Content and connection: AI driven contextual advertising

The global contextual advertising market will reach $376 billion by 2027 with an annual growth rate of 13.3%, reveals Silverpush. It was also revealed that 74% of marketers are using contextual data to improve their advertising on the web. Contextual advertising is becoming increasingly important in this digital era. This approach helps the advertisers in delivering relevant material to the consumers according to the information they are engaging with. In layman language, if you are browsing through an article about leg workouts, the ads showcased on your screen will be related to gym for eg. equipment, gym clothes etc.

Thanks to artificial intelligence, contextual advertising has reportedly become more sophisticated, helping marketers reach their audience with greater precision and improve the overall user experience. The global market for AI in marketing is projected to reach USD 40.09 billion by 2025, reveals a report by Markets and Markets. While we live in a world where updates and changes to better AI are happening every day, using AI to deliver optimal results in contextual advertising means harnessing the latest advancements to ensure ads are more relevant, timely, and effective in engaging consumers.

AI in contextual advertising

Contextual advertising was limited to broad categorization which led to consumers being differentiated into larger segments. Although this did fit the frame of contextual advertising, it missed the mark when targeting specific audiences. This also led to dissatisfied consumers who did not engage with the ads at all.

With the help of AI, contextual advertising does not need to categorise consumers into broad segments and can deliver the right ad to each consumer, thereby driving up the ROI (return on investments). Advanced AI systems employ “unsupervised learning” to autonomously identify patterns in content and user behaviour, predicting future actions with greater accuracy. In simple words, AI analyses data from across the world to understand the consumers’ interests and then offers a more nuanced and effective way to connect with audiences

By leveraging customer data, AI can help in building contextually focused classifications specially for contextual targeting.

For instance, imagine a company specialising in high-end organic skincare products aiming to connect with consumers interested in premium, natural beauty solutions. Traditional contextual advertising might target content in the broader beauty and organic categories, often prioritising the volume of reach over the precision of relevance. This could lead to ads appearing alongside articles about generic skincare brands, which may not resonate with consumers looking for luxury organic options, or ads for drugstore skincare brands that do not appeal to those seeking high-end, natural products.

In contrast, AI-driven contextual targeting provides a more precise approach. Using advanced machine learning, AI ensures that ads for high-end organic skincare products are placed in contexts that specifically focus on luxury natural beauty solutions. This means ads are displayed alongside content that aligns perfectly with consumers who are interested in premium, organic skincare, thereby enhancing relevance and engagement by avoiding placements next to generic skincare advice or budget-friendly product ads.