Let's face it, the reign of the cookie is over. Google is cracking down on all behavioral targeting tactics in 2024 and consumers are demanding greater agency when it comes to the private information online. So, what does this mean for marketers whose media strategies have thus far, relied on third-party data?
With behavioral targeting on the outs, contextual targeting solutions are quickly gaining traction as the prime solution to target consumers in a fast-approaching cookie-less world.
In fact, between 2022 and 2030, contextual advertising spending worldwide is expected to grow 13.8 percent annually. And, with good reason: Contextual advertising is privacy-compliant, brand-safe and puts the focus on targeting consumers based on their interest, and not their behavior online.
In this blog post, we will breakdown contextual advertising: what it is, benefits, examples and how you can leverage contextual ads to accelerate your campaign KPIs and achieve growth for your business in the next era of digital advertising.
What is Contextual Advertising?
In the simplest terms, contextual advertising is advertising on a web page based on the page's content—for example, a Samsung ad next to an article about the best Black Friday deals or an ad for Starbucks next to the best cappuccino recipe. Contextual targeting uses the information already present on the page to match a brand's ad to the content the user is consuming.
Why is Contextual Advertising Important?
Contextual targeting has been around for a long time. However, with the increasing advancement in contextual capabilities, coupled with the recent privacy revolution and the inevitable demise of cookies, it has had a profound resurgence in the ad tech industry. Contextual intelligence will continue to evolve and will become an every day part of strategy for many of the industry players in the future.
"The hype around contextual advertising is warranted, the proof points are there in performance, scale and stability in a market which has been tossed and turned by changing data privacy” - Peter Wallace, Managing Director EMEA, GumGum.
Benefits of Contextual Advertising
According to Statista, 49% of media professionals are worried about future restrictions around the use of cookies, making contextual advertising a very promising alternative to behavioral advertising. Here are some of the reasons why:
Reach the Right People in the Right Moments
With contextual targeting, advertisers can reach users based on the content they are browsing in real time, rather then their historical browsing habits. The content a user is viewing at any given time signals interest and intent. As such, this targeting tactic allows you to reach users when they are in a receptive frame of mind.
Protect Consumer Privacy
Now more than ever, audiences do not wish to be tracked online. Unlike behavioral advertising, contextual advertising does not rely on third party cookie data - it instead places ads within relevant content that users are already interested in and engaging with.
Thus, it protects consumer privacy and ensures that users do not feel like they are being followed or watched online.
Target Niche Audiences
With contextual, an advertiser can target by topic or use a collection of keywords for more precision, which can be as broad or granular as desired. This means that advertisers can home in on the specific audience niche they want to reach.
Access Metrics in Real Time
Contextual advertising campaigns are served programmatically, which means that advertisers can review real-time metrics to optimize as needed. With the right programmatic partner, ads can be verified and served on relevant domains pre- and in-flight.
Optimizing campaigns in real-time strengthens a live campaign's performance, which ultimately improves the outcome. And, being able to make in-flight changes means that advertisers are able to make adjustments that may reduce ad dollar waste.
Ensure Brand Safety
It’s not just legal safety that brands need to worry about, but the safety of their reputation too. And for behavioral advertisers, this has been difficult to maintain in some cases. More and more, brands are discovering their advertisements in non-brand safe environments, like adult or extremist content. But this is the risk of placing advertisements based on your user’s behavior alone.
With contextual targeting, however, the web page on which your ad will display is the heart of the campaign. You specify the topics, subtopics, and keywords, making it less likely your ads are going to follow a user to an environment where they don’t expect (or want) to see advertisements—including where you don’t want them to appear.
Build Brand Affinity
Consumers today care about where they shop, and they are increasingly conscious of the environments where brands choose to advertise. Contextual advertising offers a brand safe advertising environment while also building brand affinity, because ads are served based on content a user is browsing.
Advertisers are able to connect with consumers who are receptive to purpose-driven messaging and looking specifically for brands that align with key values.
Contextual Advertising in 3 Simple Steps:
To further understand contextual advertising, you can think of it in three simple steps:
- A contextual intelligence platform, backed by Artificial Intelligence technology, such as computer vision and natural language processing scans the information on a web page, for example, the food and kitchen utensils in a photo that accompanies a recipe on a blog.
- This information is then translated into actionable insights to target consumers.
- The contextual intelligence platform then places contextually relevant ad units within the web page that align with the user's behavior and the content they are consuming.
A Quick Break Down of Contextual Advertising Components
Now that we've covered the basic steps of contextual targeting, it's time to take a closer look at the components that work together to make contextual advertising possible.
Components:
ARTIFICIAL INTELLIGENCE
When humans teach machines to think and act like them, that is termed Artificial Intelligence (AI). An entity created by humans that is capable of making rational and humane decisions and performing tasks without being explicitly instructed to do so is one that executes and has Artificial Intelligence.
There are multiple examples of AI everywhere we turn, from self-driving cars to spam filters, navigation apps to facial recognition software to online banking apps and more.
MACHINE LEARNING
Machine Learning algorithms look for patterns within massive amounts of data, including numbers, words, images, clicks etc. This data can either be digitally stored or can be passed into a machine-learning algorithm.
For example, when using Netflix or Hulu, each platform is collecting as much data about you as possible—what genres you like watching, what links you are clicking, which statuses you are reacting to—and using machine learning to make a highly educated guess about what you might want next. This same thing occurs in advertising, where Machine Learning makes educated predictions about the ads that consumers want to see online.
COMPUTER VISION
Computer vision is a field of computer science that works on enabling computers to see, identify and process images and video and then provide appropriate output.
There are a number of technological and societal drivers that perpetuate the growth of visual content in the digital landscape. Between the increase in the number of embedded cameras in the market, and the proliferation of social media platforms that rely on images to communicate concepts and information quickly, a visual strategy is becoming essential to advertisers seeking to harness the power of visual content.
Image recognition technology allows advertisers to comb efficiently through millions of existing images and videos, then place contextually relevant marketing adjacent to the right ones. And it helps avoid brand-unsafe exposures by giving advertisers strict control over where their content appears.
In other words, computer vision empowers marketers to use contextual targeting with unprecedented ease and security, which in turn allows them to reach-and capture-users well outside of their traditional ecosystems.
As the digital landscape fragments into endless niches within niches, these tools will prove essential to an impactful marketing strategy.
NATURAL LANGUAGE PROCESSING (NLP)
Natural Language Processing, usually shortened as NLP, is a branch of artificial intelligence that deals with the interaction between computers and humans using the natural language. The ultimate objective of NLP is to read, decipher, understand and make sense of the human languages in a manner that is valuable.
NLP applications are all around us - for example, chatbots and virtual assistants, applications such as Grammarly, text extraction and autocorrect make use of NLP algorithms to be successful. NLP is an integral part of contextual advertising through it, not only can the words but also the contexts and sentiment of a webpage be understood, allowing brand messaging to be placed within pages where it will really make a difference to a consumer.
As the components work together, contextual advertising helps advertisers derive the deep understanding of a page in order to place ad messaging in effective, suitable environments.
The Difference Between Contextual and Behavioral Targeting
With cookie targeting on the decline, consumer privacy concerns on the rise and new media environments gaining traction, marketers are weighing up the best solutions to connect with consumers online. This is sparking a great debate: Contextual vs behavioral advertising - which is better?
Now that we've established a clear understanding of contextual targeting, let's take a look at what behavioral targeting entails. Behavioral targeting, also known as audience targeting, serves ads based on a user's web-browsing behavior.
For example, if a user searched for leather handbags, it can be determined that this user is interested in hand bags and can now be targeted with behavioral ads.
Behavioral advertising relies on the user’s past actions to track user behavior and understand the kind of purchase they are likely to make. If a user performs an action, online or offline, this information is tracked and used to serve them ads based on their behavior.
Now the question, what is the main difference between these two methods?
In the simplest terms, behavioral advertising makes uses of a consumer's personal data to serve ads to prospective buyers, whereas contextual advertising places ads in environments which align with user interests with no reliance on personal data.
Undoubtedly, both contextual targeting and behavioral targeting are effective advertising means, but with third-party cookies phasing out and consumers demanding more privacy, advertisers are left with no choice but to explore more sustainable means to reach consumers online.
This is causing more advertisers to replace or augment their behavioral advertising strategies with more contextual ones. With advancements in contextual technology, more nuanced targeting techniques and a grip on consumer mindset, contextual advertising is quickly becoming a leading solution to replace cookies in digital advertising.
How to Choose the Right Contextual Technology
When looking for a contextual advertising partner, where do you start? Before selecting a partner, it is important to understand what their contextual targeting involves with the following questions:
- Beyond text, do you consider video, audio, images for brand safety and contextual classification?
- Do you have any research that validates that your contextual advertising approach is working?
- Do you own your technology or are you tapping into someone else's?
- Can your technology understand the full context of a page?
- Are you using keywords to identify context?
- Is the technology accredited from a third-party?
GumGum's Industry-Leading Contextual Targeting Solution, Verity™
As the leader in contextual intelligence, GumGum has been effectively executing contextual ad campaigns for more than a decade. GumGum's contextual intelligence technology, Verity™, scans text, image, audio and video to derive human-like understandings. These insights are used by the buy-side and sell-side for executing contextually relevant and brand-suitable advertising.
The Media Rating Council (MRC) has granted Verity™ content-level accreditation for contextual analysis, brand safety and suitability across CTV, desktop and mobile web environments. This accreditation makes GumGum the first ad tech provider to show that its content-level contextual capabilities for CTV meet the MRC’s demanding requirements.
Verity™’s accreditation provides advertisers with a contextual and brand safety/suitability solution that can review all available signals (text, video, imagery, and audio) at a time when CTV advertising is expected to more than double by 2026 (eMarketer).
While most vendors can only provide video analysis at the property-level—meaning they focus on a video’s metadata, such as the description that’s tied to the video—Verity™ analyzes at the content-level. This means Verity™ examines a video on a frame-by-frame basis to gain a more comprehensive, human-like understanding of the content to then target relevant users in a privacy-forward manner.
This is becoming increasingly more important as CTV continues to grow in adoption, cookies are deprecated and privacy concerns mount.
Likewise, it enables CTV publishers to monetize their videos on a more granular level, improving scale and avoiding the blocking of content that’s actually safe.
The Proven Success of Verity™
In new research, contextual ads were found to be more accurate and cost-efficient vs behavioral targeting, with GumGum Verity™ coming out on top!
In partnership with dentsu, GumGum conducted a first-of-its-kind research study that compared the cost-effectiveness and accuracy of behavioral targeting versus contextual targeting.
Dentsu ran live campaigns utilizing both contextual and behavioral targeting for four of its brand clients; cosmetics retailer Sephora, a major technology company, a big box retailer and a direct-to-consumer retailer.
The study served 1 million impressions split across five different ad lines and found that contextual targeting was more efficient than behavioral targeting in cost-per-click (CPC) and cost-per-viewable impression (vCPM). The study also found that Verity™ was more accurate than other top contextual vendors.
Verity™ proved to be:
