Contextual vs Behavioral vs Audience Targeting: A Full Comparison
Contextual and behavioral targeting are two popular methods of serving targeted ads which have been compared to one another ever since the release of Google’s behavioral ad targeting program over a decade ago in 2009.
Since its introduction, there’s been no shortage of enthusiasm surrounding behavioral targeting’s ability to increase ad relevance based on a user’s past browsing history.
But even during the early days of the technology’s introduction, the contextual vs behavioral targeting comparison hasn’t really (or at least, infrequently) been about “which one to pick”.
Rather, most conversations focus on how to most effectively use a mix of both technologies in order to achieve effective audience targeting as part of meeting an advertising campaign’s objectives.
In fact, a study conducted by ResearchGate indicates that using a combination of contextual and behavioral targeting signals increases the likelihood of consumers perceiving marketing messages as valuable.
However, in recent times, updates surrounding the end of third party cookies have been shifting the narrative - and for good reason, as behavioral targeting relies heavily on cookies.
Additionally, up to 79% of modern consumers are more comfortable seeing contextual vs behavioral ads according to a study conducted in 2022.
With that said, behavioral targeting may not be as irrelevant in the future as many sources seem to indicate - it’ll just need to undergo a pivot, like much of the technology currently being used in the ad tech ecosystem.
In this article, we’ll be reviewing how contextual and behavioral targeting work, how each one ties in with modern audience targeting strategies, as well as clarifying some confusion which is sometimes introduced when programmatic ads are brought into the conversation.
Table of Contents
- What does contextual targeting mean?
- What is behavioral targeting?
- What is audience targeting and segmentation?
- Contextual vs Behavioral vs Audience Targeting and The Benefits of Each
- Ad placement strategies that combine contextual, behavioral, and audience targeting
- Contextual vs behavioral advertising - which should you be more excited about?
What does contextual targeting mean?
Contextual targeting is an advertising technique that works by serving ads to web properties which feature content that aligns with the topic of the ad being served - meaning that the products and services in the ad are related to the website upon which the ad appears.
As part of an ad placement strategy, contextual targeting concerns itself with where content is served on the web. In other words, contextual targeting involves the manual or automated process of ensuring ads are placed on websites which share a theme with what's being sold or promoted by the ad creative.
One of the distinguishing characteristics of contextual is that it’s a cookieless targeting technique - meaning that it doesn’t rely on third-party data (cookies) or identifiers to function.
If you’re interested in taking a deep-dive into how contextual targeting works, examples of how its used, and its benefits in digital marketing, check out our dedicated guide to how contextual targeting works.
The importance of contextual targeting data for the cookieless web
By this point, it almost feels redundant whenever the end of third party cookies is mentioned online - most people in the ad tech space are familiar with the disruption this event will have on the ad tech space.
Despite being common knowledge, 49% of marketers still regard third-party cookie deprecation as one of their greatest challenges to overcome moving forward.
The popularity of contextual advertising has seen a resurgence primarily for this reason - it’s an effective targeting technique that will continue to function on the cookieless web.
Google’s latest proposal to replace cookies following the rejection of the FLOC program is Google Topics, an approach to ad targeting which is built using a contextual framework.
Because of how contextual targeting works on Google Ads, it’s more important than ever for publishers to ensure that their web properties are context-ready to continue tapping into this programmatic revenue stream (tools like contextual management platforms can help with this).
Contextual categorization may be considered a “foundational element” for much of digital advertising moving into the post-cookie era, but contextual targeting can perform even more effectively with support from other data management practices (which we’ll cover later).
How is contextual targeting related to programmatic advertising?
Programmatic advertising sometimes causes confusion in conversations surrounding ad serving - and is a concept which ties in with both contextual and behavioral targeting.
At the most basic level, the term “programmatic” can be understood as being synonymous with the term “automated”.
Because various targeting methods like contextual and behavioral involve so many complicated factors in the ad bidding process, in most cases, it would simply be impossible for humans to manage the process themselves.
Programmatic advertising allows many aspects of the ad serving process to be managed by machines.
Despite playing a critical role in supporting many forms of ad targeting, “programmatic targeting” isn’t a really a "technique" or an "approach" to audience targeting like contextual and behavioral targeting are.
Rather, the term is often used alongside other types of targeting in conversations - for example, “programmatic contextual advertising” or “contextually driven programmatic ads”.
You might equate the concept of "programmatic" to automatic vs manual transmission in a car - it's a supporting characteristic that can be found in many different car models that each focus on different performance characteristics.
In this example, contextual and behavioral targeting would each be different "car models" - they each take a different approach to reaching a destination (or in this case, a target audience), and are often supported by the option to make use of additional programmatic targeting features.
With that said, is there such a thing as a non-programmatic contextual ad?
The answer is yes, but in modern ad serving, it’s quite uncommon.
In contrast to behavioral ads which are always managed programmatically (due to their heavy reliance on complicated sets historical user data), contextual advertising campaigns can be deployed manually by ad ops teams through what’s known as a direct deal.
Direct deals are arrangements in which an advertiser purchases inventory directly from a publisher - and is typically reserved for high-ticket purchases involving premium inventory.
However, while it’s possible to manually deploy contextual ad campaigns to a website, a technique known as programmatic direct is often used to automatically execute the delivery of ads once the terms of the deal have been agreed upon by both parties.
If you’re starting to get confused at this point - that’s not uncommon. Programmatic advertising is easily one of the most bewildering subjects in ad tech.
If you’re interested in learning more about programmatic advertising, here are some useful sources for further reading:
- How programmatic contextual category targeting works
- The Different Types of Programmatic Deals Explained
- The Programmatic Ecosystem Explained
- Ad Networks vs Ad Exchanges - The History of Programmatic Advertising
What is behavioral targeting?
Behavioral targeting is an advertising technique through which the past actions of a user are used as signals to identify trends in their behavior, which are then used to target personalized ads towards them.
It works by assessing factors like web browsing and search history, the amount of time a user has spent on certain web pages, and the level engagement (such as clicks) a user has made on certain pages and ads they’ve encountered in the past.
For example, a user that reads many review blogs about running over the course of a month might be targeted with ads by brands that sell running shoes and other running equipment.
Behavioral targeting is also sometimes referred to as "online behavioral advertising", abbreviated as OBA.
As a technique, behavioral targeting is often used to enhance the level of personalisation contained within an ad creative.
As another example, while a travel agency may often run a generic ad campaign, if a user demonstrates particular interest in a specific location, the same travel agency may instead serve an ad which contains themes that match that location.
Behavioral targeting is reliant upon access to data surrounding a user’s behavior, which has traditionally meant relying heavily on third-party identifiers in the form of cookies.
Third-party data has traditionally been accessed by advertisers through platforms known as DMPs (data management platforms), which are repositories of user information used to enrich ad tags with additional information about users.
As part of an ad placement strategy, behavioral targeting concerns itself with what actions a user has been taking recently in order to determine which ad to serve to them. Behavioral targeting is an automated process which relies on user data to increase ad relevance.
What is audience targeting and segmentation?
Audience targeting is a digital marketing strategy which involves defining a target audience by assessing demographic data, behavioral tendencies, interests, intentions, and other known information about user.
In addition to its strategic component, many advertising platforms offer features that support the ability to target specific audiences with ads (with the most popular example being audience targeting on Google Ads).
Traditionally, behavioral and audience targeting have been two terms that are used interchangeably (particularly in digital advertising) - thanks in no small part to Google’s own naming convention for its features.
However, the rise in popularity of serving ads to various OTT and CTV devices (including mobile) through device targeting raises a question - should device preference and usage data really be separated so rigidly from an audience’s holistic data profile?
For example, users that frequently watch certain genres of OTT programming generate a traditional behavioral signal - but combining this information with a user's device preferences for viewing content (which may include mobile or gaming platform devices, further signaling inferable interests and/or lifestyle habits) has the potential to add a valuable layer of non-behavioral data to their overall audience profile.
Additionally, while traditional contextual targeting exists, new proprietary tools like contextual management platforms allow publishers to showcase their own targetable contextual audience segments to their advertising partners directly.
While we’re not looking to “rock the boat” too hard with an already confusing set of ad tech jargon to contend with, the scope of what comprises “audience targeting” may be due for a practical semantic update over the coming years (and many discussion online already use the term "audience" seperaretly from "behavioral").
For instance, Peer39, a solution provider in the contextual sector, defines audience targeting as an overarching ad placement technique which can leverage a mix of demographic, psychographic, and/or behavioral data as part of a strategy for reaching a target audience (read more about different types of audience targeting).
As audience profiles expand to include more data than just a user’s past historical actions - and the third-party data traditionally used to create those profiles becomes obsolete, we offer distinct definitions for behavioral and audience targeting later in this article.
Audience targeting is heavily associated with the concept of audience addressability, which refers to the amount of reach an ad campaign is able to generate against a defined set of audience criteria.
As part of an ad placement strategy, audience targeting focuses on who an ad is being served to, by compiling first-party data to create user profiles, and creating targetable audience segments from sets of user profiles with similar characteristics.
Audience segments are used to create personalized messaging within the ad creatives that are delivered to each user group, based on the data used to construct the cohort.
If you’d like to learn more about addressable audiences and their importance in digital marketing, check out our dedicated guide to addressable advertising.
For additional tips and ideas on how to effectively plan an audience targeting strategy, this guide to audience targeting by HubSpot has you covered.
What is placement targeting?
Placement targeting is an optional feature which allows advertisers to specify and limit which websites their ads are served to, while maintaining any of their other targeting preferences.
For example, an advertiser might contextually target users that are interested in a certain topic - but may wish to limit their ads to being served on certain blogs and YouTube channels which occasionally or primarily focus on that topic.
The term commonly refers to placement targeting on Google Ads, though similar features may be found on other ad platforms.
From a semantic standpoint, while “ad placement” is a term often used when discussing different types of ad targeting, “placement targeting” specifically refers to ad platforms which allow advertisers to select which websites their ads are displayed on.
Contextual vs Behavioral vs Audience Targeting and The Benefits of Each
The difference between contextual and behavioral ad targeting lies in the ad serving method - contextual targeting selects contextually relevant content to serve ads to, while behavioral targeting delivers ads based on a user’s historical browsing data to increase personalization.
Confusion between the two terms is common, as both techniques can be used to achieve “cross-selling” in different ways. That is to say, if a user is demonstrating interest in purchasing one type of product, they may also be interested in purchasing a similar one that's related in some way.
(Source: MarTech Zone - For example, someone who’s interested in purchasing dinner party desserts may also be interested in purchasing dinner party attire.)
In this example, cross-selling could be achieved by contextually aligning the ad with a piece of content about dinner party desserts - but it could also be achieved by serving the same ad to an entirely different website by instead targeting the user’s past behavioral actions.
(“Audience targeting” has traditionally been used interchangeably with “behavioral targeting” in digital advertising - though we offer a distinct definition in this article to account for recent shifts in how audiences and their data are collected and targeted.)
Audience targeting is different from both contextual and behavioral targeting - it’s both a high-level strategy involving user profiling, as well as a holistic approach to ad serving which leverages contextual, behavioral, demographic, psychographic, and other types of data signals to reach defined audiences.
As a set of advertising terms which are often confused with one another, here’s a look at the differences between them in a side-by-side comparison table:
| How It Works | An ad serving technique which creates and ensures contextual alignment between the content on a page and the content of an advertisement. | A method of delivering ads which involves the use of action-based data to identify trends in a user’s historical online activity - qualifying or disqualifying them as an ad candidate (traditionally used interchangeably with “audience targeting” - particularly due to the naming convention of Google’s behavioral targeting feature). | A high-level user profiling strategy and approach to serving ads which may include contextual, behavioral, demographic, psychographic, and other diverse forms of data to improve campaign performance when targeting specific segments of a web publisher’s audience. |
| Reliant on Third-Party Data? | No | Yes (In the near future, behavioral targeting will instead rely upon first-party data.) | It depends on which method is used to reach the audience. |
| Ad Placement Focus | Where the ad is going to appear online (on which web property, based on contextual alignment). | What actions users have previously taken online which qualify or disqualify them from receiving a particular ad. | Who the ad is being served to, based on defined audience criteria (including behavioral traits, demographics, psychographic interests, perceived intent, contextual content signals, and device usage). |
| Example Scenario | A gardening center wishes to promote a spring sale. In order to do so, they run a contextual ad campaign which targets the “Home & Garden” topic to serve their ads to websites which feature content about gardening. | By using third-party identifiers, a clothing company is able to identify users who have recently been researching and purchasing clothing for formal events - behaviorally targeting a clearance sale ad for formal dresses, suits, and accessories towards users which have signaled purchasing intent. | An advertiser promoting a new gaming headset identifies middle-aged PC gaming enthusiasts as an ideal audience they’d like to target. The advertiser approaches several gaming news publishers, and contextually targets all inventory that includes multiplayer games, which are more likely to involve communication. The advertiser also requests that first-party data be used to target users aged 18-40 who are browsing from a desktop PC, with an emphasis placed on registered users who have recently demonstrated engagement by clicking other links on the website. |
| Benefits / Advantages | • More engaging user experience • Higher purchasing intent • Easy to implement • Higher brand-safety • Privacy friendly • More contextual targeting benefits |
• More engaging user experience • Higher purchasing intent • Higher ad personalization • More behavioral targeting Benefits |
• Beneficial in comparing and identifying the most cost effective ad placement options for each audience • More audience targeting benefits |
| Challenges / Drawbacks | • Can be challenging (or sometimes impractical, depending on the scope of the web property) to tag content and showcase inventory without the use of a contextual management platform | • Very commonly reliant on third-party data, which is set to expire soon • Considered “creepy” by many users online • Can compromise brand safety when control over which websites are involved isn’t maintained • May not always be relevant to a user’s current interests, as data is often historical |
• Can involve extensive (and expensive) trial and error to refine audience segments |
While there are some clear overlaps in the benefits offered by each targeting method - contextual, behavioral, and audience targeting go about providing these benefits in different ways.
Combining the advantages of each technique with one another can be very effective in modern ad serving - let’s take a look at why that’s the case, and some of the different combinations that can be used.
Ad placement strategies that combine contextual, behavioral, and audience targeting
The benefits of each ad targeting method are great, but why not combine them for a greater effect?
This is the approach that many media buying and selling strategies are moving towards.
While contextual and behavioral targeting have almost been locked in a sort of “competition” throughout their history in regards to ROAS efficacy, adjustments to privacy legislations have drastically shifted the status quo in recent years.
Rather than using datasets from each targeting method separately, marketers are beginning to see the use-case of combining data as an effective way to overcome privacy challenges.
Combining contextual and behavioral targeting
An alternative to the “contextual OR behavioral” narrative is the combination of both technologies in maximizing ad relevance, effectiveness, and overall user sentiment.
First-party data in all its forms can be considered the “digital gold” of the privacy-conscious internet, including first-party behavioral data.
Certain sectors, especially eCommerce and retail media advertising, are in a perfect position to capture consensual first-party user data surrounding online sales activity.
Similarly, audio and video platforms have complete access to how their users are interacting with their platforms, offering behavioral insights that can augment both video ad serving and audio ad serving.
This kind of behavioral data is great - but when combined with the ability to target a contextual niche - this information paints a bullseye on the content that advertisers want their ads to show up in.
For example, a retailer who has several hundred product pages featuring different computer components might notice that sponsored ads for discounts on graphics processor units perform exceptionally well on product pages featuring compatible motherboards.
The same retailer might reach out to its GPU suppliers and offer to sell them the newly identified inventory at a premium across their motherboard product listing pages.
In another example, a publisher who generates dozens of news articles per day might notice that all of their contextually tagged articles related to the term “recession” have been seeing a trend of increased traffic volumes, and use that data to create more content on the subject.
There are a myriad of ways for publishers and tech providers to innovate on traditional performance metrics and provide advertisers with new targeting options, like using live behavioral data to make real-time contextual recommendations to users.
There are also inherent advantages associated with combining both targeting techniques.
With 40% of users to 64% of users indicating that ads are “too aggressive” across different studies, behaviorally guided contextual ads can provide a viable option for combating modern ad fatigue in visitors.
Combining contextual and audience targeting
Not to sound like a broken record, but just in case you happened to miss the other mentions of it earlier, traditionally, the terms “behavioral targeting” and “audience targeting” have been used interchangeably in the ad serving space, largely due to Google’s naming convention.
Earlier in the article, we offered distinct descriptions for each term to account for the many shifts taking place surrounding how data is being used to target audience segments.
Research from across multiple studies, including a report on audience targeting published by Nielsen in 2022, have produced statistics which indicate the importance of the “new approach” to audience targeting in modern digital advertising.
(Source: Nielsen’s 2022 Annual Marketing Report)
This chart, pulled from the report, showcases that return on ad spend is up to a staggering 10x higher for campaigns that serve a higher volume of ads towards a well defined target audience.
A technique referred to as “multi-touch attribution” is offered as one effective method of blending contextual, behavioral, and audience targeting capabilities to achieve ad performance similar to that showcased in the study.
In the associated article, Nielsen also highlights that globally, 69% of marketers believe first-party data is critical for achieving effective audience targeting - but that 36% of marketers find that practical ways to implement and leverage the data is a major challenge.
While behavioral targeting via first-party data is a new and highly experimental venture for most marketers, contextual targeting’s ease of implementation really shines in addressing the first-party data conundrum.
Contextual management platforms offer an out-of-box solution for creating and managing contextual audience segments, allowing publishers to gather valuable first-party contextual data about their user base.
Furthermore, the contextual audience segments created by publishers can be showcased directly to their advertising partners through the combined use of self-serve advertising platforms, an increasingly popular way to alleviate friction from media sales.
Contextual vs behavioral advertising - which should you be more excited about?
Contextual advertising is an ad targeting technique that works by pairing ad creatives with contextually relevant content online, while behavioral advertising leverages historical first and third-party data to target users based on personal details and taken actions.
After covering all of the changes, challenges, and opportunities that each approach has to offer, including the combination of techniques to improve efficacy - which set of technology should you be more excited about?
It’s a popular answer that marketers looking for specifics don’t want to hear - but when it comes to the future of contextual vs behavioral advertising, it really does depend on the situation that each publisher and advertiser finds themselves in.
Publishers in the eCommerce and retail advertising sector, as well as just about any publisher with an extensive registered audience list may find that placing an emphasis on new ways to make use of first-party data to power a behavioral targeting strategy presents an exciting opportunity.
In contrast, publishers that aren’t able to tap into a deep repository of first-party data still have a lot to look forward to when it comes to contextual targeting - an industry which is forecasted to be worth $376 billion USD by 2027 in a prediction by IBM.
Because contextual targeting programs are built around straightforward, easy-to-implement systems that naturally align with shifting privacy legislations, it’s not surprising that the “hype” surrounding contextual advertising is slightly higher than that of behavioral in recent years.
With that said, ideally, publishers should be searching for ways to effectively combine both techniques to optimize the performance of their inventory offerings.
As contextual advertising becomes a norm in the privacy-first era, augmenting contextual intelligence with first-party behavioral signals is sure to become a differentiator that attracts the attention of advertisers and their ad budgets.
Offer cutting-edge contextual audience targeting to advertisers with a contextual management platform
We’ve covered a lot of information surrounding contextual and behavioral targeting, as well as the importance of audience segmentation for the future of the cookieless web.
Contextual management platforms are a new tool that allow publishers to create and manage contextual audience segments, as well as gather segmented performance data to guide revenue-lifting content creation.
When combined with a self-serve portal solution, publishers can showcase their contextual inventory to advertising partners, display rate cards, and even allow their advertisers to configure and pay for their ad campaigns completely on their own.
Contextual advertising is positioned to be a fundamental component of digital advertising in the coming years, and we hope you’ll be positioned to take advantage of all it has to offer.
The AdButler team has over two decades of experience in providing and configuring ad serving solutions for both publishers and advertisers.
Check out AdButler's contextual management platform for yourself.
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