Contextual vs. behavioural advertising: what’s the difference, and which is better?

Both have played different roles in digital marketing strategy, but only one is truly future-proof.

Contextual and behavioural targeting are both used to ensure that ads are placed in front of users looking to buy something. Each is dynamic and able to adapt to new data when it becomes available, meaning they can be quite relevant to users. However, while both aim to drive sales conversions, there are notable differences in how they work and operate.

At NinaData, our solution and methodology centers around the shift in focus from targeting based on behavioural data to contextual targeting. But what exactly is the difference? 

What is behavioural targeting?

Behavioural targeting advertising divides potential customers into segments and interests based on their web browsing behaviour. This means that it tracks your past browsing behaviour, searches, links clicked, and purchases you’ve made in order to display relevant ads. This is done through the use of third-party cookies.

For example, if you’ve recently bought a new car, behavioural targeting might use the data from your purchase history to display car accessories adverts to you. And these car accessory advertisements will be displayed on any web page you’re on, regardless of whether the page’s content is related to cars or not.

Essentially, behavioural targeting tracks and sends information about your visits, search, and buying history to an ad server. It then finds relevant content in its ad inventory and bids to display it in advertising real estate on websites, such as the side banners. The more advanced the advertising system is, the more relevant ads it will display.

These types of ads can feel quite intrusive. According to a GumGum survey, a majority of consumers (66%) report they are uncomfortable with companies tracking their browsing history to show them personalised ads. Digital ads that make consumers uncomfortable destroy brand trust and increase the likelihood of a negative response.

What is contextual advertising?

Contextual advertising involves placing ads on pages based on the content of those pages – or in other words, “in the appropriate context.” Contextual targeting utilises data sources like keywords and the content topics of the web page into consideration when displaying ads instead of user behaviour.

For example, if you’re reading a news article about 2022’s best handbags for fall, the site would display handbag options to purchase in the advertising space. Contextual targeting is seen as a potential tool to work around the complex issues surrounding personal data and privacy.

Contextual advertising is still a dynamic strategy because as the page content changes, so do the ads. Using machine learning (ML) and artificial intelligence (AI), we can deliver contextually-powered ads through the semantic analysis of segment, sentiment, and customer journey stage. So as the content changes, the better the platform detects the content relevancy, the higher chances a consumer will click the ad.

Which is better?

Consumers are uneasy about brands tracking their browsing history with cookies – due to the privacy concerns Google has announced that it will stop the use of third-party cookies in Chrome by the end of 2023, with other brands also ditching the technology. Additionally, according to the GumGum study, 79% of consumers report being more comfortable seeing contextual ads than behavioural. 

According to data from Adpushup:

  • Contextual advertising averaged a 73 percent increase in performance when compared to behavioural advertising.
  • 49 percent of US marketers utilise contextual targeting today.
  • 31 percent of brands plan to increase their spending on contextual advertising next year.

Ultimately, contextual advertising is the future-proof method, which also happens to be more relevant, easier to implement and cheaper than behavioural advertising. For example, a tech advertiser could buy advertising directly on CNET or the technology section of the New York Times directly at a high CPM. Alternatively, they could leverage contextual targeting to find highly relevant pages across millions of websites at a much lower CPM.

At NinaData, we are especially focusing on predicting the Buying Intent of the content consumer in a way that makes the contextual matching of the content against the desired audience segment easy and precise.

Buying intent platform

NinaData helps brands reach consumers at the exact moment they are making a purchase decision online. Our contextual data platform uses purpose-built AI for the semantic analysis of text, video, and images to build sustainable data value and insights for brands and content owners, and drive in-moment online results for brands.

Our technology constantly crawls millions of web pages, and by using a specific data extraction process, it understands the core meaning, segmenting the pages into semantic categories. Once crawled, the page information is sent to a core engine, processed, and stored in a centralised database that holds a vast amount of such information. The database is constantly growing and frequently updated.

Essentially, it focuses first on recognising the core content of a page using keywords, then offers to the user that understanding for the purpose of enhancing ad targeting and recommendations. 


The need for privacy-first solutions has pushed market leaders like Google, Apple, and Microsoft to announce the future demise of third-party cookies in their browsers. Also, privacy-related regulations like GDPR and CCPA have provided content consumers an easy way out of cookies, forcing advertisers to look for different solutions.

NinaData can support brands and respect consumers to achieve this – all while consigning cookies to the past. 

If you’d like more information about how we can help you lower your advertising costs and reach more targeted audiences than ever before, get in touch with us today.

This post was written by NinaData General Manager Kimmo Valtonen. Kimmo oversees NinaData’s business modelling and go-to-market strategy for our innovative new AI platform.

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