Solving the major issues of the removal of cookies and IDFA and rocketing costs for advertisers and brands.

The cookie jar is officially closed. Over the past couple of years, online advertisers have seen several major trends emerge that will permanently shift the way they work. 

Arguably, the most significant change (and which has caused costs to skyrocket for many) is that third-party cookies, the staple tool for targeting ads at online users, are becoming obsolete. Advertisers have long used the browsing activity stored in web browsers to identify and reach their desired audience online, fueling the trade for personal data.

Google will stop supporting cookies by 2023. And now with Apple giving users the choice to block the IDFA identifier on an app level, advertisers no longer have the option to monitor user behavior on non-browser apps.

Users are more aware of how their data is utilized and are concerned about privacy issues. Companies need to find solutions that transition toward responsible and sustainable advertising, and for non-intrusive personalization that prioritizes user experience and trust as the new normal.

Contextual Intelligence as a solution

How can brands give consumers what they need if they can’t rely on cookies to provide the data? The answer is to not look to the past. It’s to understand what’s happening in real-time.

Instead of using a historical database of online user behavior to predict what the user might want to see, today’s technologies can help brands combine multiple data sources to deliver contextually-powered content offerings through sentiment, semantic, and on-page behavior analysis powered by Machine Learning (ML).

Of the many proposed solutions, Contextual Intelligence is seen as a potential tool to work around the complex issues surrounding personal data. It is a way of programmatically purchasing digital advertising based on appropriate categories of relevance. 

This will allow elements such as headlines and images, product selection, and on-page placement to be optimized, resulting in brand-sensitive, relevant content that is more user-centric and maintains anonymity.

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 Intelligence to find highly relevant pages across millions of websites at a much lower CPM.

Contextual Intelligence blends Artificial Intelligence and Machine Learning to mimic human-level intelligence in understanding the context of the advertising environment. Advertisers are constantly exploring ways to use it more effectively in terms of scale and measurement.

Contextual content, paired with ML, is already making waves in the advertising world, the front-line of business/consumer interactions. But even here, there is still room for improvement to reverse the slump in ad spend return many brands are battling. As always, it’s not only about the tools you use – but the process you use to deploy them. 

Buying intent platform

This is where NinaData’s Buying Intent platform comes in. We help 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 centralized database that holds a vast amount of such information. The database is constantly growing and frequently updated.

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

For example, in the case of a streaming service, it suggests which movie to watch. In the case of e-commerce, it suggests which product to buy, or in the case of Kindle, which book to read. It plays a prominent role in providing required content to the user in a product-based or a user intent-based search.

Our Buying Intent Prediction platform uses a semantic engine utilizing the Interactive Advertising Bureau (IAB) content taxonomy classification to provide better accuracy and performance. This basically means that you can trust us to differentiate and understand the meanings between keywords like “squash,” which classifies both as a sport and as a family of vegetables and that your advertising money won’t be wasted on targeting the wrong ad groups.

Finally…

For better or worse, brands, advertisers, and consumers are deeply connected in a data-driven relationship that isn’t going anywhere for a long time. In an online-first world, brands need to find privacy-first and relevant ways to offer consumers the essential and inspiring products and services they wish to purchase. NinaData can support brands and respect consumers to achieve this – all while consigning cookies to the past. 

The NinaData technology team combines extensive industry experience with technical know-how and a long background in building a scalable, high-value, and high-performance architecture for AI analytics of media content. NinaData’s data scientists and management combine methodological skills with practical application experience.

Get in touch for more information about how we can help you lower your advertising costs and reach more targeted audiences than ever before!