✍️ Column by Masa Sweidan, our Business Development Manager.
The emergence of new financial models in the digital space, ranging from subscriptions to sponsored content, continues to shape much of our experience with major tech companies and platforms. In particular, advertising technology now fuels many of the free services we use and has fundamentally disrupted the way in which companies develop their financial sustainability.
This is an interesting time to be examining the use of ad tech, as Google announced last week that they will phase out technology in its Chrome browser that allows other companies to track users’ web browsing, after they made a promise last year to end support for third-party cookies by early 2022. Let’s put this in context and take a look at the origins, developments and impacts of ad tech that have led us to this point.
When Sheryl Sandberg joined Google in 2001, it was still a private company without a solid business model. However, she soon started developing their two main advertising services: AdWords and AdSense. AdWords was designed to help advertisers create and deliver ads that were targeted to search queries, while AdSense was designed to help online publishers monetize their content by showing AdWords that were relevant to search queries or content on that page.
To get a sense of these two services’ significance, consider the fact that in 2018, 70.7% of Google’s revenue came from advertising through Google properties, which included its search platform and traffic generated by search distribution partners who use Google.com as their default. Today, most ad impressions online go to Google and Facebook, and the growth in ad revenue has mainly been driven by algorithms.
These companies have collected unimaginable amounts of data about each of us in order to build up extensive individual profiles and customize their ads. According to Siva Vaidhyanathan, we are experiencing the development of the attention economy. At the core of this theory is the idea that time is the most valuable commodity, for the longer a person spends on your website, the more ads you can show and the more profit you can make.
The attention economy emerged during the late 1990s, when the main defining component was that online information should be free. At that time, “free” meant digital content could be cost-free and shareable. This led to a collective feeling of technological optimism, for the internet was empowering individuals in a way that it never had before. But how would the digital companies make revenue to pay for their operational expenses? The answer was (and still is): advertising.
This is particularly relevant today, as the media ecosystem becomes more polluted and companies try to find innovative ways to capture and hold our attention. This is where AI enters the picture. Powerful algorithms can now be used to efficiently sift through data pertaining to our behaviour and preferences, with the intent of displaying unique ads that are deemed to be highly relevant to each and every one of us. AI has enabled this data-driven approach that has upended the advertising industry, which used to be based on a certain level of faith that a particular message will somehow reach the proper audience.
Consequently, the Consumer Decision Journey (CDJ) in the digital era diverges significantly from previous journeys that consumers have undertaken in the past. Today’s consumers take a much more iterative journey of four stages, including:
- Enjoy, advocate, bond
The majority of ads and promotions hit the Consider and Buy stages, but it has been shown that consumers are often influenced more during the Evaluate and Enjoy, Advocate, Bond stages. Since machine-learning algorithms are now able to make personalized recommendations at all stages of the CDJ, new brand information can pop up during any of these consumer touchpoints. However, the use of invasive ad tech by major companies like Facebook and Google began impacting not only brand marketing, but also journalism, financial services and online job listings, to name a few.
This raises an important concern. Although AI in ad tech has enabled companies to reach their optimal target market, it has also reinforced biases and exacerbated discriminatory outcomes for individuals who have been historically underrepresented. These practices have therefore flagged issues pertaining to fairness, whether it be bias detection, privacy, or diversity, in algorithmic decision-making.
What are the incentives and rewards being put in place to apply responsible AI practices in the development of ad tech? With Google and Facebook capturing the majority of ad revenue in the market, consequently leading them to be amongst the most profitable companies in history, firms must critically think about the trade off between achieving a competitive advantage with micro-targeted online advertising and utilizing overly intrusive ad tech that will undoubtedly ring alarm bells.
There should be no surprise that this discussion around ad tech and AI is inherently linked to privacy concerns. The abyss between what we know and what can be known about us is currently growing, creating “unprecedented asymmetries in knowledge.” According to Shoshana Zuboff, surveillance capitalism begins with the secret theft of private human experience, which is now declared as free raw material, and then translated into behavioural data to be conveyed to AI that predict what we will do in the future. It is ironic that these predictions are about us, but they are not necessarily for us. Rather, they are sold to businesses, as promises of certainty, in a new kind of “market that trades exclusively in human futures.”
In essence, surveillance capitalism is a human-made economic logic, where the ultimate goal for businesses is to make profit, and ad tech seems to be the ideal vehicle to achieve this result. Google’s most recent decision is a move that proves their attempt to balance consumer privacy while maintaining the personalization of ads. Their alternative method, which is part of the Privacy Sandbox project, would use an algorithm to group people according to their common web browsing, allowing individuals to remain unidentified.
With this technology, businesses can target ads to clusters of consumers, and label them using “cohort IDs.” In light of this new announcement, many are still skeptical (and rightly so) about the real impact of this change. At the surface, it may seem to be a step forward for user privacy, but it does not mean that Google will stop collecting your data. Yes, they will stop selling web ads targeted to individual users’ browsing habits, but they will still be able to target ads to users based on their behavior on Google’s own platforms, which make up most of their revenue anyways.
There is no question that this announcement will affect the entire digital ad industry, but one of the biggest challenges will be finding ways to hold these major companies accountable. As Alice Xiang put it at her talk during the RE•WORK Deep Learning 2.0 Summit, “how do we design AI Ethics guidelines that are flexible enough to deal with diverse contexts but strict enough to be enforceable?” Since ad tech can be used across a vast number of industries, finding the answer to this overarching question will be especially important for the future of this evolving digital business model.