Contextual Advertising: Classifications, Privacy Concerns, and The Future

By Kevin B. Simon

Contextual Advertising: Classifications, Privacy Concerns, and The Future

Contextual Advertising is a form of online advertising tailored explicitly to the page's content. For example, contextual targeting allows advertisers to display ads relevant to the page's content, increasing the likelihood that users will click on the ad and visit the advertiser's website. This type of advertising can be carried out in several ways, including using keywords, meta tags, and other techniques to match ads to relevant content on a webpage. 


Google AdSense is one of the most well-known examples of contextual advertising. Google robots serve relevant advertisements to your users. For instance, if you run a blog that reviews movies, AdSense may display advertisements to purchase movie tickets or sign up for a movie streaming service. The advertisements are selected from the inventory of Google Ads-registered advertisers.


Types of Contextual Advertising;


In-game Contextual Advertising


In-game contextual advertising refers to targeting advertisements to specific content or contexts within a video game. This type of advertising employs information about the game environment and player behaviour to display advertisements pertinent to the game's current situation. For instance, an ad for a car brand could be displayed when the player is racing or driving in a car-related level or mission, or an advertisement for sports equipment could be displayed during a sports-themed minigame. Both advertisers and game developers can benefit from in-game contextual advertising. Advertisers can reach a highly engaged audience and increase players' likelihood of interacting with their ads. At the same time, game developers can generate additional revenue by incorporating advertisements into their games. 


In-video Contextual Advertising


This type of advertising displays advertisements that are relevant to the topic or theme of the video by using data about the video's content, audience, and other factors. For instance, a clothing brand advertisement could be displayed during a video about fashion, or a travel company advertisement could be displayed during a video about a specific destination. Such advertising can be delivered through different platforms such as YouTube, Hulu, Twitch, etc. It can take many forms, such as pre-roll, mid-roll and post-roll ads, where the ads are inserted at the video's beginning, middle or end. With pre-roll ads, the ad is played before the video content is viewed; with mid-roll ads, the ad is played in the middle of the video and post-roll ads are played after the video content.


Native Advertising


Native advertising is a form of contextual advertising designed to blend in with the page's surrounding content. The objective of native advertising is to create advertisements that are less intrusive and more engaging for the user by matching the look and feel of the website or platform on which they appear. There are numerous types of native advertising, including sponsored content, in-feed ads, recommended content, and search ads designed to resemble organic search results. Depending on the platform or website on which native advertising is displayed, its format can vary.


Due to its ability to blend in with the surrounding content, one of the primary benefits of native advertising is that it can be less disruptive to the user experience. This can result in increased engagement rates and improved advertising performance. Importantly, native advertising should be clearly labelled as advertising and should not mislead users. 


Behavioural Advertising


Behavioural advertising targets users based on their browsing history and online behaviour. Advertisers compile a profile of a user's interests and preferences based on their browsing history, search history, and other online activity. Behavioural advertising aims to display advertisements likely to interest the user based on their previous actions.


Various tracking technologies, including cookies, pixels, and browser fingerprints, are utilised to execute this type of advertising. These technologies collect information about the user's browsing habits, search history, and other online activity, which is then analysed to create a profile of the user's interests and preferences. Advertisers can use this information to target advertisements likely to interest the user.


Behavioural advertising can be advantageous for both advertisers and users. Advertisers can reach a highly targeted audience and increase the likelihood that users will engage with their ads, while users are more likely to see ads relevant to their interests.


NOTE: All the above-mentioned contextual advertising types should be implemented to not disrupt the target audience's experience; otherwise, it may result in adverse outcomes such as frustration and decreased engagement.


Privacy Concerns


The fact that contextual advertising can collect sensitive information about users, such as their search queries, browsing history, and other online activities, is one of its primary concerns. This data can be used to create a profile of the user's interests and preferences, which can then target advertisements to the user. This can raise privacy concerns because it allows advertisers to collect and use personal information about users without their consent or knowledge.


In response to these privacy concerns, numerous businesses and organisations have implemented diverse privacy controls and policies to safeguard the personal information of their users. Companies that engage in contextual advertising, for instance, must disclose their data collection and use practices and provide users with the option to opt out of data collection and targeting. On top of that, Federal Trade Commission (FTC) has established guidelines mandating that such advertisements be clearly labelled and distinguishable from the surrounding content. 


The Future


As technology and data capabilities advance, contextual advertising will likely experience continued growth and innovation. Advertisers can target their ads more effectively and efficiently to specific audiences and content as the amount of available data increases. It is anticipated that contextual advertising on various platforms, such as social media, video streaming, and podcasts, will increase.  As many individuals consume media across multiple platforms, advertisers must adapt their strategies to reach consumers on each platform.


One trend likely to continue is using artificial intelligence (AI) and machine learning (ML) in contextual advertising. These technologies can be used to analyze large amounts of data and make predictions about user behaviour, improving targeted ads' accuracy and relevance. Another trend that is likely to grow is voice search and virtual assistants. As more and more people use voice-controlled devices to search for information, advertisers will need to adapt to this new way of searching. They will have to develop new strategies for targeting ads to users searching for information using voice commands.


In addition, contextual advertising in the form of sponsored content and influencer marketing will likely increase in the coming years. Sponsored content and influencer marketing are becoming more effective at reaching consumers in a more authentic and relatable manner as consumer scepticism of traditional forms of advertising increases.


About the Author

Kevin B. Simon (ORCID: 0000–0002–2962–8008) is a Technology Editor at IndraStra Global with a keen interest in technology-driven businesses. He is a graduate in management studies (MBA) from the Institute of Management, Nirma University, Ahmedabad. 



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IndraStra Global: Contextual Advertising: Classifications, Privacy Concerns, and The Future
Contextual Advertising: Classifications, Privacy Concerns, and The Future
By Kevin B. Simon
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