Share on facebook
Share on linkedin
Share on twitter
Share on facebook
Share on linkedin
Share on twitter

What Is Contextual Targeting and Why It Matters

Contextual targeting is back, and brands are increasingly using it to find and reach today’s digital customers. According to a recent report, the global contextual advertising market is expected to reach over US$166.2 billion by 2025. So, what exactly is contextual targeting, why does it matter, and how can it enhance your advertising efforts? 


What Is Contextual Targeting?

Contextual targeting is the practice of displaying ads in the most appropriate context, for example, placing an ad for a beauty product on a fashion website or an ad for running shoes on a health and fitness forum. 

Contextual advertising is nothing new. In fact, it has been used in TV and print for years. However, it is now having a resurgence online, primarily influenced by the growing demand for privacy, the General Data Protection Regulation (GDPR), and Google’s scrapping of third party cookies.

There are different types of contextual targeting; category, keyword, and semantic. Category places ads on pages that fall into a pre-defined category, keyword matches them on specific keywords, while semantic targeting, the most advanced, looks at page meaning.


How Is Contextual Targeting Different From Behavioral Targeting?

Contextual targeting involves placing ads by matching keywords, topics, locations and taxonomy. Behavioral targeting, on the other hand, consists of serving adverts to groups of customers with similar browsing patterns, including pages visited, searches, or products purchased.

Let’s take skiing as an example. Behavioral targeting might group customers who had recently purchased skis and had been browsing websites about ski resorts in Japan. It would then serve them up ads for winter clothing or ski goggles when they are online.

By comparison, when using contextual targeting, the sites and blogs related to skiing would display adverts for cold weather wear and ski apparel, regardless of who was visiting the site. The ads would simply be placed based on the relevance of the site’s content.


What Are the Benefits of Contextual Targeting?

Contextual targeting offers multiple benefits to brands and advertisers:  

It is highly-relevant

One benefit of contextual targeting is that the ads customers see are always highly relevant to the content they are already looking at. This relevancy means they are more likely to respond positively to an ad, which is more likely to drive a conversion or sale.

It reduces ad fatigue

If a person sees an ad too many times in too many places, it can lead to ad fatigue – especially if it interrupts what they are doing online. Because contextually targeted ads only appear on pages with highly-relevant content, they have fewer impressions, and therefore ad fatigue is less likely to occur.

It is privacy-friendly

Unlike behavioral targeting, which tracks a person’s online behavior using cookies and HTTP requests, contextual targeting keeps the user anonymous. It only uses the content of a webpage, including keywords and topics, to appropriately place ads. This ensures compliance with today’s new privacy regulations and changes.

It heightens brand safety

Brand safety is on every marketer’s mind, and contextual targeting can reduce that risk. By providing a more accurate understanding of what is on a page, ads are less likely to be shown next to sensitive, inappropriate, and potentially brand-damaging content.  


How Does Contextual Targeting Work?

Here is how contextual targeting works in three simple steps:

Step 1: Select your keywords or topics

Your keywords and topics determine what kind of content your ads will be placed alongside. Your ads will only show up on sites that include these keywords or topics.

For example, if you were running an ad for a coffee machine, your keywords might be ‘coffee machine’, ‘kitchen appliances’ and ‘kitchen gadgets’. If you were just using topics, you might choose ‘coffee’ or ‘kitchen and home’.

Step 2: Ad platform analyzes potential placements

By using crawlers to analyze the content of potential websites and pages, ad platforms can determine which sites are most relevant to your keywords and topics, and categorize them accordingly.

Many of today’s AI-driven ad platforms go deeper than just keywords and topics. Using natural language processing and deep learning algorithms, they can analyze contextual content such as text, speech, structure, imagery, links, metadata, and geo-location to understand the semantics for more accurate, brand-safe placements.

Step 3: Ad platform serves up relevant ads 

When a customer visits a web page, the page content goes to the ad platform. Once received, the platform matches the content with relevant keywords and topics to serve up the most suitable adverts.


Contextual targeting allows you to deliver highly-relevant ads in places where customers are happy to see them. If done right, it can decrease your ad spend and increase your clicks and conversions. For best results, it should be used alongside behavioral targeting, so that you can reach shoppers in different ways at different stages of their journeys.


* Want to know more about contextual targeting? Read our blog post ‘Why Advertisers Need Contextual Targeting in the Age of Privacy’. Have a question? Get in touch with our team today.


Let us know the marketing challenges that you’re facing, and how you want to improve your marketing strategy.


A Conversation With AI Talent: The Best and the Brightest (Part 1)

To stay relevant and competitive, every type of business needs to become technical in part if not all of their operations. The world is moving too fast to stay analogue. This means however that almost everywhere in the world, there is great demand for technology talent and limited supply, particularly in areas such as artificial intelligence (AI). What can we learn from those who are already the leading minds in this space? Recently, our own Chief AI Scientist Dr. Min Sun sat down with Dr. Masashi Sugiyama, faculty at the University of Tokyo and Director of the RIKEN Center for Advanced Intelligence Project (AIP), to discuss a variety of AI-related topics. Check out our video of their insightful conversation below, and hear our experts’ points of view on how AI research has advanced over the past 10-20 years in Japan and beyond, and particularly in the past three to four. They discussed advancements in machine learning, and the goal to ultimately reduce the amount of supervision required to ‘teach’ the machine, which would indicate significant progression in AI capabilities. This is coupled with what these advancements might look like in the future, such as in the area of healthcare, allowing

In Race to Win Digital Media Subscribers, Are You Succeeding?

The success of digital media services including over-the-top (OTT) platforms like Netflix and Amazon Prime, as well as news publishers like The New York Times, have proven that traditional cable and the prints are no longer the only way audiences consume content. Enabled by technology, the shifting audience demands for what, when and where they consume content have driven incredible growth in the digital media market over the past few years. About 10.2 percent of the global population (765 million) will use a subscription OTT video service at least once per month in 2018, and the global OTT market will grow by 24 percent thanks to increasing internet penetration, faster speeds and a broader shift toward internet entertainment, according to eMarketer. For instance, Netflix has recorded a robust 34 percent year-over-year (y-o-y) revenue growth to US$4 billion driven by the growth in subscribers across both the US and international streaming markets. The latter saw the subscriber base increase by 40 percent y-o-y. On the news media side, Deloitte estimates that by the end of 2020, the proportion of subscription to advertising revenue for publishers will be 50:50 in digital. This split was still 10:90 just in 2012. As digital media

What Is the Role of Deep Learning AI in Marketing?

It’s no secret to marketers that effective use of data is critical. It is the key to executing successful campaigns that engage consumers and drive towards long-term, profitable relationships, and artificial intelligence (AI) and machine learning are vital parts of analyzing and optimizing data at scale. The amount of available data on consumers and their habits, preferences and behaviors continues to grow. It is therefore increasingly challenging to make sense of the data and make accurate predictions. Consumers sharing information about themselves via social media and e-commerce using mobile devices and personal computers are leaving behind data that can prove extremely valuable if marketers are able to look at all the data points to build holistic profiles of past, current and prospective customers. A new conversation has started among marketers about Deep Learning. Deep Learning is the most advanced branch of artificial intelligence. Deep Learning uses multi-layered, ‘neural networks’ (computer systems modelled on the human brain and nervous system) to process large volumes of scattered data. At Appier, we have seen Deep Learning work particularly well for in-app marketing. In-app spend is expected to increase by 105% in APAC by 2021 (according to AppAnnie), and marketers are increasingly relying on