Using AI with Contextual: Q&A with Seedtag


In collaboration with Seedtag.

In this exclusive Q&A, Jorge Poyatos and Albert Nieto, co-CEOs and co-founders of Seedtagdiscuss their recent acquisition of KMTX and how AI is being used to address challenges within contextual advertising.

What were the main drivers behind the decision to acquire KMTX? How does KMTX’s solution integrate with Seedtag?

Over the past eight years, we’ve built a privacy-first advertising solution, pioneering the use of AI and machine learning to create the best contextual product on the market. Now we feel it’s time to evolve even further. When we got the chance to analyze KMTX’s technology, we knew they were the right target to help us do that.

The “death” of third-party cookies is a golden opportunity for our business, and the acquisition of KMTX puts us in the best possible position to take advantage of it. We were somewhat lacking in low-funnel capabilities, but with KMTX’s predictive models combined with Seedtag contextual data, we are able to provide brands with that long-awaited full-funnel contextual solution. This positions us as a one-stop shop for contextual advertising, reducing complexity while helping clients achieve their campaign objectives.

What are the current challenges within context that can be addressed with machine learning and AI? How will AI solutions evolve to address these?

Jorge Poyatos (r) and Albert Nieto (l), Seedtag

The industry has traditionally taken a rather blunt approach to contextual targeting, with strategies based almost exclusively on keywords or the domains people visit. In recent years, however, AI has enabled advertisers to develop a more sophisticated strategy based on AI’s abilities to read and understand text and in our case also images in the same way that you and I do. This allows us to target user interest with an accuracy and brand safety that was unimaginable a few years ago.

However, one of the biggest challenges advertisers face is understanding how to find their audiences through contextual targeting. For many years they have been analyzing the demographic profile of their customers, but they have no idea about their contextual profile. Contextual AI can help them overcome this challenge by providing and analyzing consumer interest data for them. For example, we show brands what users in the UK are reading in real time and then convert this data into customer-specific targeting strategies.

These new capabilities are the result of innovating using AI for context. Until now, contextual AI was mainly based on supervised learning. This means that the machine has been trained using data that is properly “tagged”, i.e. some data is already tagged as being the “correct answer”. Our AI also used this approach for page-level analytics (PLA). Each time a URL is entered, the technology can place it in a number of categories based on prior training.

However, we are pioneering the use of unsupervised learning for contextual advertising. Unsupervised learning means that the machine has to work alone to discover patterns in the data. Through unsupervised learning, our AI allows us to group articles based on similarity and semantic proximity. All of this means we can not only perform page level analysis (PLA), but also use the intelligence of network level analysis (NLA) to make recommendations to brands as to what content to target based on how close it is to their audience. , similar to how Netflix recommends which movie to watch next.

How can full-funnel contextual solutions benefit advertisers from a performance perspective? Likewise, how can contextual marketers capitalize on brand objectives?

In recent years, we’ve seen a strong correlation between contextual cues and performance outcomes, although we didn’t have the technology to predict post-click behavior at scale. The acquisition of KMTX brings AI-based predictive models to our stack that, when combined with our proprietary contextual data, will provide an industry-leading solution for achieving performance outcomes in a cookie-free world.

As third-party cookie-based strategies die out, new solutions are emerging. For example, unified IDs and first-party data are both great alternatives, but they lack scale and reach. Thanks to our contextual performance technology, we are now able to provide a solution that combines both scale and effectiveness on the open web.

How are contextual ad results and KPIs currently quantified? Are new and/or evolved stats required here?

KPIs depend on the client’s objectives. Contextual brand campaigns are typically measured using commonly used industry metrics: CTR, VTR, visibility, etc. However, we found that these are not sufficient in determining the attention quality of a contextual media investment. Typically, we extend that information by partnering with independent research firms such as Lumen or Metrixlab to measure the real impact of attention on brand perception.

Our performance solution focuses much more on post-click KPIs such as time spent on the page, qualified visits, leads, etc. This means that Seedtag is now able to optimize campaigns across all parts of the funnel.

How does contextual solution adoption differ in EMEA, LATAM and North America? What are the specific challenges and opportunities within each of these markets?

We have seen an increasing interest in deploying contextual advertising solutions in all markets, but we certainly notice differences between regions, with Europe being a pioneer in this regard. Europe was one of the first regions to address user privacy issues, forcing countries to adopt regulatory measures to ensure the privacy of their citizens. France is at the forefront of this and is probably the market in which we operate with the strictest regulations. As a European company, these changes have pushed us to become leaders in contextual advertising, providing companies in these markets with solutions that respect user privacy and comply with all regulations.

One of the challenges of an international company is the linguistic differences between the different countries in which we operate. We started this challenge from the beginning and our contextual AI is currently capable of analyzing content in more than eight languages. and more will be added in the future.

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