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- Following the passage of landmark consumer privacy laws, Google announced its intention to phase out third-party cookies by 2022
- Businesses that rely on these cookies for granular consumer data are now forced to rethink their strategies for accurate audience targeting
- Some businesses are turning to publisher walled gardens, while others are leaning more into contextual advertising
- Coegi’s Sean Cotton explores the challenges and opportunities marketers face in the absence of third-party cookies, as well as viable alternatives they can use to keep audience targeting on point
Following the passage of landmark consumer privacy laws, Google officially announced its intention to phase out third-party cookies on Chrome browsers by next year. This is certainly a victory for the conscious consumer wary of selling data to advertisers, but it’s also one that might leave businesses scrambling when the cookie jar disappears. But these businesses should be more excited than alarmed. While the death of third-party cookies is an obstacle, it’s also an opportunity: As alternatives to third-party cookies emerge, advertisers might find themselves better-equipped audience targeting and acquirement methods.
Third-party cookies haven’t always been perfect right out of the oven, and their quality was largely dependent on factors such as the data provider’s methodologies, the latency and recency of that data, and any related acquisition costs. Although occasionally stale, these prebuilt audiences allowed advertisers to quickly scale their audiences. The forthcoming phaseout will put pressure on marketers to rethink their strategies for accurately targeting audiences.
What are the alternatives to third-party cookies?
Publisher walled gardens (in which publishers trade free content for first-party data) are a solid starting point for advertisers seeking alternatives to third-party cookies. These audiences won’t come cheap, but it will be possible to find publishers with audiences that strongly align with your own customer base. And because these sources of data are generally authenticated, they’re also an accurate source of modeling data to use as you construct your own user databases.
Many purchases these days begin with online research, so savvy marketers are also exploring contextual advertising as a third-party cookie alternative. Mapping out the sales funnel for your product or service will help you identify opportunities for targeted advertising as your audience performs research, but it’s important to be precise at the same time. Be sure to use negative search terms and semantic recognition to prevent your brand or product from appearing in potentially embarrassing or unsafe placements. (Just consider the word “shot,” which in this day and age could relate to anything from COVID-19 or health and wellness to debates surrounding the Second Amendment.)
There’s still time for a smooth transition away from your dependency on cookies, but you shouldn’t wait much longer to get started. As you explore new ways to get your message out to precise audiences, these strategies are a great place to start:
1. Lean on second-party data
Second-party data (such as the kind provided on publisher walled gardens) can offer accurate audience targeting for advertisers in a hurry to replace third-party cookies. This type of data can inform people- or account-based marketing strategies, helping you identify individuals in a specific industry or those with a certain relevant job title. Similarly, integrating second-party data with your broader digital marketing strategy can create use cases for lookalike modeling or provide a strong foundation for sequential messaging.
Because second-party data will come at a potentially high cost, however, try to partner with publishers and providers for the long term to keep rates as low as possible. As an added benefit, this will give you time to experiment and use various types of data in different ways.
2. Implement mobile ad ID (or MAID) targeting
MAID targeting is based on an anonymous identifier associated with a user’s mobile device operating system. MAIDs have always been the go-to for application targeting because they’re privacy-compliant and serve as a great way to segment audiences based on behaviors and interests. In fact, everyone expected MAIDs to grow as mobile and in-app usage has accelerated. In the U.S., for instance, mobile users spend just over an hour more on those devices than their computers each day, and they spend 87 percent of the time on their smartphones in-app. But the death of third-party cookies will certainly accelerate the usage of these audiences across channels even more.
One of the most powerful insights offered by MAIDs is the ability to track a user’s location data. If a device is frequenting an NFL stadium, for example, you can infer that the user is a football fan, which allows a host of other inferences to form. You can also enrich MAIDs with offline deterministic data, allowing you to construct a more complete picture of the user, their demographic information, and their relevant interests.
Note that recent changes to Apple’s iOS 14 platform might limit this type of targeting on the company’s devices. Besides this, it’s also important to verify the precision and accuracy of the provider giving you location data.
3. Build custom models and indexes
Algorithmic targeting or lookalike modeling caught a bad rap from advertisers who worried the modeled audiences would broaden targeting too far. But as the quality of your audience input increases, the quality of your modeling output increases as well. In other words, concerns are justified only if you’re modeling audiences after modeled data.
On the other hand, models can be an excellent source of additional insight if you’re using deterministic data. This information comes from all kinds of sources, including social media platforms, questionnaires and surveys, and e-commerce sites that have information on user purchase history. In short, it’s data you can trust — meaning it can inform the creation of accurate audience segments and models that capture real customer intent. With deterministic data at the helm, you can create your own models and indexes to aid in your targeting efforts.
First-party data from customers and active social media followers generally provides the best source for models. Be aware of outliers when it comes to audience insights, though; signals should be strong enough to imply the target audience’s actual behavior.
4. Use Unified ID solutions
The death of third-party cookies doesn’t mean the death of all your strategies, and you can expect to see a variety of sophisticated solutions emerge in the coming years that offer audience segmentation with increased control for advertisers and enhanced privacy protections for consumers. In fact, some companies are already working collaboratively to create Unified ID solutions that modernize audience targeting and measurement.
The solutions they’re creating aim to collect user information (such as email addresses) in exchange for free content. Those addresses will then be assigned encrypted IDs that are transmitted along the bid stream to advertisers. If publishers widely adopt unified identity products, they’ll provide an excellent alternative to an overreliance on walled gardens.
However, one of the biggest hurdles for a unified ID solution will be scalability: It will likely not 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 makes room for alternatives to third-party cookies that actually deliver a better experience for advertisers and the audiences they’re looking to target. As advertisers gain more granular control over who hears their messaging (and when) and customer data is ensconced behind modern encryption and privacy protection tools, it’s not hard to argue that everyone wins when we put away the cookie jar.
Sean Cotton is the president and co-founder of Coegi.