The Most Overlooked Cohort Strategy in Advertising
Consumer-privacy has become the centerpiece of digital advertising, as mainstream understanding of data collection and regulatory scrutiny has pushed the industry to an inflection point. The rate of technological evolution has made the need for action critical, ensuring data protection is optimized for the future of advertising. It’s these forces driven by consumers and regulators that have forced big tech giants like Google and Facebook to develop new data collection and advertising targeting solutions that put privacy first, which was not always the case.
Now, with the inevitable death of third-party cookies, marketers seek AdTech that aligns with developing regulations while actually delivering results for their businesses. One such regulation is GDPR, which is a component of EU privacy and human rights law. The general consensus is that these developments are essential to protect internet users, but the changes create a massive challenge for marketers still reliant on third-party data and programmatic advertising.
Most major web browsers such as Apple Safari and Mozilla Firefox took action to block third-party cookies years ago, but Google Chrome kicked the can down the line to 2023. With 60% of the global web browser market share, Google’s actions have a massive role in the direction of the industry. Moving just as fast as Google is IBM, with AI technology focused on targeting and advertising to users based on first-party data that exhibits their shared behaviors.
Google’s first attempt at a solution was Federated Learning of Cohorts (FloC), which aimed to preserve user privacy by masking individuals in a crowd of users. This new method of tracking consumer behavior was meant to ensure advertising relevance for consumers while hiding individual user information from advertisers. The solution garnered rapid backlash, as concerns that their behavioral cohorts contain enough entropy to pinpoint a unique user’s footprint. At the same time, behavioral cohorts open the door to discrimination and the degradation of civil liberties.
The justifiable backlash has officially forced Google to abandon FloC and attempt to replace cookie tracking with a new product called Topics API and FLEDGE. Another attempt at replacing cookies, FLEDGE makes ad auction decisions in the browser as opposed to at the ad server level. In theory this will lead to less user data being harvested for the purpose of building user profiles. While this move makes sense given the vulnerabilities of FloC, there are effective and secure cohort solutions that are not behavioral, but instead built around engagement with keywords.
Cohort-based advertising is carried out by targeting anonymized segments of internet users united by a common criterion. An example of such criteria would be “All customers that purchased an iPhone in 2021” or “Every customer acquired through last month’s PPC campaign.” Cohorts deliver insights that can improve short-term marketing efforts, as behaviors change on a daily basis, and help group users into baskets of shared characteristics.
A common misconception around cohorts is the false comparison to general demographics such as age, gender, and income. In actuality, cohorts separate groups of people within their demographic, and instead sort by shared life events and time. This is where personalization comes in, enabling advertisers to create tailored campaigns based on these events and specific moments in time.
This functionality has led many marketers to explore cohorts as a way to group consumers based on their behavior, which is easily classified by events and moments in time. This changes the nature of audience targeting, moving away from the need of uncovering needles in a haystack. Using cohorts, as data is collected and more campaigns are deployed, the accuracy increases.
We are still in a very experimental phase when it comes to advertising cohorts. Marketers are beginning to recognize where to utilize this approach and where it falls short. The early focus on behavioral cohorts seemed to have some promise, but now the cracks are becoming apparent. This doesn’t mean that cohorts should be written off, as there are ways to re-conceptualize cohorts and deliver real results in alignment with GDPR and CCPA.
Limitations of Behavioral Cohorts
While advertising cohorts are still relatively new, the shortcomings of behavioral cohorts are becoming abundantly clear. Experienced marketers and advertisers are witnessing the downsides of utilizing this type of approach, and how it is leading to real ethical concerns.
Broad Audience Generalizations
Behavioral cohorts are a form of aggregated data, failing to provide the granularity that 1:1 targeting can. This fact leads to concerns around generalizations, as the data collected can only provide a broad-brush stroke when it comes to pinpointing performance results related to specific audiences. Broad pieces of information aren’t as helpful when reviewing performance and assembling marketing strategies.
Assembling groups based on behaviors can lead to the generalization of an audience and reduce the potency of an advertising campaign. As data is collected and campaign optimization is carried out with new cohort audiences factored in, it is possible to trend in the wrong direction based on assumptions being made.
Using certain types of behavioral cohorts opens the door to the possibility of browser fingerprinting. This practice involves the collection of many small pieces of information that might seem discrete individually, but when combined create a unique identifier for that user. As a user’s browser begins to differentiate itself from others through their actions, it becomes easier to fingerprint.
Fingerprinting is one of the primary push backs that caused Google to abandon FloC, despite their initial plans. In theory, as a user is grouped into a cohort with thousands of others, the cohort ID doesn’t allow for a single user to be distinguished from the pack. However, if trackers start with a cohort ID, there are only a few thousand other browsers for it to move through to recognize a unique identifier. This privacy issue is notoriously difficult to defend against, with major browsers spending years working to reduce the potential threat. One of the ways to reduce the risk of fingerprinting is to remove sources of entropy, which is what a behavioral cohort is.
The Degradation of Civil Liberties
The fundamental concept of targeting can lead to discrimination if not carried out in an unbiased way. Biases are inherent in most processes, and behavioral cohorts have the potential to deepen these biases and work at odds with civil liberties. Targeting users based on ethnicity, gender, or age can lead to discriminatory ads for jobs. Targeting based on location or demographics can help push disinformation or suppression of ideas. Behavioral targeting as a whole can increase the likelihood of scams, as they become more convincing when highly targeted.
Behavioral cohorts are algorithmically assembled, providing no control over the way in which individuals are grouped. While the aim is to connect individuals based on their interests, online behavior is inherently connected to personal characteristics such as gender, age and income. This makes it nearly impossible for behavioral cohorts to avoid grouping users based on this sensitive data.
Google initially proposed a solution to this problem by monitoring system outputs for relationships with categories deemed sensitive, and subsequently adjusting the algorithmic parameters that determine groupings. This proposal only makes behavioral cohorts worse, requiring data audits focused on race, gender, health and financial status. Doing so would require constant reconfiguration of their algorithm in hopes of avoiding additional sensitive information. The problem here is that this solution becomes another version of the original problem they are trying to solve.
All of this just opens the door to discrimination for those with additional information on users. This information is relatively easy to obtain through dedicated examination and experimentation. In the end, it will become harder for platforms to monitor these breaches of civil liberties and advertisers will have plausible deniability.
Power of Keyword Cohorts
Most view advertising cohorts as a singular solution that is always based on behavioral data. The reality is that innovative new developments are being made utilizing the cohort approach without being forced to lean on behavioral data.
Putting Keywords First
While behavioral cohorts group users based on their actions online, keyword cohorts are focused on grouping engagement with specific keywords. The difference being that the underlying algorithm is fed specific data on where to focus across sites and platforms, regardless of the user’s behavior beyond that data. This means that an auto company can deploy targeted campaigns based on the keyword engagement of “vehicle safety” or “automatic cars”, enabling a more precise and private system of targeting.
Keyword cohorts make it possible to group keywords around intent to purchase, with no visibility into user’s sensitive information. If a user engages with a specific keyword at a specific time, the algorithm can take note and actually group anonymized individuals based on their interests. This keyword-first approach is transformative for businesses in considered purchase path industries, as data over time allows for optimization and improvement of targeting all without privacy-overreach.
Avoiding specific keywords ensures that only non-sensitive information is being used for cohort groupings. While engagement with a single keyword doesn’t provide the clearest picture of an audience, clarity forms as soon as keywords are clustered.
The magic begins to happen when keywords are clustered together for the purposes of advertising campaigns. Through this process, it becomes easier to refine cohorts while still remaining completely private. As users engage with multiple keywords, their cohort groupings become more nuanced. Given that all of the keywords being tracked are pre-determined, there are no concerns about sensitive information entering the mix.
Let’s take for example a user that is on the path of purchasing a new car. The automotive company that aims to convert that user can pinpoint the top 25 keywords most likely to demonstrate interest in their unique offering. Engagement with “best family cars” doesn’t demonstrate much intent, however as soon as you begin to cluster additional keywords based on their engagement like, “vehicle safety”, “used cars”, “Chicago car dealerships”, the intent to purchase becomes more obvious.
All of this delivers accuracy around what groups of users are interested in, and what point on the path to purchase they are at. This is how keyword cohorts flip the script by grouping keywords first, and then tracking engagement with those keywords instead of tracking user’s behavior in a broader sense.
Mapping the Open Internet
In order to deliver keyword cohorts that continually provide results, ReverseAds is constantly mapping the millions of pages on the open internet. Most businesses don’t consider this expanding ecosystem as a viable advertising opportunity because it is so sprawling. 1.2 billion websites exist across the open internet, and 175 new ones are created every minute. This represents a massive resource for keyword data, and one that is continually expanding.
The keyword data collected across the open internet is extremely relevant for businesses. This is because 61% of internet users start their search for products/services on the open internet. The keywords being engaged with here are valuable for that reason. By data-mining all of these sites for keywords, ReverseAds is able to constantly pull and classify the most relevant keywords that demonstrate intent, and cluster them in ways that are aligned with how they are being used across websites.
Every web page is crawled to pinpoint the keywords related to a product or service being considered. As keywords accumulate, a buyer’s profile begins to take shape and connections are made. The data that we collect is used to fuel our keyword cohort, and predict purchasing decisions based on engagement. Crawling the open internet and using AI to match keywords related to a range of content hosted on websites allows us to better understand which keywords drive specific purchasing decisions. This is the connecting of dots to accurately predict where users are heading without sacrificing privacy.
A Shifting Perspective
We’re at an industry turning point, as many AdTech platforms explore targeting methods that maintain privacy and deliver the right ads to the right audience. It’s easy to get distracted by Google’s 180 on FloC. The pushback received on their behavioral cohorts should not be a reflection of the power that cohorts hold overall. This technology still represents real potential for businesses that understand how to utilize it. Focusing on keyword cohorts instead of behavioral cohorts shifts the spotlight from users to the content they engage with, thus improving privacy and performance.
ReverseAds is at the forefront of this industry shift, providing an alternative to behavioral cohorts. We deliver access to this through an app purpose-built for the cookie-less future. Our technology is addressing the need for privacy-forward solutions that deliver new advertising experiences across the open internet.