Historically, digital advertisers relied heavily on behavioral targeting - powered by browser cookies This tactic misses the mark for real-time content-consumption relevance to what the user is reading or watching. Users are shown ad content that may not be relevant or applicable in the context or at the time they see it. This is where contextual advertising improves impact.
Research shows that 82% of people actually want to see ads online, as long as they’re relevant. They want ads that are interesting and relatable, appealing to their needs and wants. That same study shows that people are more likely to feel indifferent when exposed to irrelevant ads, suggesting indifference results in reduced attention.
Advertisers will need to make a decision about targeting types and data pools when the cookie is fully phased out– especially for landing pages and ecommerce, where cookies are used to measure funnel performance, beyond just click-through rates.
Contextual marketing is the best option for privacy protections where reliable tools like retargeting aren’t an option.
This guide is an introduction to contextual advertising and a roadmap for how to navigate a cookieless future.
Table of contents
What is contextual advertising?
Contextual advertising is a form of targeted, programmatic advertising that shows ads based on the content of a specific web page, video, social media feed, or any other supported location in a web browser.
Contextual advertising is based on the environment in which the audience is browsing. This is done through the analysis of keywords and content on the web page itself, rather than user browsing behavior. This kind of complexity requires machine learning and algorithms, but is transparent and simple.
Focusing on where the user currently is instead of where they’ve been ensures that the advertisement is related to the content that the user is consuming at the time it's shown.
This process is, of course, facilitated by contextual targeting.
What is contextual targeting?
Contextual targeting is based on the type of content on a website, and the implied intent or interest of the reader based on that content. Ads are selected and served by an automated system based on the context of what the user is consuming.
Contextual targeting is a form of programmatic advertising, typically done through an ad network, which segments contextual ads and targets groups based on parameters like keywords or website topic.
Behavioral advertising is based on past browsing behavior.
Contextual advertising is based on the content of a web page or piece of content.
How does contextual advertising work?
Contextual advertising requires the use of a demand side platform (DSP) that will place ads on webpages or other relevant content that meet your contextual targeting parameters.
Demand side platforms are used by advertisers to buy mobile, CTV and video ads from a marketplace on which publishers list advertising inventory. They allow for the management of advertising across many real-time bidding networks. Examples of DSPs are The Trade Desk, MediaMath, Vivant, Xandr, and Yahoo.
Typically, contextual ad placement is a three-step process that looks something like this.
Step 1: Choose your contextual targeting parameters
Like behavioral targeting, the first step is to select the targeting parameters.
This might include targeting based on:
- Keywords. Precise targeting for specific keywords or phrases in the web pages that you will eventually target.
- Topics. Broad categories in which your campaign and ad creative would fit. For example: back-to-school shopping.
- Negative keywords. These can be used to specify which keyword you do not want your ads to appear alongside, helping you to match your ad to website content. Negative keywords are a great tool to help ensure brand suitability for your campaigns.
- Negative topics. These are the same as negative keywords, but for full topics or subtopics. \
- Location and language. You can specify the locations and languages you would like to target. This is particularly important if you are promoting products or services that are only supported in a specific region or in a specific language.
Each parameter can be layered to create a highly targeted audience.
Advertisers also have the ability to set their display settings to be either specific or broad in reach. Broad targeting uses topics and subtopics. Specific targeting relies on specific keywords.
Step 2: The DSP analyzes the pages in its network
Once the contextual targeting parameters are set and the campaign is live, the DSP analyzes each page in its network to try and match your ad with the most relevant content available.
This process involves analyzing:
- Page structure
- Link structure
- Keyword usage
Once content has been found that matches your topic or keyword, your display ads will become eligible to appear. A real-time auction occurs - similar to what happens through Google Adsense - and the winning bid will have their ad shown to users.
This whole process happens programmatically, in milliseconds, billions of times per day.
Step 3: Relevant pages and content are identified and the ad is placed
Once the winning bid has been determined, your ad will appear in the designated locations on the website or content.
Here’s an example of how that might look. Note the contextual synergy between the ad creative and the content of the page.
Step 4: Monitor your results and reframe your parameters as needed
Like with any advertising campaign, there is a certain level of iteration required in contextual targeting.
Advertisers should closely monitor campaign results, both from a top-of-funnel and opportunity-creation perspective, and tweak their contextual targeting parameters as needed.
TIP: Using negative keywords and categories will help you avoid broad-based targeting that may only be tangentially related to your products or services.
Misconceptions about contextual advertising
Contextual targeting is a powerful overall strategy because it works with first-party data and privacy-safe third-party data. At Peer39, we use data from our marketplace partners to go beyond simple keywords and phrases.
By analyzing every element of a page, we can classify a page in real time. We look at everything from the url to the page layout, and the type of page, which includes the section on each page, page quality and page signals, language, text to ad ration, video size, comments, and social media.
We use the power of artificial intelligence, natural language processing, and machine learning algorithms and have perfected our approach since our humble start in 2008. The brand safety tactics that we've perfected allow us to ensure brand suitability of a page or advertiser.
Is it less effective than cookie-based behavioral targeting?
While looking at purchase history can offer some insight, messaging in the moment is far more useful. With contextual advertising, you can target your audience based on the content the reader is currently viewing.
Contextual targeting works in real time. It also results in higher engagement. We take into account intent during that moment, as in, if someone is searching for car reviews, they're likely doing so with an intent to purchase. That's the perfect context for showing an ad related to that vehicle, rather than for a pair of shoes they searched for last week.
Is it less cost-effective than cookie-based behavioral targeting?
Cookie-based behavioral targeting was seen as cost efficient–that is, until contextual advertsing arrived.
The overall cost of ad impressions is lower than behavioral ad impressions. The eCPM, or effective cost per thousand impressions, as well as CPC or vCPM are all less than cookie-based advertising.
A study with Dentsu Aegis Network showed that the eCPM for contextual ads was 36% less than it was for similar third-party cookie ads. CPC was even more extreme at a cost of 48% less than cookie-based targeting was. Contextual vCPM ended up costing advertisers 41% less than it would have for using cookies.
Are all contextual targeting companies the same?
The short answer is no, no two targeting companies are the same. The reason is for their technology, which is typically an AI-powered ad targeting algorithm combined with a user interface that makes it easy to filter and target with accuracy.
Any marketer seeking out a contextual targeting company should look for years of expertise and brand authority in contextual ad placement. With well-worn machine learning in place and a well-trained staff, marketers can expect companies like our to drive measurable and consistent results that only improve the quality of their spending over time.
Should you keep using cookies for now?
While you may think it's important to run out the clock with cookies, that's not the best use of your time. Now is the time to test out different strategies, learn about contextual advertising, and leverage your learning from cookie-based targeting to guide your future strategy.
Everything you've learned up to now has value, but you must adjust if you don't want to lose momentum.
Contextual targeting vs. audience targeting
Before switching campaign strategies to contextual targeting, it’s important to understand how this approach differs from traditional audience targeting. As cookies are phased out, this is information that many advertisers will need to ensure continuity in their ad campaigns.
Here is a breakdown of what audience targeting is, how it differs from contextual targeting, and what the benefits are from this cookie-free approach.
What is audience targeting?
Audience targeting is a technique widely used by digital marketers that ensures their ads are only shown to the people or audiences most likely to show interest in the product or service that they are selling.
This is accomplished by separating customers into segments based on variables like interests, demographics or past behavior.
In general, there are three types of audience targeting than an advertiser might use:
- Demographic targeting segments audiences based on age, average income, interests, location or gender.
- Psychographic targeting segments audiences based on their interests, activities, opinions, values, personality traits or lifestyle choices.
- Behavioral targeting segments audiences based on ‘digitally observed behaviors” like websites they’ve visited, other ads they’ve clicked on or items they’ve purchased in the past.
The source of this data is another key differentiating factor between audience targeting and contextual targeting.
In audience targeting, data typically comes from one of four sources:
- Zero-party data
- First-party data
- Second-party data
- Third-party data
For optimization of your audience targeting, it’s vital to understand the value of each.
Zero-party data is data that people deliberately and proactively give to a brand, such as communication preferences, on-site quizzes such as those that guide customers towards products that meet their needs, and polls.
First party data is that which companies collect themselves through their typical interactions with prospects and customers.
Some examples include:
- Past customers
- Social media followers
- Email newsletter subscribers
- Website visitors
Each of these pools of contacts can be used to create segmented audiences for advertising campaigns. By nature, first party data sets are usually quite small, and so are typically layered with second- and third-party data to expand the audience size.
Second-party data is someone else’s first-party data that advertisers acquire for their audience targeting. It may be acquired from a contact in the industry, shared through a partnership or purchased as part of a data exchange.
This type of data is used the least often, but it can be helpful if you’re looking to broaden your data set while maintaining quality and inclusivity.
Third party data is the most commonly used data for audience targeting. It’s collected, segmented and sold by data providers through public data exchanges, DSPs or data management platforms (DMPs).
This is the data that’s used in the built-in targeting parameters you’re likely familiar with in Google AdWords, Google Display Network, or social media advertising. It’s used to rapidly scale an advertising campaign, allowing you to extend to whole new groups of users.
At this point, if you’re wondering how all of this data is collected, you’ve hit on the key difference between audience targeting and contextual targeting.
Audience targeting is, by and large, done using cookies.
Contextual targeting, on the other hand, is cookie-free and is generated using page-level analysis of content and keywords.
Both methods show ads based on user interest. But that interest is determined in very different ways.
Benefits and disadvantages of audience targeting
A primary benefit of audience targeting - and especially the behavioral targeting subset - is that it allows advertisers to keep their brand top of mind after a visitor has left their website.
This is accomplished through the use of re-marketing - powered by cookies - that helps to give your content and brand staying power in the mind of the user. It also extends your presence beyond a single session, and gives multiple changes for a user to covert.
Of course, the nature by which this re-marketing occurs is increasingly being seen as a major problem of audience targeting. Privacy legislation like GDPR is making it increasingly difficult for advertisers to effectively target using cookie-based audiences, thus shrinking the size and scope of audience pools.
The disadvantages to audience targeting in 2022 and beyond are numerous.
- Ads may be seen as intrusive, annoying, or even creepy
- Ads are placed alongside content that doesn’t necessarily align with the ad message
- Assuming that users will still be in buying mode when they leave your website
- Lower clickthrough and conversion rates due to a lack of context
- Being costly and requires a lot of upfront strategy and testing to ensure that your targeting and frequency capping is optimized
- Failing to resonate with your targeting audience, especially if there is a lack of audience or market knowledge
- Relying on a shrinking inventory of audience data and publishers due to many users and websites shifting away from cookie-powered digital advertising
450m+ unique web pages classified daily
150k+ CTV and mobile apps classified daily
1000+ contextual categories across web, mobile and CTV
Why use contextual targeting?
To understand the core benefits of contextual targeting, it’s important to understand that audience targeting is based solely on the user’s past behavior, and does not take into account that ads are viewed within an environment with other dimensions or signals.
When advertisers shift their focus to understanding the immediate context and mindset of the people they are trying to reach - rather than focusing on what they’ve done in the past - the result is alignment and relevance between ads and context.
Context is a key factor for relevance and relevance results in increased attention. That’s great for the end user, of course. But what’s in it for the advertiser?
Research shows that when attention and alignment are part of the advertising strategy, ROI can be improved by up to 30%.
The reasons for that significant boost in ROI related back to the core benefits offered by contextual advertising.
- Contextual targeting is faster and easier to implement. It doesn’t require a large amount of owned behavioral data. Instead, you can leverage DSPs and contextual platforms to find targeting opportunities. This allows you to connect with a wider range of potential users based on category and keyword targeting.
- Offering more control over brand safety. Brand safety and suitability are major factors for advertisers today, especially with the growth of misinformation, disinformation and fake news. It can be very difficult to maintain complete brand safety and suitability when targeting behavioral audiences. Contextual targeting, on the other hand, gives you complete control over the web pages, topics, subtopics and keywords that your ads appear alongside. This allows you to closely tailor the types of content and websites that you want to be affiliated with as a business.
- Showing relevant ads in the right context. Users respond better to ads and offerings that are related to what they’re reading about or watching at the moment. Contextual ads increase the likelihood that a user will respond to your ads at a specific point in time. This has positive implications for direct response and conversions, which isn’t always the case when targeting based on past behavior.
- Future-proofing your ad campaigns. Privacy regulations like GDPR are quickly becoming the norm around the world. Companies that rely heavily on programmatic advertisers will need to quickly find alternatives to cookie-based advertising. Contextual targeting has emerged as one of the most viable and effective options.
- The ability to translate behavioral segments into contextual categories. The good news is that all the marketing strategy and testing you’ve done with behavioral targeting won't go to waste. If you’ve been segmenting audiences and tracking from demographics and content consumption, you should be able to map those profiles to relevant contextual categories. You don’t need to hit the reset button to transfer to a cookie-free and future-proofed alternative.
Contextual advertising is a good option for advertisers concerned about the death of the cookie. Unlike behavioral targeting segments, contextual in-market categories are deterministic, consistent, and enable advertisers to reach a reader in the moment of consuming content.
If you’re just starting out in the world of contextual advertising, below are examples of contextual targeting strategies that you can use to plan your own campaigns.
Sample contextual targeting strategies
Contextual targeting is a powerful solution for advertisers looking for an alternative to cookie-based ads. It’s important to explore and understand the different types of contextual targeting and how they can be applied to your campaigns.
Contextual targeting for automotive buyers
The automotive sector provides many successful examples of contextual ads. We know that 92% of car buyers prefer to do a majority of their research online. That includes searches that result in a shopper visiting web pages makes and models (60% of shoppers begin with multiple vehicle options), pricing, trade-in value, car histories, credit score, warranties, and financing.
This is all incredibly rich content for advertisers to reach users in the moment of conducting purchases related to their research.
One of the strongest indicators of purchase intent is the type of content readers engage with. In the automotive example above, the reader would be considered in-market.
Below is a table outlining the different in-market contextual categories that can be targeted using a platform like Peer39.
Weather-triggered contextual advertising
Weather is the most consistent and impactful external environmental driver of demand in the consumer economy. This is true in New York, London, or small towns. Understanding and acting on this context has been consistently and measurably effective.
When combined with promotions or layered on top of other types of audience targeting, an observable “synergistic effect” occurs, and the efficacy of the targeting is boosted.
Planalytics is one example of a data provider who offers contextual weather data. Businesses leverage Planalytics’ product demand index for ideal times and locations for optimization of advertising in-market categories.
They do this by combining weather analytics with historical purchase data and contextual categories. This creates a clear, data-driven connection between weather and purchasing behaviors, taking into account all unique interactions and online behavior that occur across regions and time periods for a specific product.
Leverage weather data AND past product purchase data by region to predict how specific market segments will purchase during existing or future weather events.
One of Planalytics’ mass merchant clients saw an average sales increase of 8% when they used weather analytics to target specific markets with specific products. In another example, a client used weather analytics to select audiences and saw ROAS increases that were 3 to 4 times the gains captured with traditional cookie-based behavioral ads.
Contextual targeting for political advertising
As the amount of news content increases, it's become difficult for advertisers to appear next to relevant, suitable, and accurate content that complements their brands and products or services. There's the growing issue of misinformation, disinformation and content that is generally not suitable for brands to advertise next to. This gives advertisers the opportunity to leverage impressions on high-quality content that gets to the right audience.
Advertisers likely want to run ads alongside content that aligns with their ideals and mission as a way to reinforce their message to like-minded individuals. At the same time, they likely want to avoid content that conflicts with those ideals.
So how do you manage that delicate balancing act? We recommend a layered approach to contextual targeting, brand safety and suitability.
This layered approach should include:
- Contextual targeting to bolster your messaging with ad placements alongside high-quality content that complements what you want to say.
- Negative targeting tactics to avoid ad placements with content that doesn’t align to your message and values.
- Contextual categories to reach the right audiences with interest categories that fit their categories.
- Avoiding misformation by layering NewsGuard categories - as an example - ensure you target only credible, high-quality news sites.
Here’s an example of what that layered approach might look like using a platform like Peer39.
Looking to a cookieless future for advertisers
The cookieless future pressures advertisers to find alternative tactics. User data and browsing history will be protected by laws and technological changes. Availability and scale in behavioral targeting segments will continue to shrink and become more expensive.
To combat this, advertisers will begin to use a combination of first-party audience data from publishers who have the permission and the right to share information alongside identity solutions. These user-specific methods aren't able to achieve the same scale that third-party cookies offer today.
Reaching your audience with precision and scale in a world that is cookie-free and privacy-regulated requires a shift in mindset about the environment of the ad versus the behavior of the user.
As new channels like connected TV (CTV) emerge, instead of the old approach of tracking user behavior and targeting ads based on that activity, the new mindset is understanding the environment, focusing on what the user is watching, along with how, where, and when they are watching.
Innovative companies are creating data models for things like CTV advertising that are not tied to users or associated with cookies. These unconventional data sets are becoming extremely valuable to advertisers as targeting categories.
Many of these companies may lack the knowledge, technology or establish ad tech relationships to enable scale and access for advertisers. And the complexity involved presents an enormous hurdle for even veteran ad tech companies, let alone newcomers.
As the end of the cookie gets closer, it becomes increasingly more important to move quickly and provide advertisers access to existing and new companies with cookieless, pre-bid contextual categories that meet the increasing demand.
Peer39 is uniquely suited to meeting this demand, and has already launched the Contextual Data Marketplace™ to help advertisers access these new and unique data sets within their DSPs.
Peer39: Contextual Data Marketplace brings broad access to cookieless data sources
Peer39’s Contextual Data Marketplace is a first-of-its kind solution created to give advertisers direct access to innovative cookieless pre-bid data sources.
In the Contextual Data Marketplace (CDM), advertisers can explore and discover innovative data providers with cookieless categories that scale. Partners include Adloox, Burbio, DeepSee, Experian, GDI, Goldfish Ads, NewsGuard, Planalytics, Polk, Reticle, and SocialContext.
Some of these categories include targeting around:
- Automobile owners and in-market shoppers
- Credibility ratings
- Demand-sensing analytics
- Emotional targeting based on words
- News trustworthiness
- Purchase affinity
- Quality targeting
- Weather analytics
These categories and data sets are available in all major DSPs integrated with Peer39, and can be easily discovered and activated by advertisers.
As we all shift to a cookieless future, contextual targeting tools like Peer39’s CDM offer a range of benefits for advertisers, data providers, and DSPs.
Benefits for advertisers
The Contextual Data Marketplace provides advertisers with access to innovative pre-bid, contextual categories at scale.
- More effective ad campaigns
- Increased support and access to cookieless data at scale
- Sustainable, future-proofed targeting strategies for a post-cookie world
- Enhanced relevance and scale for their ad campaigns at a fraction of the cost of audience-based targeting
- Access to new types of data such as cookie-free demographics, purchase affinity, emotional targeting based on words, quality targeting and more at scale
- Access to planning tools and data sets that enable planning and testing for sustainable targeting tactics
- Increase accuracy in reaching your target audience in moment when they are most receptive to the message
Benefits for data providers
The Contextual Data Marketplace is a toolset used by programmatic buyers, giving data providers access to DSPs that they would otherwise have trouble accessing. Think of the CDM as the pipeline between data providers and the DSPs. This allows smaller or newer data providers to access DSPs much faster, and allows advertisers to access those unique, contextual data sets when needed.
- Dramatically reduced time-to-market in the ad industry
- Reduced costs and business development
- Integrations with a toolset that includes a ready-made, globally-distributed technology infrastructure
- A gateway to reach advertisers where they buy programmatic media
- Industry-leading classification tools for the highest scale and accuracy
- A toolset built on technology infrastructure developed by industry-leading contextual targeting pioneers
Benefits for DSPs
The CDM provides DSPs with access to more data providers without the cost and time required from unique integrations. Whether you want custom audiences or pre-built automation, our system allows for the optimal user experience.
As such, DSPs benefit from:
- Strengthening their data ecosystem
- Easily supporting new contextual data providers without the need to use engineers resources to onboard
- Supporting localized or niche data providers quickly and easily
In other words, the CDM allows DSPs to quickly onboard new contextual data partners, making those categories and audiences available to their users on much shorter timelines - and for much less cost - than previously possible.
Test drive the Contextual Data Marketplace today
Interested in taking a look at the data sets and planning tools offered in the Contextual Data Marketplace?
We invite you to create a free account today. This will give you visibility into all of the data partners currently in the CDN, as well as tools to help you plan for a cookieless future. Our powerful tool allows you to respect user privacy while giving you everything you need for the future of online advertising.
Fill out the form below and someone from our team will reach out to you ASAP to give you a hands-on tour of our contextual data marketplace to maximize your ad placements instantly.