Behavioral targeting is the practice of choosing which ads or content to show someone based on their past online behavior, such as the pages they visit, the searches they run, the products they view, and the links they click. Instead of relying only on who a person is, it uses what they have actually done to predict what they are likely to want next. AI has sharpened it by finding patterns across large streams of behavioral data in real time.
Also known as behavioral advertising, interest-based advertising, audience-based targeting
Behavioral targeting works by collecting signals about what a user does across websites and apps, then grouping users into segments based on shared patterns, such as recent shoppers for running shoes or frequent readers of finance news. Those segments are matched to ads meant to fit the behavior. The data can be first-party, gathered on a brand’s own properties, or third-party, aggregated across many sites, though privacy rules and the decline of third-party cookies have reshaped what is available.
Machine learning has become central to how it runs. Models score users in real time, predict the likelihood of a click or a purchase, and decide which creative to serve to whom, often within the milliseconds of an ad auction. The result is targeting that adapts continuously as behavior changes, rather than fixed rules a marketer sets once.
For agencies, behavioral targeting is one of the main levers for turning audience data into performance, and one of the main places where privacy and accuracy tradeoffs surface.
It lifts relevance and efficiency. Reaching people based on demonstrated interest, rather than broad demographics, tends to raise click and conversion rates and cut wasted spend, which is often the difference between a campaign that hits its numbers and one that does not.
It depends on data you can actually use. As third-party cookies fade and privacy regulation tightens, agencies increasingly have to build targeting on first-party data and consent, so the quality of a client’s own data collection now shapes how well behavioral targeting can work.
It carries brand and compliance risk. Targeting that feels intrusive, or that leans on data a user did not knowingly share, can erode trust and run afoul of privacy law, so agencies have to weigh precision against how the targeting will be perceived and whether it is compliant.
A retail client wants to cut wasted spend on a prospecting campaign. The agency builds behavioral segments from the client’s first-party data: people who viewed a product but did not buy, people who abandoned a cart, and lapsed customers who have not purchased in ninety days. Each segment gets its own creative and offer, and an AI bidding model shifts spend toward the segments converting best that week. Cart abandoners see a reminder with free shipping, while lapsed customers see a win-back offer. The campaign spends less reaching cold, untargeted audiences and more on people whose behavior signals real intent.
The generative AI foundations module of the workshop covers how today’s models work, what they can and can’t do, and how to choose between them.