Behavioral targeting 101

Rather than relying solely on broad demographics such as age or gender, behavioral targeting focuses on users’ actions, the websites they visit, the products they view or purchase, the searches they perform, and other online behaviors. By analyzing this behavioral data, marketers can group users into segments and serve them relevant messages at the right moments. The goal is to increase engagement and conversion by catering to individual interests and intent, as inferred from past behavior.

According to Adweek, behavioral targeting is “a highly specialized and personal form of marketing” that zeroes in on how an audience actually interacts with a brand rather than just who that audience is demographically. In the following sections, we will explore the definition and mechanisms of behavioral targeting, its applications in marketing strategy, the business impact it delivers, the privacy/ethical challenges it raises, real-world examples of its use (and misuse), and how Aerospike’s technology supports behavioral targeting in the AdTech industry.

What is behavioral targeting?

At its core, behavioral targeting (also known as online behavioral advertising) is a marketing technique that delivers tailored advertising or content to people based on their previous behaviors online. These behaviors can include a wide range of user actions, such as:

• Websites and pages visited: For example, which articles a user read or which product pages they browsed

• Search queries used: Keywords or terms the person has searched for, indicating their interests or intent

• Interactions and clicks: Ads or links clicked, videos watched, items added to cart, time spent on certain content

• Purchasing history: Past purchases or conversions, which reveal preferences and needs

• Location and device information: For instance, a user’s geographic location (via IP or GPS) or device type, which helps explain their behavior

By compiling this data through cookies, tracking pixels, and similar technologies, advertisers build behavioral profiles or audience segments. For example, a user who frequently visits travel blogs and searches flight prices might be labeled as a “travel enthusiast.” Another person who spends a lot of time on fashion retail sites could fall into a “fashion shoppers” segment. Behavioral targeting uses these insights to decide which ad or message to show a given user, aiming to show something aligned with their demonstrated interests. In essence, it is about delivering the right message to the right user based on what their past actions say about their preferences and intent.

Behavioral targeting is different from contextual targeting. With contextual targeting, ads are placed based on the content currently being consumed. An example is showing a camera ad on a photography blog, regardless of who the user is. In contrast, behavioral targeting follows the user’s own history. An example would be showing a camera ad to that specific user because they previously browsed cameras, even if they’re now on an unrelated site. The ultimate goal is to increase relevancy: Behavioral ads attempt to anticipate what the user might be interested in buying or clicking, based on patterns in their past behavior.

How behavioral targeting works

Behavioral targeting works through a pipeline of data collection, analysis, segmentation, and ad delivery. Today’s digital advertising infrastructure,  including data management platforms (DMPs), demand-side platforms (DSPs), and ad exchanges, each plays a role in this process. Here’s a step-by-step look at how it typically works:

Data collection

The first step is gathering information about user behavior. This is often done through browser cookies and tracking pixels that log user actions across websites. For example, when you visit an online store, a third-party cookie might note which products you viewed. Mobile apps likewise can send usage events. Other sources of behavioral data include mobile device IDs, login activity (if the user is registered on a site/app), and offline data integrated from customer relationship management (CRM) systems. As much data as possible is collected to build a rich picture of the user, from pages viewed and links clicked to purchase events and time spent on various content. Large advertising platforms aggregate this information in a DMP or similar database, often in real time.

User profiling and segmentation

Once data is collected, it is analyzed to identify patterns and group users into audience segments. Users with similar behaviors are clustered. Examples could be  “people who browse tech gadgets late at night” or “users who have looked at running shoes in the past month”. These segments can be granular. Advertisers define targeting rules for each segment, such as “show Product X ad to the ‘running shoes’ segment. The segmentation can also incorporate predictive analytics and machine learning to determine which people are more likely to buy. In addition, segments may combine behavioral data with demographic info when available, such as focusing on “18-34 year olds who frequently order takeout” as a segment. The better the data and modeling, the more precise the targeting can become.

Ad serving (targeting in action)

With segments and profiles in place, the system then delivers personalized ads or content to users based on their segment membership. In online advertising, this often happens through programmatic ad exchanges: When a user visits a website or opens an app that serves ads, an instantaneous auction (real-time bidding, or RTB) takes place. Advertisers bid to show an ad to that specific user, using the behavioral profile data to decide whether the user matches their target audience. If the user belongs to an audience segment an advertiser wants (say, “tech gadget enthusiasts”), that advertiser’s DSP may bid higher to win the impression and show a relevant ad, such as a new smartphone ad to someone who has been researching phones. This entire process, from detecting the user’s profile to auctioning the ad slot to displaying the ad, happens in split seconds. 

Beyond display ads, behavioral targeting also powers actions such as personalized product recommendations on websites, “people also bought…” suggestions, or targeted email marketing, like emailing a travel deal to users who recently searched flights. All these executions use the underlying behavior data to tailor the content shown to each user.

Feedback and optimization

A continuous feedback loop refines the process. As users interact with targeted content, those new behaviors are fed back into the data pool. Campaigns improve by analyzing which segments respond best. For instance, if “tech enthusiasts” are clicking an ad but not purchasing, advertisers might adjust the message or refine the segment further. Today’s AdTech platforms use artificial intelligence to adjust targeting parameters in real time and increase conversions. 

Applications in marketing strategy

Behavioral targeting underpins numerous marketing and advertising strategies in the digital era. Its ability to deliver the right message to the right person makes it helpful for both getting and keeping customers. Its primary applications include:

Personalized advertising

The most common use is in online ads, where behavioral data helps make the ads a user sees match their interests. For example, someone who has been reading about electric cars might later see ads for electric vehicles or related products when browsing other sites. Social media platforms use behavioral targeting to inject sponsored posts into your feed that match topics you’ve engaged with. This personalization aims to improve click-through rates by appealing to individual tastes or needs.

Retargeting  campaigns

One form of behavioral targeting is retargeting, which means showing ads to users based on specific past actions, such as visiting a website or adding a product to a cart without purchasing. If you’ve ever noticed that after looking at a pair of shoes online, you then “get followed” by ads for those exact shoes on other sites, that’s retargeting in action. It works by tracking that you viewed item X, and then later bidding to serve you an ad reminding you of X. Retargeting boosts conversion by nudging already-interested users to come back and complete a purchase. Marketers often allocate more money to retargeting because these users are considered “warmer” leads than new prospects.

Product recommendations and personalization on-site

Behavioral targeting isn’t confined to ads; it’s also used on websites and apps to personalize the user experience. E-commerce giants such as Amazon and Netflix use algorithms that take into account users’ past behaviors, such as purchases, views, and ratings, to recommend other products or content. This is behavioral targeting within the product itself: The interface adapts to each user. 

Amazon’s recommendation engine, for instance, analyzes what a shopper has browsed or bought to suggest complementary or alternative items. This strategy has proven successful; according to a McKinsey analysis, roughly 35% of Amazon’s revenue is generated by its recommendation engine, which uses behavioral data from customer browsing and buying patterns. The personalized shopping experience keeps customers engaged and sells more items by recommending items in which they are statistically more likely to be interested.

Email and CRM targeting

Marketers also use behavioral cues to trigger personalized emails or messages. For example, if a user hasn’t logged in or made a purchase in a while, they might receive a targeted “we miss you” discount offer. If they viewed a specific product, an email message might follow up with “Still interested in Product X? Here’s 10% off.” These behavioral marketing tactics are aimed at re-engaging customers based on their actions or inactions. Such triggered campaigns tend to outperform one-size-fits-all blasts because they reach the customer with contextually relevant content, such as reminding someone of an abandoned cart.

Dynamic content and offers

Even elements of a webpage can be dynamically tailored to different users. Behavioral targeting supports what's sometimes called “1-to-1 marketing,” where, for instance, the hero banner on a retailer’s homepage might show different featured products to different users depending on their browsing history or loyalty status. Similarly, pricing or promotions might be customized, though this runs the risk of violating consumer protection laws or inspiring accusations of discrimination or manipulation if not done transparently. In mobile apps, push notifications can be behaviorally targeted. For example, a fitness app might send a nudge if it notices you haven’t worked out in a week.

In marketing strategy, these applications share a common theme: relevance. By responding to individual user behavior, companies aim to make their marketing more relevant and timely, which in turn improves user engagement, conversion rates, and overall customer satisfaction. A travel site that knows you recently searched for flights to Hawaii can target you with a deal on Honolulu hotels. In fact, a well-timed offer might actually be welcome. This relevant approach contrasts with blanket mass marketing, and it has become expected by consumers; people now often assume the ads or recommendations they see will reflect their interests, to the point that generic, untargeted messages can feel jarring or ineffective by comparison.

Business effects of behavioral targeting

Behavioral targeting has had a profound effect on the advertising and media business, changing how advertising generates value. Making ads more relevant, spending marketing budgets more effectively and efficiently. Several aspects of its business impact are noteworthy:

Higher ad performance and ROI

Targeting ads based on user behavior yields better results than untargeted ads. Research summarized in Harvard Business Review found that digital ad targeting “meaningfully improves the response to advertisements.” In other words, targeted ads get higher click-through and conversion rates. Conversely, ad performance declines when marketers have less access to consumer data.  This performance lift translates into a higher return on investment (ROI) for advertisers. By zeroing in on likely buyers, marketing dollars are spent more efficiently. A 2023 Yale School of Management report reinforced this point, noting that advertising is more effective when “strategically directed” via data-driven targeting. A campaign that uses behavioral targeting might demonstrate increases in conversion or sales lift of more than 10% compared with a non-targeted campaign, a significant edge in competitive markets.

Premium value of targeted inventory

Behavioral targeting also helps publishers of websites or apps that show ads make more money.  Advertisers are willing to pay more to reach specific audiences who are more likely to engage or buy. According to Google and industry sources, personalized ads are valuable for the entire ecosystem because users prefer relevant ads and publishers make more money from personalized advertising, Wired reported. 

In fact, Google has argued that publishers could lose more than half of their ad revenue if they stopped using behavioral targeting techniques. Without behavioral data, many ad impressions become “low value” because advertisers see them as reaching random, unknown users. With behavioral targeting, that same ad slot attracts advertisers in categories that bid more. The net effect is more revenue per impression for the publisher.

Widespread adoption and industry growth

Given these advantages, it’s no surprise that behavioral targeting via programmatic advertising has grown to dominate the ad industry. Programmatic ad buying, which largely relies on real-time data and behavioral segments, now accounts for most digital ad spending. By 2023, roughly 81% of worldwide digital ad spend was transacted programmatically, reflecting how ubiquitous data-driven targeting has become in marketing. Entire business models were built on it: Facebook/Meta’s and Google’s multi-billion-dollar advertising empires are essentially fueled by behavioral targeting, profiling users’ interests and actions to sell targeted ads. Many direct-to-consumer brands credit platforms like Facebook Ads for allowing them to find niche audiences across the world through behavioral filters that weren’t possible in pre-digital advertising. Small businesses also benefit by narrowly targeting their relevant audience. For example, a local bakery can target only people within 10 miles who have shown interest in “gluten-free baking”, which is more cost-efficient.

Improved customer experience and loyalty

From a broader marketing perspective, when done thoughtfully, behavioral targeting can enhance customer experience by showing people ads that interest them, rather than bombarding them with irrelevant ads. This can indirectly boost brand loyalty and customer lifetime value. For instance, if a streaming service’s recommended movies match a user’s tastes, the user is likely to use the service more and retain their subscription. In retail, if targeted promotions align with what a customer wants, they are more likely to make repeat purchases. Thus, beyond immediate ad clicks, behavioral targeting supports long-term business metrics by personalizing the customer journey.

Of course, it’s important to acknowledge that these business benefits come with some caveats. If targeting is done poorly because of incorrect data or an off-base ad, it can backfire and annoy consumers. That’s not only wasted money but could also cost you a customer. There’s also a point of diminishing returns; hyper-targeting a small segment could mean an advertiser is paying a lot per impression to reach a tiny group, missing out on scale. However, overall, access to detailed user data has been a game-changer for effective marketing. Companies that use behavioral targeting well often acquire customers and buy effective advertising more effectively than competitors that rely on traditional, untargeted approaches.

Real-world examples and case studies

To better understand behavioral targeting in practice, let’s look at a few real-world examples, both good and bad: 

Social media ad targeting by Facebook/Meta

Facebook (now Meta) offers advertisers granular targeting options based on user behavior and interests on its platforms. Everything from pages a user has liked, to what topics they engage with, to their off-Facebook browsing affects which ads they see. This means niche businesses can find their exact audience, such as “people in California who have recently been looking at sustainable living blogs and are into yoga”, with minimal waste. 

It’s been a boon for businesses; Facebook’s ad business makes more than $100 billion annually. But it’s also led to controversies. In examples such as the Cambridge Analytica scandal,  Facebook’s targeting was implicated in helping political operatives micro-target misleading messages to specific voter groups in the 2016 U.S. election. That case demonstrated that Facebook’s powerful ad tools, combined with extensive behavioral data, could exploit psychological vulnerabilities at scale. Facebook has since put stricter policies in place for political ads and certain sensitive targeting categories, but the example stands as a cautionary tale of behavioral targeting’s potential for misuse.

Target’s predictive analytics (the pregnancy case)

One well-known example of behavioral targeting comes from the retail chain Target,  illustrating the power and unintended consequences of offline behavioral analysis. Around 2012, Target’s data science team created a “pregnancy prediction” model based on shopping behaviors. By tracking purchases of certain products, such as unscented lotion, vitamins, and cotton balls,  Target could predict when a customer might be newly pregnant and then send them targeted coupons for baby items. 

The model worked, perhaps too well. In one reported incident, a father complained to a Target store that his teenage daughter was receiving baby product coupons; it turned out Target had algorithmically identified her as pregnant before her family knew, based on her shopping pattern. This story highlighted how behavioral targeting, even using in-store and purchase data, not just online data, can cross personal boundaries into creepiness.  

Target adjusted how it uses such predictions, blending them with more generic ads to be less overt. The case study is now a go-to example of the need for sensitivity in using behavioral insights. Just because you can target someone with a deeply personal inference doesn’t always mean you should.  

Political campaign micro-targeting

Beyond commercial use, behavioral targeting has been a key tool in political advertising. Campaigns can use voter data combined with online behaviors to send different messages to different slices of the electorate on social media or streaming platforms. 

For instance, a campaign might identify a segment of voters who frequently visit military family forums and target them with ads focusing on defense policy, while another segment that reads about student loans gets ads about education policy. Each citizen sees the angle most likely to resonate with their concerns. While this can be seen as a way to efficiently inform voters on issues they care about, it has also raised alarms about the spread of misinformation. Targeted political ads are often not subject to the same public scrutiny as a more widely disseminated ad, so a misleading ad shown only to a small group can fly under the radar. After 2016, Facebook and others created searchable ad archives to increase transparency, but micro-targeting in politics remains controversial. 

Countries such as the United Kingdom have debated banning micro-targeted political ads altogether for fear they undermine the democratic process by fragmenting the public discourse. This real-world use underscores that behavioral targeting is a powerful communications tool, one that can be used for persuasive messaging in any domain, not just selling products.

These examples demonstrate both the potency and the pitfalls of behavioral targeting. On one hand, it drives tangible business success, such as higher sales, better ad performance, and growth for brands that take advantage of it. On the other hand, when applied without oversight or ethical restraint, it can lead to public relations backfires or societal issues.

The common thread is that behavioral targeting magnifies the ability to reach people with relevant messages, which can be a double-edged sword depending on the intent behind those messages and the context in which they’re delivered. As we move forward, the lessons from these case studies guide how companies and regulators set the boundaries for the responsible use of behavioral data.

How Aerospike supports large-scale, real-time behavioral targeting

However, taking advantage of behavioral targeting means being able to analyze user behavior and place ads in essentially real time. If you’re an AdTech developer building a behavioral targeting system, using Aerospike under the hood means you can trust that no matter how much your user data grows or how fast you need decisions, the database layer will keep up. This lets you focus on designing clever algorithms and segments, knowing the infrastructure can deliver those insights in real time when an ad impression is on the line. Aerospike supports behavioral targeting by providing the speed, scale, and reliability required to take advantage of behavioral insights in the fractions of a second during which the personalized ad or offer is delivered to the consumer. That kind of performance is a game-changer for behavioral targeting initiatives.

Executing behavioral targeting at internet scale requires a data layer that can ingest millions of events per second, update user profiles in real time, and return results in under 100 ms for programmatic ad auctions. The Aerospike real-time data platform delivers:

• Sub-millisecond reads/writes at petabyte scale, supporting millions of bid-request look-ups per second without cache layers

• Low total cost of ownership; global AdTech leader Criteo cut its server footprint 75%, from 3,200 to 800 servers, while storing 1.2 trillion user-profile records and matching ads to users 950 billion times daily in ~50 ms

• Multi-model data, such as key-value, document, and graph, supports identity graphs and audience segmentation in the same cluster, helping advertisers cope with cookie loss and privacy-first IDs

• Active-active, geo-distributed architecture helps keep user data local for GDPR/CPRA compliance, yet available worldwide for serving ads with low latency

For brands and platforms looking to set up long-term behavioral targeting, Aerospike provides the speed, scale, and efficiency required to process large behavioral datasets and serve personalized ads in real time.