Contextual Advertising—Now Driven by AI and Machine Learning—Requires Regulatory Review for Privacy and Marketing Fairness

Contextual Advertising—Now Driven by AI and Machine Learning—Requires Regulatory Review for Privacy and Marketing Fairness

What’s known as contextual advertising is receiving a big boost from marketers and some policymakers, who claim that it provides a more privacy-friendly alternative to the dominant global surveillance-based “behavioral” marketing model. Google’s plans to eliminate cookies and other third-party trackers used for much of online ad delivery are also spurring greater interest in contextual marketing, which is being touted especially as safe for children.

Until several years ago, contextual ads meant that you would see an ad based on the content of the page you were on—so there might be ads for restaurants on web pages about food, or cars would be pitched if you were reading about road trips. The ad tech involved was basic: keywords found on the page would help trigger an ad.

Today’s version of what’s called “contextual intelligence (link is external), “Contextual 2.0 (link is external),” or Google’s “Advanced Contextual (link is external)” is distinct. Contextual marketing uses artificial intelligence (AI (link is external)) and machine learning technologies, including computer vision and natural language processing, to provide “targeting precision.” AI-based techniques, the industry explains, allow marketers to read “between the lines” of online content. Contextual advertising is now capable of comprehending “the holistic and subtle meaning of all text and imagery,” enabling predictions and decisions on ad design and placement by “leveraging deep neural (link is external) networks” and “proprietary data sets.” AI is used to decipher the meaning of visuals “on a massive scale, enabling advertisers to create much more sophisticated links between the content and the advertising.” Computer vision (link is external) technologies identify every visual element, and “natural language processing” minutely classifies all the concepts found on each page. Millions of “rules (link is external)” are applied in an instant, using software that helps advertisers take advantage of the “multiple meanings” that may be found on a page.

For example, one leading contextual marketing company, GumGum (link is external), explains that its “Verity” algorithmic and AI-based service “combines natural language processing with computer vision technology to execute a multi-layered reading process. First, it finds the meat of the article on the page, which means differentiating it from any sidebar and header ads. Next, it parses the body text, headlines, image captions with natural language processing; at the same time, it uses computer vision to parse the main visuals.… [and then] blends its textual and visual analysis into one cohesive report, which it then sends off to an adserver,” which determines whether “Verity’s report on a given page matches its advertisers campaign criteria.”

Machine learning also enables contextual intelligence services to make predictions about the best ways to structure and place marketing content, taking advantage of real-time events and the ways consumers interact with content. It enables segmentation of audience targets to be fine-tuned. It also incorporates a number of traditional behavioral marketing concepts, gathering a range of data “signals (link is external)” that ensure more effecting targeting. There are advanced measurement (link is external) technologies; custom methods to influence what marketers term our “customer journey,” structuring ad-buying in similar ways to behavioral, data-driven approaches, as “bids” are made to target—and retarget—the most desirable people. And, of course, once the contextual ad “works” and people interact with it, additional personal and other information is then gathered.

Contextual advertising, estimated to generate (link is external) $412 billion in spending by 2025, requires a thorough review by the FTC and data regulators. Regulators, privacy advocates and others must carefully examine how the AI and machine-learning marketing systems operate, including for Contextual 2.0. We should not accept marketers’ claims that it is innocuous and privacy-appropriate. We need to pull back the digital curtain and carefully examine the data and impact of contextual systems.