influence personal or account-based marketing strategies and help you identify people in a specific industry or people with a specific relevant job title. Similarly, integrating third-party data into your broader digital marketing strategy can create use cases for lookalike modeling or provide a solid foundation for sequential messaging.

However, try to work with publishers and vendors long term to keep prices as low as possible as third party data may come at a high cost. As an added benefit, you have time to experiment and use different types of data in different ways.

2. Implement targeting for mobile ad IDs (or MAIDs)

MAID targeting is based on an anonymous identifier assigned to a user’s operating system for mobile devices. MAIDs have always been the first address for targeting applications, as they are data protection compliant and offer an excellent opportunity to segment target groups according to behavior and interests. In fact, everyone expected MAIDs to grow as mobile and in-app usage accelerated. For example, in the United States, mobile users spend just over an hour more each day on these devices than they do on their computers, and they spend 87 percent of the time using their smartphone’s in-app. But the death of third-party cookies will certainly accelerate the use of these audiences across channels even further.

One of the most powerful insights that MAIDs offer is the ability to track a user’s location data. For example, if a device visits an NFL stadium, you can conclude that the user is a soccer fan, which can lead to a variety of other conclusions. You can also enrich MAIDs with offline deterministic data to create a more complete picture of the user, their demographic information and their relevant interests.

Note that recent changes to Apple’s iOS 14 platform may limit this type of targeting on the company’s devices. It is also important to check the precision and accuracy of the provider providing you with location data.

3. Create custom models and indexes

Algorithmic targeting, or lookalike modeling, was poorly received by advertisers who feared the modeled audience would over-target the targeting. However, as the quality of your audience input increases, so does the quality of your modeling output. In other words, you should only have concerns when modeling audiences on modeled data.

On the other hand, models can be a great source of additional insight when using deterministic data. This information comes from all sorts of sources including social media platforms, questionnaires and surveys, and e-commerce websites that provide information about users’ purchase history. In short, it’s data you can trust – that is, it can help create accurate audience segments and models that capture real customer intent. With deterministic data on top, you can build your own models and indexes to aid your targeting efforts.

First-party data from customers and active social media followers are generally the best source for models. However, watch out for outliers when it comes to audience insights. The signals should be strong enough to imply the actual behavior of the target audience.

4. Use Unified ID solutions

The death of third-party cookies does not mean the death of all of your strategies, and you can expect a multitude of sophisticated solutions to emerge in the years to come that provide audience segmentation with improved control for advertisers and improved privacy for consumers. In fact, some companies are already working together to develop Unified ID solutions that modernize target group approach and measurement.

The solutions they create aim to collect user information (like email addresses) in exchange for free content. These addresses are then assigned encrypted IDs that are transmitted to advertisers along the bid stream. When publishers use largely uniform identity products, they offer an excellent alternative to over-reliance on walled gardens.

However, one of the biggest hurdles to a unified ID solution will be scalability: it probably won’t be a solution that can stand on its own for some time.

The death of third-party cookies will absolutely shake up the advertising world, but that’s probably a good thing. Cookies were never designed to be the backbone of digital advertising, and their disappearance leaves room for alternatives to third-party cookies that provide a better experience for advertisers and the audiences they want to target. With advertisers having finer control over who hears their messages (and when) and customer data is protected behind modern encryption and privacy tools, it’s not hard to argue that if we put the cookie jar away, everyone wins.

Sean Cotton is the President and Co-Founder of Coegi.