What Is Behavioral Targeting and How Can You Use It?

How Does Behavioral Targeting Work?

Behavioral targeting means diving deep into online activity to deliver personalized advertising. Companies study browsing behavior, search queries, and past purchases to align ads with user preferences.

It relies on sophisticated data analytics to interpret user behavior and predict future actions. The primary goal is to make advertising more relevant and increase the likelihood of user engagement. Unlike demographic targeting, which focuses on static user attributes like age, gender, or location, behavioral targeting is dynamic and adapts to the user's ongoing interactions.

The Types of Data Used for Behavioral Targeting

There are two main types of data used in behavioral targeting: first-party and third-party data. First-party data is information collected directly by the company from its own audience, such as website analytics, app usage data, or customer loyalty information. This data is considered highly valuable because it is unique to the company and reflects direct engagement with its brand.

On the other hand, third-party data is collected by external organizations and aggregated from various sources. This data can be purchased or accessed through partnerships and provides broader insights into user behavior across the internet. Third-party cookies have traditionally been a primary means of collecting such data, but with increasing privacy concerns and regulatory changes, their use is declining. However, as Google retains cookies, marketers need to stay informed about the implications for data collection strategies.

How Do Companies Implement Behavioral Targeting?

To implement behavioral targeting, companies use technologies such as cookies, pixels, and device fingerprinting. Cookies are small files stored on a user's device that track interactions with websites. They help in understanding user preferences, login details, and shopping cart contents. Pixels, also known as tracking pixels, are tiny invisible images embedded in web pages or emails that notify the server when the content is accessed. Device fingerprinting collects information about a user's device configuration to create a unique identifier without relying on cookies.

Machine learning and artificial intelligence play an increasingly significant role in behavioral targeting. Algorithms analyze vast amounts of data to identify patterns and segment users based on their behavior, illustrating how AI makes targeting more efficient. These models can predict future actions, such as the likelihood of making a purchase or responding to a specific type of advertisement. Real-time bidding (RTB) platforms use this information to serve ads almost instantaneously as users navigate the web.

However, behavioral targeting must balance effectiveness with privacy considerations. Regulatory frameworks like the General Data Protection Regulation (GDPR) in the European Union and the California Consumer Privacy Act (CCPA) in the United States impose strict guidelines on how personal data can be collected and used. Companies must obtain explicit consent from users before tracking their behavior and must provide options for users to opt out.

In response to these challenges, contextual targeting is gaining popularity as an alternative or complement to behavioral targeting. Rather than focusing on user behavior, contextual targeting places ads based on the content of the webpage being viewed. This approach respects user privacy while still aiming for relevance.

Behavioral Targeting: Step-By-Step Breakdown

1. Data Collection

Marketers collect information through cookies and other tracking methods. This includes browsing patterns, search queries, and time spent on sites. The more data you have, the better you can understand your audience.

Data collection is the foundation of behavioral targeting. It involves tracking various user activities across digital channels. Common data points include:

• Page Views: Recording which pages a user visits helps build a profile of their interests.
• Click Paths: Analyzing the sequence of clicks provides insights into user navigation and engagement.
• Time Spent: Measuring how long a user stays on a page indicates the level of interest or engagement with the content.
• Interaction Events: Tracking specific actions like video plays, form submissions, or social shares adds depth to the user profile.
• Purchase History: Past buying behavior is a strong predictor of future purchases, especially when combined with other data points.
• Search Queries: Understanding what users are searching for reveals their immediate needs and intentions.

As users interact with websites and apps, this data accumulates to create a comprehensive behavioral profile.

2. Segmentation

Users are grouped based on shared habits and interests. Categorizing people into segments allows advertisers to tailor ads that grab each group's attention.

Segmentation involves dividing the broad audience into smaller groups with shared characteristics. Effective segmentation strategies include:

• Behavioral Segments: Grouping users based on actions such as pages visited, frequency of visits, and types of content consumed.
• Purchase Intent: Segregating users who exhibit behaviors indicating they are close to making a purchase.
• Engagement Level: Separating highly engaged users from those who are less active to tailor appropriate messaging.
• Lifecycle Stage: Identifying where users are in the customer journey—new visitors, returning visitors, or loyal customers.
• Propensity Models: Using predictive analytics to estimate the likelihood of specific actions like conversion or churn.

3. Targeting

With these segments in hand, advertisers craft campaigns that address each group's specific needs, often using AI-powered targeting to improve precision. Key aspects include:

• Personalized Messaging: Crafting ad content that resonates with the specific interests and needs of each segment.
• Dynamic Creative Optimization (DCO): Using technology to automatically assemble and serve customized ad creative based on user data.
• Cross-Channel Consistency: Ensuring that messaging is coherent across different platforms such as web, mobile, and email.
• Timing and Frequency: Determining the optimal moments to serve ads to maximize engagement while avoiding ad fatigue.
• Location-Based Targeting: Incorporating geographical data to deliver regionally relevant content.

4. Delivery of Customized Content

Personalized ads reach users at the right moments, and boost engagement and conversion rates. For instance, a skincare brand might pair items like moisturizer and sunscreen in targeted campaigns to encourage combined purchases. An online retailer could see steady revenue growth by using retargeting ads to re-engage interested shoppers. 

The final step is the execution of the campaign—serving the personalized content to users. This involves:

• Ad Platforms and Networks: Utilizing platforms that support behavioral targeting.
• Real-Time Bidding (RTB): Participating in auctions to purchase ad impressions targeted to specific users or segments.
• Retargeting Campaigns: Re-engaging users who have previously interacted with the brand but did not convert, reminding them of products or promotions.
• A/B Testing and Optimization: Continuously testing different ad variations to identify what resonates best with each segment.
• Performance Tracking: Monitoring key metrics like click-through rates (CTR), conversion rates, and return on ad spend (ROAS).

Behavioral Targeting on Google

Google Ads uses behavioral targeting through search history, YouTube activity, and interactions with websites across the Google Display Network (GDN). By analyzing browsing habits, past searches, and even engagement with video content, Google helps you serve highly relevant ads at the perfect moment.

Behavioral targeting options on Google Ads. Source: 39 Celsius

Here’s how you can tap into behavioral targeting on Google:

• Custom audiences – Define your ideal customer based on their search queries, visited websites, and app interactions.
• Remarketing – Re-engage users who have already interacted with your brand.
• Predictive audiences – Google’s AI predicts user intent based on past behavior, allowing you to target users likely to convert even before they actively search for your product.
• In-market segments – Google identifies users actively researching products in your category.

Behavioral Targeting on Meta

Meta’s advertising platform gathers insights from user activity across its apps, including likes, follows, comments, shares, and interactions with ads and shopping features.

Behavioral targeting options on Facebook. Source: Jon Loomer Digital

Here’s how you can use behavioral targeting on Meta:

• Engagement-based targeting – Serve ads to users who have interacted with your brand’s Facebook or Instagram content, from liking a post to watching a video.
• Retargeting website visitors – Use the Meta Pixel to track user activity on your site and re-engage them with customized ads that reflect their browsing behavior.
• Lookalike audiences – Reach new customers who share behaviors and interests with your best existing customers.
• Interest and behavioral segmentation – Meta categorizes users based on their online and in-app behavior, allowing you to target audiences who engage with specific content types, from fashion enthusiasts to tech early adopters.
• Shopping and purchase behavior – Meta tracks users who engage with Facebook Shops or Instagram Shopping.

What are the Benefits of Behavioral Targeting?

Targeting customers based on their behavior helps with personalization, conversions, and ad spend.

Personalization

With behavioral targeting, you analyze browsing history and purchase patterns to send messages that really resonate. This leads to better engagement and deeper loyalty. Considering personalization is table-stakes for modern customer engagement, ignoring it means you're leaving money on the table.

Personalization in behavioral targeting goes beyond merely addressing customers by their first names. It involves creating a unique experience tailored to each user's preferences and behaviors, which includes:

• Product Recommendations: Suggesting items that a user is likely to be interested in based on their browsing and purchase history.
• Customized Content: Altering website content dynamically to match user interests.
• Predictive Personalization: Using machine learning algorithms to anticipate user needs before they explicitly express them.
• Contextual Messaging: Delivering messages that align with the user's current context, such as time of day, location, or weather conditions.
• Personalized Offers and Promotions: Providing special deals tailored to individual purchasing habits, increasing the likelihood of conversion.

Plus, personalization can reduce customer churn because they feel understood and valued, so they're less likely to switch to competitors. This is particularly important in saturated markets.

Improved Conversion Rates

Tailoring content based on user behavior can significantly improve conversion rates, especially with retargeting tactics aimed at shoppers already familiar with your brand. Companies can refine marketing strategies through data insights that spot user groups most likely to purchase.

Improved conversion rates are a direct result of delivering relevant content to users who are most likely to take action.

Behavioral targeting improves conversions in several ways:

• Retargeting Abandoned Carts: Reaching out to users who have added items to their cart but did not complete the purchase.
• Upselling and Cross-Selling: Suggesting complementary products or higher-end alternatives based on customer preferences.
• Reducing Friction: Personalizing user interfaces and streamlining navigation to improve the likelihood of conversion.
• Time-Sensitive Offers: Delivering promotions at optimal times based on user behavior patterns.
• Message Alignment: Ensuring that the marketing message matches the user's stage in the buying journey.

You can use A/B testing in conjunction with behavioral targeting to identify the most effective strategies for different segments.

Optimized Advertising Spend

Behavioral targeting helps businesses spend their budgets more efficiently by focusing on people most likely to respond. This tighter aim means fewer wasted impressions and better returns.

Specifically, behavioral targeting helps with:

• Reducing Waste: By serving ads only to users who are likely to be interested, companies avoid spending money on audiences that are unlikely to convert.
• Higher Engagement Rates: Targeted ads generally see higher click-through rates (CTR) and engagement.
• Dynamic Budget Allocation: Behavioral insights allow marketers to reallocate budgets toward high-performing segments in real time.
• Predictive Budgeting: Data analysis can forecast campaign performance, enabling more accurate budget planning.
• Programmatic Advertising: Platforms that leverage behavioral data can automate bidding in ad auctions.

Types of Behavioral Targeting

There are two types of behavioral targeting, each offering a unique path to personalized marketing.

Onsite Behavioral Targeting

This method personalizes user experiences within a single website. It draws on data like browsing history, product searches, and time spent on specific pages. If someone frequently checks out a particular product category, you can display promotions or content related to that interest.

For example, an online bookstore could use onsite behavioral targeting to recommend books based on a visitor's browsing history, potentially increasing sales. A fashion retailer might display personalized offers to users who spend significant time viewing certain types of clothing.

Network Behavioral Targeting

Network behavioral targeting goes beyond a single site, capturing user data across various platforms. IP addresses and device information help identify users wherever they browse. Marketers then deliver ads that match the user's broader web behavior.

Specifically, this approach involves:

• Comprehensive User Profiles: By aggregating data from various sources, marketers build more detailed and accurate profiles of user interests and behaviors.
• Cross-Device Tracking: Tracking users across different devices ensures a consistent understanding of user behavior.
• Programmatic Advertising: Automated buying and selling of ad space in real time using behavioral data to inform bidding strategies.
• Lookalike Audiences: Identifying new potential customers who exhibit behaviors similar to existing high-value customers.

A retailer could achieve a significant drop in cost per acquisition and a boost in conversion rates by using network targeting.

How Behavioral Targeting Elevates Your Marketing

Behavioral targeting isn’t just a marketing trend—it’s a powerful way to connect with your audience on a deeper level. By tracking user actions, segmenting them into meaningful groups, and delivering personalized content, businesses can increase engagement, boost conversions, and build lasting customer relationships.

The key to success lies in continuously refining your approach. Data collection should be an ongoing process, segmentation should adapt to shifting behaviors, and personalized content should always be tested. Tracking performance and making adjustments based on real-time insights will keep your marketing relevant and impactful.

If you’re looking to take your behavioral marketing to the next level, Pixis offers AI-driven solutions designed to optimize your campaigns with precision. We help brands automate audience targeting, personalize content, and maximize ad efficiency—without the guesswork. Explore Pixis for performance teams and see how AI can transform your marketing strategy.