Reaching the right audience is harder than ever—60% of advertisers agree. Buyers aren’t seeing ads that spark their interest, and 67% of consumers complain that the ads they see aren’t relevant to them.

This growing problem is largely due to the changing digital landscape. Privacy concerns and the impending deprecation of third-party cookies have made behavioral targeting less effective and more controversial. Consumers are increasingly wary of how their personal data is used, leading to a shift away from intrusive ad practices.

In response, many advertisers are moving towards contextual targeting strategies. But how can advertisers know which approach is better?

Let’s explore the differences between behavioral and contextual advertising. You’ll learn how each method works and the benefits of each to help you decide which targeting strategy best supports your advertising goals.

What is behavioral targeting?

Behavioral targeting, also known as audience targeting, is an advertising mechanism that uses a person’s online behavioral data to deliver tailored digital ads. Unlike contextual targeting, which focuses on placing ads based on the content of the website, behavioral targeting relies on tracking and analyzing a user’s past online activities to predict their future actions and preferences.

How does behavioral targeting work?

Behavioral targeting builds a detailed profile of users by collecting data on their online behavior over time. This profile helps advertisers understand what users are likely to buy, how they shop, and what interests them, allowing for the delivery of highly personalized ads.

For example, imagine you sell organic seeds. Behavioral targeting would use data showing that your customers frequently visit gardening blogs, sustainability forums, and search for “best organic seeds.” This information enables you to place ads for your organic seeds in these specific contexts, directly targeting users who have shown interest in gardening and sustainability.

Key data types used in behavioral targeting

To effectively utilize behavioral targeting, advertisers need a variety of data about users’ online activities. Here are the main types of data used:

• Browsing history: A person’s web browsing data tells you about the websites the user visits and how they interact with content. So, if a user often reads articles about fitness, this might result in more ads for gym memberships.
• Purchase history: This data includes past behavior, purchases, and product interests. For example, if someone bought music in a specific genre, they might see ads for similar artists going forward. Or, if they recently bought a kitchen stand mixer, they may see ads for complimentary products, like blenders or food processors.
• Demographics: These are basic statistics about a person, such as age, gender, and location. Advertisers use this data to target ads to specific groups. For instance, say, a dating app notices that Gen Z users upgrade most often. In this case, it’ll target ads to this specific age group.
• Search history: A person’s search history data tells you about the terms and phrases they input into search engines. This data helps you target ads to people searching for relevant topics.
• App usage: Advertisers can tell a lot about a user by the types of apps they engage with. For instance, a person who uses a business travel planning app would be the ideal target for hotel promotions and car rental deals.
• Social media activity: What a user likes, shares, and follows on social media tells you about their interests, hobbies, and preferences. For example, a person who follows mostly foodies and chefs might see ads for cooking courses and recipe books.

The main problem with behavioral targeting is its reliance on personal data. This raises serious questions and concerns about privacy and data ethics, especially in the face of a (soon-to-be) cookieless world.

Why data types matter in behavioral targeting

Understanding behavioral targeting’s foundation requires recognizing the pivotal role of various data types. Each data source—be it browsing history, purchase records, or social media interactions—offers unique insights into consumer behavior.

These data strands weave together to form a comprehensive view of consumer interests and preferences, allowing advertisers to precisely tailor their messages. This nuanced approach ensures that ads are seen and resonate deeply with the intended audience, fostering a connection based on genuine interest and need.

Pros and cons of behavioral targeting

In digital marketing, the balance between personalization and privacy is crucial. Transparency and control over data collection and use is critical to consumer trust. This gets harder as we move toward a cookieless world. It’s becoming more complex to collect customer data. This is why brands are exploring cookieless advertising solutions like contextual targeting and first-party data strategies.

Pros of behavioral targeting

• Enhanced relevance: Tailoring ads to user interests and preferences leads to more sales. In fact, 76% of consumers are more likely to buy from brands that personalize their marketing strategy. Imagine you’re a bookworm who loves thrillers. You’re more likely to respond to a promotion on thriller novels than historical biographies.
• Improved campaign performance: Targeted ads reach a more relevant audience—resulting in better conversion rates and return on investment. When ads are relevant to them, customers are more likely to buy.

Targeted promotions have the highest influence on buyers over any other form of advertising.

• Personalized user experience: With behavioral targeting, users receive ads for products and services they’re genuinely interested in. This creates a more positive and relevant ad experience.
• Granular targeting options: Businesses can target specific audience segments based on demographics, behaviors, and purchase stages. This highly focused approach makes it easier to tailor marketing to match a user’s real-life experiences. For instance, a maternity website could tailor its ads to align with different stages of a user’s pregnancy journey.
• Dynamic ad content: Ads can adapt to real-time user data and context. This delivers an even more personalized and relevant ad experience. For example, a user browsing clothing trends ‌might see ads for warm coats in the winter.

Cons of behavioral targeting

• Privacy concerns: Tracking user behavior raises privacy concerns. Users are notoriously uneasy about companies collecting and using their online behavioral data. Plus, there’s issues around collection and storage methods. You don’t just lose consumer trust if you’re subject to a breach. Data privacy regulations—like the California Consumer Privacy Act (CCPA) and the General Data Protection Regulation (GDPR) in Europe—mean you could face legal repercussions.
• The death of the cookie: With third-party-cookie deprecation, traditional data targeting is less effective. This means it’ll be much harder to track behavioral data. This forces marketers toward alternative approaches like contextual targeting.
• Algorithm bias: Bias in targeting algorithms can lead to unfair and inaccurate targeting. This can exclude certain demographics or viewpoints and lead to skewed ad distribution. For instance, a financial app’s algorithm might target credit card ads primarily to high-income neighborhoods. It unintentionally excludes potential customers in lower-income areas. This results in unequal service access and missed opportunities.

These laws emphasize transparency, user consent, and privacy rights, reshaping how advertisers collect and utilize data. The transition towards a cookieless world is an opportunity to pioneer advertising strategies that respect user privacy while delivering impactful messages.

Examples of behavioral targeting

Here are some examples of behavioral advertising in action:

1. Fitness App (FitFirst)

A FitFirst user frequently searches for terms like “beginner running plans” and “running programs”. They also spend a lot of time reading articles about healthy eating. As a result, they might encounter an Instagram ad for FitFirst’s “Couch to 5K” program. Later, while listening to a running podcast, they hear an audio ad for a free FitFirst meal plan.

2. Travel Booking Platform (Wanderwell)

A platform user is interested in traveling to Bali. They browse flights online, save hotels in Agoda, and read blog posts about hidden gems in Indonesia. In response, they see Google PPC ads for Wanderwell’s curated Bali itinerary packages. On Facebook, they see image ads featuring Wanderwell’s hotel discounts. Later, they receive a personalized email from Wanderwell suggesting top Bali tours.

3. Online Furniture Retailer (CozyNook)

A past customer searches for “modern living room furniture sets” and pins home decor inspiration on Pinterest. They also click on ads for mid-century coffee tables. Later, they see promoted pins from CozyNook in their Pinterest feed. These pins showcase living room sets similar to their saved items. They might also receive a personalized SMS featuring promotions on mid-century coffee tables.

What is contextual targeting?

At the heart of contextual targeting lies the principle of relevance—placing ads where they naturally align with content. This strategy pivots from tracking user behavior to analyzing the content’s context through three sophisticated methods: keyword, category, and semantic targeting.

Semantic targeting, particularly, showcases the power of AI, enabling a deeper comprehension of content beyond mere keywords. This AI-driven approach enhances ad placement precision, marrying advertiser messages with content that speaks the same language as their intended audience. So, an ad for online yoga classes might appear in a healthy living podcast.

Faced with a cookie-less future, advertisers are ramping up their contextual ad spend. In fact, more than 60% of advertisers plan to increase their spend. 53% of these expect to boost their budget by up to 50%.

But it’s not just about the decline of cookies. It’s also because programmatic contextual advertising works. 95% of advertisers using contextual advertising expect it to increase revenue.

In other words, by improving ad relevance, you target your ideal buyers where they like to spend their time. Let’s take a closer look at how contextual advertising works.

Methods of contextual targeting

There are three different methods of contextual targeting:

1. Keyword targeting: This tactic matches ads to keywords found on the page. For example, an ad for a fitness tracker might appear in a blog post discussing marathon training tips.
2. Category targeting: This method reaches users based on predefined website categories. For instance, a travel insurance ad might show up on travel advice forums.
3. Semantic targeting: This method uses AI to understand the meaning of the page’s content and match ads accordingly. An ad for eco-friendly cosmetics might appear in an article discussing sustainability, even if your specified keywords aren’t present.

Pros and cons of contextual targeting

Contextual ad campaigns tackle ‌issues dealing with privacy concerns and tracking cookies. While contextual advertising has several advantages, it also has a few limitations.

Pros of contextual targeting

Privacy-friendly: Contextual targeting doesn’t rely on user tracking. In this sense, it’s more respectful of privacy.

As Krzysztof Lis from Yieldbird explains, “As privacy concerns continue to shape the digital advertising ecosystem, I expect contextual targeting to experience a constant evolution, becoming a linchpin for both programmatic and direct advertising campaigns.”

Broader reach: Contextual advertising targets potential customers in new contexts. Rather than chasing ‌customers based on similar behaviors, you define your ideal audience and target them based on their values and interests. After all, 82% of customers pick brands that align with their values. By doing this, you’ll find more customers that fit your persona that you may not have reached before.

More relevant audience: More than 70% of marketers recognize how important contextual marketing is for audience targeting. Relevant ads placed in the right context streamline the content experience. They’re more likely to resonate with the audience.

Realtime appeal: Contextual targeting works in the present moment. It appeals to browsers while they’re thinking about topics related to your products and services.
As Samantha Allison, director at StackAdapt, notes, “With contextual, ads are targeted based on the content the audience is consuming in the present moment, ensuring they’re captured in the right frame of mind to be receptive to the ads.”

Brand safety: Less than 40% of marketers understand the true importance of contextual marketing for brand safety.

With behavioral programmatic advertising, there’s a chance your ads will appear next to harmful or inappropriate content. This could tarnish your brand’s image. Contextual advertising significantly reduces this possibility by matching ads to the content’s context instead of relying on black-and-white lists.

Cons of contextual targeting

Limited personalization: While contextual targeting increases relevance, it’s less personalized than behavioral ad campaigns. It doesn’t factor in user data to place ads. As a result, it can’t tailor campaigns to individuals on the behavioral level.

Dependence on content quality: The effectiveness of contextual targeting relies heavily on the quality of the content surrounding the ad. Readers (sometimes unknowingly) associate the quality of content with the quality of products within ads. To avoid low-quality content and poor ad placements, use tools like SeekrAlign. Its fully automated, AI-driven content rating system helps you discover high-quality content that aligns with your values and upholds brand safety.

Examples of contextual targeting

Here are some examples of contextual advertising in action:

1. Gourmet Cooking Blog (Chef’s Kiss)

A food enthusiast is reading a post about Italian cuisine on a gourmet cooking blog. In the sidebar, Chef’s Kiss advertises its premium Italian cooking set. This ad directly relates to the content the user’s engaging with. This improves the product’s relevance and appeal.

2. Eco-Friendly Home Products Store (GreenLyfe)

An eco-mom is listening to an environmental podcast discussing sustainable home practices. Within the podcast, GreenLyfe places an ad for its biodegradable kitchenware.

Using Seekr to analyze content at the granular level, GreenLyfe places the ad right after a discussion on‌ zero-waste solutions. This contextual placement means the ad appears to your relevant target audience at an appropriate time. This increases the chances of engagement.

3. Online Education Platform (DigiLearn)

On an educational forum that discusses career advancement, DigiLearn places ads for its latest online digital marketing courses. The ad contextually aligns with the forum’s focus and targets individuals ready to convert.

Contextual vs. behavioral targeting

Contextual and behavioral targeting are two distinct approaches to reaching your audience. Let’s break down the key differences and why each might be right for your campaign.

Basis of targeting

• Behavioral targeting: Uses personal data and online behavior history to serve ads. It tracks what users do online—like the websites they visit, what they search for, and what they buy—to predict what they might want next.
• Contextual targeting: Places ads based on the content of the webpage a user is currently viewing. It looks at the keywords and topics on the page to show ads that fit the content the user is engaging with right now.

Personalization

• Behavioral targeting: Delivers highly personalized ads based on individual user data. It’s all about tailoring the ad experience to each user’s specific interests and past behavior.
• Contextual targeting: Matches ads to the context of the content, not the individual user. While it’s less personalized, it ensures ads are relevant to what the user is currently reading or watching.

Data Usage

• Behavioral targeting: Depends heavily on cookies and tracking technologies, raising privacy concerns. It collects and analyzes personal data, which means dealing with privacy issues and user consent.
• Contextual targeting: Doesn’t use personal data, making it more privacy-friendly. It focuses on the immediate context of the content, which helps it comply with privacy regulations more easily.

Ad Relevance

• Behavioral targeting: Shows ads based on past behaviors, which might not always match a user’s current interests. This can sometimes make ads feel out of touch or intrusive.
• Contextual targeting: Ensures ads are relevant to the content being consumed at that moment. This means the ads are more likely to match the user’s current mindset and interests, boosting immediate engagement.

Cost-Effectiveness

• Behavioral targeting: Can be more expensive due to the need for detailed data collection and analysis. Tracking and personalizing ads can add significant costs.
• Contextual targeting: Usually more cost-effective since it uses a “best-fit” principle. It’s based on content rather than extensive user data, making campaigns cheaper and easier to manage.

Simply put, behavioral advertising uses a consumer’s personal data to serve ads based on their past behavior, while contextual advertising places ads in environments that align with user interests without relying on personal data. The biggest distinction is that behavioral campaigns target people based on their history, whereas contextual campaigns show ads based on the content people are viewing right now.

The future of advertising with evolving landscapes

As we navigate the distinctions between contextual and behavioral targeting, it’s crucial to look ahead. The advertising industry stands on the cusp of revolutionary changes driven by emerging technologies and shifting privacy norms.

Beyond semantic targeting, AI enhances ad personalization, optimizes campaign performance, and ensures ads meet the highest standards of brand safety and suitability. By analyzing vast datasets, AI predicts trends, identifies audience segments, and creates dynamic ad content that resonates on a personal level. These advancements promise a new era of advertising where efficiency, effectiveness, and ethical considerations converge.

As we understand the nuances of targeting strategies and the transformative potential of AI, it’s clear that the future of advertising lies in innovation and adaptability. This is where Seekr emerges as a pivotal player, steering the course towards more effective, ethical, and engaging advertising solutions.

Pioneering precision in contextual advertising

In an era where relevance and privacy go hand in hand, SeekrAlign is leading the way with innovative solutions to modern advertising challenges.

SeekrAlign uses advanced AI to evaluate content at a detailed level, ensuring your ads meet strict brand safety and suitability standards. This technology protects your brand and helps you find new advertising opportunities that align with your values, expanding your reach responsibly.

With SeekrAlign, your ads are placed in the perfect context, connecting with your audience’s current interests and values. As the advertising landscape changes, combining contextual relevance with ethical data use is essential. SeekrAlign offers a privacy-conscious approach to ad placement, helping you navigate this new terrain confidently.