The advertising industry is slowly breaking up with cookies. In a bid to be more privacy-conscious, Google will sunset third-party cookies in the second half of 2024. This means advertisers will no longer be able to use cookies to track behavior for advertising.

The solution to this problem is contextual advertising – the method of placing ads next to content that is contextually relevant – by using things like keywords, topics, and language to find the most content.

That’s where Natural Language Processing (NLP) comes in.

What is Natural Language Processing?

NLP is a branch of artificial intelligence that focuses on enabling computers to understand, interpret, and generate natural human language. NLP algorithms use a combination of machine learning, linguistics, and computer science to analyze and understand natural language data, such as text or speech. The algorithms process vast amounts of training material to learn a sophisticated model on contexts.

NLP is an integral part of contextual advertising because through it, not only can the words but also the context and sentiment of a webpage be understood. This means that brands and companies can rely on AI to find the most relevant content, opening up opportunities for their campaigns to get more reach.

Isn’t contextual advertising really old fashioned?

It used to be. But nowadays with AI, contextual advertising is a dynamic strategy because as the page content changes, so do the ads. Using artificial intelligence (AI) and NLP, you can place ads on pages based on the content of those pages – or in other words, the contextually appropriate place. Contextual targeting takes the semantic, contextual analysis of a web page into consideration when displaying ads instead of user behaviour.

This means you can deliver dynamic, 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.

What is sentiment analysis?

Sentiment analysis uses NLP to determine the emotions and attitudes of content, comments, opinions, user feedback, or any other online data set. It analyzes the emotional tone of a piece of text, such as whether it is positive or negative. This helps publishers and advertisers understand how consumers react to certain topics.

Additionally, advertisers can use sentiment analysis to determine whether their campaign was effective and successful. This is important for advertisers because the success of an advertisement often depends on how well it aligns with the audience's expectations and interests. This helps to identify brand ambassadors or “evangelists” – people who are likely to actively spread the word on behalf of the company. 

An advertisement with a positive emotional tone and references to specific products or services is likely to have a persuasive intent, while an advertisement with a neutral tone and a focus on providing information is likely to have an informative intent.

What about contextual intent?

NLP algorithms can also be used to analyze the audience's response to an advertisement and determine whether the advertisement is meeting its intended goal. This can be done by analyzing the comments and feedback provided by the audience, as well as other indicators of engagement, such as clicks, shares, and likes.

By using NLP to determine the contextual intent of an web page and assess its effectiveness, advertisers can improve the targeting and relevance of their advertisements, resulting in better engagement and performance.

NLP can be used to predict 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.

This helps brands reach consumers at the exact moment they are making a purchase decision online. AI crawls millions of webpages to semantically analyse text, video, and images across millions of web pages, and by using a specific data extraction process, it can understand the core meaning, segmenting the pages into semantic categories. 

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.

NinaData’s buying intent prediction’s engine improves the understanding by categorising a keyword into one or more taxonomies. A classic example of a word could be ‘squash’ which classifies both as a sport and a family of vegetables.

To sum up

NLP is a powerful tool for advertisers that can help determine the contextual intent of an advertisement and assess its effectiveness, as well as determine the buying intent of a consumer. By using NLP algorithms to analyze natural language data, advertisers can improve the targeting and relevance of their advertisements, resulting in better engagement and performance.

NinaData’s Buying Intent Platform

NinaData’s platform uses state-of-the-art AI, NLP and fine-tuned language models to achieve a high level of understanding of any web page’s intent. This allows the semantic-based platform to optimize the selling of ad inventory by matching URLs to a viewer’s predicted intent based on page-level context. 

This in turn allows advertisers, ad agencies and marketplaces to precisely target ads with 90+% accuracy without using browser cookies or any other form of personal identifiers. The result is a privacy-friendly, end-to-end platform that provides clients with a publisher-independent web crawler, an application layer, campaign management and an analytics layer.

If you would like to learn about how we can drive your brands advertising ROI with contextual advertising, get in touch today!