Let’s face it, the reign of the cookie is over. Apple and Google are cracking down on all behavioral targeting tactics and consumers are demanding greater agency when it comes to the private information online. So, what does this mean for marketers whose media strategies rely on third-party data? With behavioral targeting on the outs, contextual advertising solutions are quickly gaining traction worldwide. And, with good reason: Contextual solutions effectively target consumers based on the content they are consuming, without any reliance on third-party data.
Let’s break down contextual advertising: Its uses, implementations and potential in a fast-approaching cookie-less world.
What is Contextual Advertising?
In the simplest terms, contextual advertising is advertising on a web page based on the page’s content. For example, an ad for Sephora in an article about the best makeup brushes, 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.
Not surprisingly, the global interest in contextual targeting is on the rise, with analysts predicting that marketers will break hard for contextual advertising, adding billions of dollars to those budgets by 2027. The fact of the matter is that context is everywhere. With users constantly churning out diverse and far-reaching content, it has become essential to understand what it all means — the bias, tone, the neutrality, the satire all needs to be determined in order to target users effectively.
Contextual Advertising in 3 Simple Steps:
1.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 fashion model’s hair in an InStyle photo.
2.This information is then translated into actionable insights to target consumers.
3.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.
Breaking Down Contextual Advertising Components
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 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 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. Leveraging computer vision as a tool, marketers are able to capture relevant visual content in scalable and brand-safe ways.
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
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 because through it, not only are the words but the context and sentiment of a webpage can also be understood, so brand messaging can be placed within pages where it will really make a difference to a consumer.
We can say that Artificial Intelligence is almost synonymous with Machine Learning. Computer Vision and Natural Language Processing operate using Machine Learning. Many providers in the contextual space do not actually use Machine Learning, but actually make use of keyword based algorithms, while GumGum makes use of Machine Learning to derive the deep understanding of a page in order to place ad messaging in effective, suitable environments.
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 a future every day part of strategy for many of the industry players right now.
"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.
Contextual targeting has grown by leaps and bounds - now, it goes beyond just simple keywords to a far deeper understanding of the page, allowing advertisers to target consumers based on their interests, without limiting brand scale or reach. Plus, by placing ad messaging in brand suitable environments, contextual targeting also protects brands from unsafe content online.
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 ask contextual providers 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 for from a third-party?
GumGum’s Industry-Leading Contextual Solution, Verity™
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.
As the leader in contextual intelligence, GumGum has been effectively executing contextual ad campaigns for more than a decade. GumGum’s contextual intelligence engine, VerityTM helps brands to home in on page-level content to amplify ad messaging within the most relevant content. It also achieves brand suitability, avoiding unsafe content without restricting campaign reach. And, it is a future-proof targeting method that is not reliant on any third-party data.
Humans look at thousands of pages across the internet to classify the context, and identify any potential threats present within the content. This data is then fed into Verity™ to start the machine learning process. Once the training is done, Verity™ begins to make predictions or decisions.
GumGum’s Verity™ for instance, focuses on broad categorization and scans for tone instead of relying on keyword searching. Verity™ sets itself apart from other contextual analysis tools because it combines natural language processing with computer vision technology to execute a multi-layered reading process.
First, it finds the meat of the article on the page, which means differentiating it from any sidebar and header ads. Next, it parses the body text, headlines, image captions with natural language processing; at the same time, it uses computer vision to parse the main visuals.
GumGum’s CTO, Ken Weiner, explains that Verity™ is often the only entity that reads a page before a brand places an ad on it. Since ad spaces get bought and filled in a mere milliseconds, humans simply can’t work fast enough to keep up. This process is wholly algorithmic. This is where Verity™ comes in. Verity blends its textual and visual analysis into one cohesive report, which it sends off to an adserver. The adserver then automatically assesses whether Verity’s report on a given page matches its advertiser’s campaign criteria.
Contextual Performance Research & Validation
GumGum x dentsu
A recent dentsu study, overseen by a third-party researcher, demonstrated that Verity™ is not only more accurate than other contextual vendors, but that contextual targeting is more efficient than behavioral alternatives.
Verity™ was 1.7x more accurate than other contextual intelligence vendors
Verity™ had 48% lower cost per click compared to behavioral targeting
Verity™ had 41% lower cost per viewable impressions than behaviorally targeted ads
GumGum x Spark Neuro
GumGum partnered with neuroanalytics company, SPARK Neuro to prove that contextually relevant messaging is a key driver in advertising success. In this study, participants’ brain activity, eye -movement and physiological response were noted for both contextual and non-contextual ads.
Contextually relevant ads generated 43% more neural engagement than contextually irrelevant ads.
Contextual relevant ads were 2.2X more memorable than contextually irrelevant ads.
Contextually relevant ads were more engaging than contextually irrelevant ads and were actually 10% more engaging than article content overall.
Contextually relevant ads inspired greater purchase intent across the board.
The Big Debate: Contextual vs. Behavioral Targeting
Contextual targeting is effective, accurate & better than behavioral targeting:
Targets users based on current interest
When a person is browsing content about a specific topic, it signals their intent at that moment. Instead of targeting based on their past actions, like behavioral targeting does.
Allows brands to target nice contexts
A brand can choose to target by a general topic, or a collection of phrases for more precise targeting. Instead of targeting “coffee,” you can use input phrases like “organic coffee beans, or “dark beans” to narrow the context even further.
It doesn’t use third-party cookies
Because it doesn’t collect or use information about users, it protects user privacy. Instead, it leverages the context next to which the ad appears.
Optimizes in real time
Ability to verify if ads are served on relevant domains pre- and in-flight. This gives brands the ability to revise selected topics and phrases in real-time to improve the performance.
The Data Privacy Crisis
It is undeniable that consumers are constantly being tracked online: cookies track their interests, locations, spending habits, wish lists and so much more is channeled into some mega database by the omniscient, all-knowing internet. Or at least, that is what it feels like. But the big question is, what does this mean for consumer privacy? And, what is being done to protect personal data?
With the complexity and sophistication of algorithms increasing over time, it is now possible to achieve contextual relevance and scale in the wake of a privacy revolution. With recent advancements, contextual intelligence technologies can now achieve content-level understanding of digital content, looking at all available signals such as text, images, and audio. Deep learning systems use Neural Networks that can make human-like predictions as they consume digital data. This then helps to place brand messaging in contextually relevant and suitable environments online.
Where is Contextual Advertising Now?
GumGum has been on video for quite some time, and is taking video signals and using them to create contextual analysis and make it available to advertisers. As video URLs normally don’t flow through the bid stream, it is imperative to get connected with the publishers’ content management systems and make video contextual signals available to marketers.
To learn more, check out GumGum’s new online magazine, Contextual Insider.