What Is Behavioral Targeting and How Does It Work?

Publishers and advertisers have relied on behavioral data for years to improve the relevance and effectiveness of the ads and marketing messages they serve to online audiences. Understanding how that process actually works — and how regulation is reshaping it — is essential context for anyone working in digital advertising today.

This guide covers what behavioral targeting is, how it works mechanically, how it differs from contextual targeting, and what the General Data Protection Regulation (GDPR) means for its future.

What Is Behavioral Targeting?

Behavioral targeting (also known as online behavioral advertising) is a method that allows advertisers and publishers to display relevant ads and marketing messages to users based on their web-browsing behaviour. This form of targeting typically relies on data tied to a user's demonstrated behaviour, such as:

• Pages viewed
• Previous search terms
• Amount of time spent on a website
• Ads, content, and buttons clicked
• Last date of website visit
• Other information about their interactions with the website

What's the difference between behavioral targeting and online behavioral advertising (OBA)?

Online behavioral advertising (OBA) is a form of online advertising that uses behavioral targeting to display certain ads and personalized content (e.g., product recommendations) to users. In practical terms, OBA is the type of advertising while behavioral targeting is the technique behind it.

Types of Behavioral Targeting

There are two main types of behavioral targeting: onsite and network.

Onsite Behavioral Targeting

Onsite behavioral targeting happens within a particular site and is typically implemented as part of website personalization. Ads are displayed to the user based on behavioural data and/or other information about the visitor gathered on the pages of the same site. This method plays a significant role in building a more streamlined and relevant experience for users.

Onsite behavioral targeting allows advertisers and publishers to surface relevant content, recommended products, and promotions to users visiting the site. As a result, users are more willing to engage, spend more time browsing, and convert — that is, perform a desired action like making a purchase or signing up for a newsletter.

Network Behavioral Targeting

In behavioral advertising broadly, implied choices are made for the audience. That audience is categorized based on factors like purchase intent and interests, derived from demonstrated web behaviour. Usually, data is collected only on the advertiser's own site and used for retargeting and personalization.

In network behavioral targeting, data can be collected and shared across multiple sites on the internet. This doesn't include personal identifiers like names, email addresses, or telephone numbers, but may involve device-identifying information such as IP and MAC addresses, cookies, or other device-specific IDs. Algorithms process this data and assign users to specific segments, inferring attributes like age, gender, and likely purchase decisions in order to serve customized ads the person would be more likely to click.

A straightforward example: a user seen across a number of automotive sites, business publications, and men's fashion sites would reasonably be inferred as male and business-oriented. Advertisers can buy that segment — typically provided by data management platforms (DMPs) that are integrated with and sell to demand-side platforms (DSPs) — to reach the relevant audience.

How Behavioral Targeting Works

Behavioral targeting consists of collecting information about a visitor and delivering relevant ads that match that person's profile. Gathering behavioral data — a prerequisite for effective behaviorally targeted advertising — can be done in multiple ways, but a DMP is generally required to aggregate behavioral data about a site's visitors. DMPs are responsible for collecting, storing, and organizing that data for advertisers.

The data used for behavioral targeting can come from a range of sources, including websites, mobile apps, CRM systems, and other marketing-automation systems, and can include:

• User login information (for registered users)
• IP address and geolocation
• Pages (or products) viewed on the site
• Duration of visit
• Clicks
• Recency of visit
• Interaction with on-page elements
• Previous purchases
• Demographics
• Content read
• Sections of the page regularly visited
• Searches within the site
• Websites previously visited

Nearly all advertisers and publishers can gather some onsite data on their customers and visitors. Knowing how to leverage that information toward specific marketing goals is what separates effective behavioral targeting from basic data collection.

The Behavioral Targeting Process

1. Collection and Analysis of Data

User data is collected from a range of sources, but is typically gathered via tracking pixels (also known as third-party cookies) and stored in a DMP or another AdTech platform such as a DSP. The more data available, the more accurate the targeting. The collected data is then analyzed and used to create user segments.

2. Segmentation

Users are clustered into segments by behaviour — for example, frequent travellers, cycling enthusiasts, or people who repeatedly return to the same product category.

3. Application of Data

Ad campaigns are built to match specific user segments, making advertising more relevant to particular groups and increasing the likelihood of conversions and responses.

Beyond data collected by DMPs and AdTech platforms, behavioral targeting can be enriched with data pulled from registered user profiles.

Registered users who make purchases in an online store generate transaction data. Those purchases, combined with site-navigation history, are often stored and analyzed to surface targeted offers the next time the user visits.

Unregistered users can be targeted using cookie information saved in the browser. When the user revisits the site, the cookie (unless deleted) is sent back to the web server, enabling the site to recognize and target that user.

There is also a method of collecting and tracking data through internet service providers (ISPs). ISPs can perform deep packet inspection (DPI) to analyze customer traffic and determine the types of websites their subscribers visit. That data is then sold to marketing and ad-serving companies to enable more personalized ad delivery. This is a common practice: many ad-serving companies purchase behavioral data from third-party data brokers such as Nielsen (formerly eXelate) and dataLogix (now part of Oracle).

Behavioral Targeting vs. Contextual Targeting

These two targeting approaches are frequently compared, and the distinction matters — particularly in a privacy-constrained environment.

Contextual targeting involves displaying ads that are relevant to the content of the page being viewed. This method typically doesn't use information about individual users; it relies solely on the context of the ad placement. That said, behavioral data can be layered in to improve the relevance of contextual ads where that data is available.

Behavioral targeting allows advertisers and marketers to target individual users directly. The underlying premise is that the ad should be relevant not to the page, but to the specific person visiting it. Behavioral targeting has been widely used in online advertising and marketing for well over a decade, driven by the growing availability of user data.

For behavioral targeting to be effective, there must be sufficient information available about the user. Thin data leads to imprecise segmentation, which undermines the core value proposition.

Benefits of Behavioral Targeting

The volume of data available to marketers enables them to build detailed user profiles and serve ads tailored to each segment. The premise is that behavioral targeting benefits the visitor as much as it benefits the publisher — users are exposed to more relevant content, and publishers see stronger engagement metrics.

When implemented well, behavioral targeting tends to produce high click-through rates (CTRs) and better conversion rates, offering a measurable return on investment. Today, the discipline is not just about capturing explicit user information but about extracting meaningful signals from data and drawing accurate inferences from behaviour patterns.

Behavioral Targeting and the GDPR

With the General Data Protection Regulation (GDPR) in effect across Europe, behavioral targeting faces stricter rules around cookie storage and user consent. The GDPR may compel marketers to reduce their reliance on behavioral data and pursue alternative targeting approaches.

For many, the answer lies in contextual advertising. The principal advantage of contextual targeting in a post-GDPR environment is its built-in compliance: because it minimizes reliance on personal data, companies can deploy contextual methods while largely sidestepping GDPR obligations — provided they avoid collecting or using personal data entirely.

Several companies have already moved in this direction. AccuWeather, for example, partnered with Comprendi, a contextual advertising automation company, to provide real-time ad-personalization algorithms. By drawing on data tied to allergies, migraines, driving conditions, and lawn-and-garden forecasts, advertisers working with AccuWeather can serve ads to specific audiences based on contextual signals that correlate with purchase intent (e.g., gardening supplies, rain gear, outdoor recreation).

Quora offers another example, providing GDPR-safe contextual targeting options based either on the specific questions users are reading answers to, or on defined topic areas.

Approaches like these are likely to become more sophisticated over time. It's also worth noting that the GDPR applies specifically to EU residents and citizens. Behavioral targeting remains fully permitted in other parts of the world and continues to be used extensively by major players in the online marketing industry, including Facebook and Google.