What, Exactly are We Curating?

We know a fair bit about curation.  It’s what our platform does and has done since 2018.  In fact, it was built to curate as a core function.  So we’ve had experience in curation and our brand customers benefit from it.  More than 70% of our brand customers have been with us for 2.5+ years.  That retention rate indicates we’re able to deliver what works at scale and make sure our customers avoid the steady torrent of crap that we all read about.

To give you a sense of our scale,  we curate content at the rate of 1mm impressions per second across 3.5bn URLs.  We aggregate those, dynamically, into segments that deliver scale and performance to our brand customers.  For curation, our contextual topic model and keyword analysis are the only two pieces of data we’ve ever used.  

The content we curate are individual pages.  We curate only article pages and never home pages or channel fronts.  We have learned that the strongest signal comes from article pages; that home pages and channel fronts have little signal.  Article pages are what consumers focus on when they’re at task.  They gain information from them and that information forms a mindset with the reader. That’s what we’re after.  

We continue to curate our content segments in real-time based on the scale and performance our brand customers provide us as feedback.  This creates a cycle where optimizations can and do happen by adjusting the words and the weighting of words our topic and keyword models deem relevant based on scale and performance.

With this cycle of continuous curation of page-level content into segments, we’re able to help our brand customers navigate the landscape of legacy data that’s either changing or going away.  Over time, we’ve begun to understand there are topics and KWs that consistently indicate the mindset of the consumer who’s reading about a topic.  

We see similarities across brands in the same categories with words and topics that create different responses.  With the knowledge of a category across many brands we can create other filters to better understand a page.  The cycle goes on.

That’s our version of curation and it has been proven over the years to be effective.  

What we don’t do is use curation as a marketing tool for inventory and domains.  In fact, we think focusing on domains and inventory misses the point of curation.  And might be a bit of a dodge.  

To us, the inventory a brand targets is a tactical optimization and they should be able to separate that from the audience they’re after.  Some segments might respond best with one ad size and another brand in the same category might find success with another size.  So why try to be the arbiter of optimization?  Our function is to provide curated content that delivers an audience.  The inventory is up to the brand and they like that control.

Then we’ve read that curation rids segments of MFA sites.  To that we say anyone that needs curation to remove MFAs isn’t trying hard enough.  MFAs have to be excised at the platform or ecosystem level first, as close to the publisher as possible.  If the only place in a workflow to excise worthless content is the segment, the logical outcome is you won’t remove all of it.  

We should all get behind curation.  But we should raise the bar on what curation means and not lower it to suit the result of our current efforts.  Or problems.  Feel free to reach out to us and we’ll show you how curation works.  

How Advanced Contextual Uses AI

Every adtech company says they use AI. But what really matters is what they can actually do with AI that they couldn't do otherwise and how it affects performance for advertisers. 

To understand how AI drives better performance with contextual advertising, let’s review how most companies approach contextual targeting and how AI allows advertisers to generate better performance through more granular targeting.

How most companies do contextual targeting 

Most companies use cookie-cutter taxonomies to facilitate contextual targeting. For example, they might borrow a taxonomy of roughly 300 topics, categorize content on the web based on those topics, and then use keywords to place their ads on content supposedly relevant to their audience.

This is a level of content analysis you don’t need AI to perform because the taxonomy is simple and unchanging, and the targeting mechanism is a simple question of matching advertising keywords to pages of content on which those keywords appear.

The problem with this approach is that it is insufficiently granular, which leads to irrelevant advertising and poor performance. For example, a travel advertiser using keywords to describe travel might end up advertising on a story about people fleeing natural disasters. Or a Bahamas hotel chain might end up advertising on stories that are about travel but have nothing to do with the Bahamas. That doesn’t exactly capture the dream of highly relevant contextual advertising.

Enter AI.

How AI helps Advanced Contextual drive higher performance

AI allows us to process vast swathes of information at a scale and pace impossible for humans. This allows us to do two things.

First, we’ve used AI to develop and leverage a much more granular taxonomy of topics than most cookie-cutter taxonomies. Our taxonomy is 1,500 topics, and we’re able to use it to categorize content more finely, generating more relevant ad targeting and higher performance.

Second, we process 1 million auctions per second. That means we’re able to understand content much better, match the advertiser with a page and audience relevant to them, and drive conversions more efficiently than alternative solutions.

Finally, we use AI to generate synonyms for our advertisers’ keywords. Using generative AI allows us to develop a consistent, rigorous strategy for each client instead of allowing each human team member to freestyle when developing the keywords that will facilitate ad targeting. 

AI is neat. Driving performance is imperative

Every company should be straightforward about where and how they use AI — and also be able to show both how it works and the impact it generates. Let us know if you’d like to see our AI-driven solutions in action. 

How Advanced Contextual Enables More Cost-Effective Retargeting

The end of the third-party cookie on Chrome will make it urgent for advertisers to rethink tried and true strategies such as retargeting. 

Advertisers have been using data from direct publisher buys, data partner usage, and third-party data segments to create site visits for their campaign and retarget consumers with high intent from there. 

But as third-party segments go away, advertisers will lose signal on “intenders,” and parts of their retargeting pools will be eliminated. 

Contextual advertising can help advertisers fill the coming retargeting gap at scale. Here’s how.

Finding your audience more efficiently with contextual advertising

Retargeting efficiencies are directly related to the CPCs paid to create site visits, which themselves create retargeting pools. And the lower the CPC, the better the retargeting efficiency is.

Because context is a good indicator of consumer intent, it can deliver low CPCs that are of high quality. We focus our platform and operations around a few activities to achieve a high-quality, low-cost CPC.

1. We use topics, not keywords. That means that, for a hotel chain in the Bahamas aiming to find audiences who might stay at their hotels, we don't just look for articles that contain the words "Bahamas" or "travel." We identify topics related to your search such as high-end Bahamas hospitality and find a narrower and more relevant set of pages relevant to that topic.

2. We screen out brand-unsafe articles as well as ineffective and low-quality inventory such as MFA sites. You only show up on sites that are brand-suitable and where real humans are consuming content.

3. We only target pages, not domains or channel fronts. For example, the New York Times has pages relevant to a Bahamian hotel chain. But not every page on the NYT is relevant, nor is even every page on its "travel" section. So, we don't target the NYT home page or its travel section. We target the NYT pages most relevant to the topic.

The way most contextual providers would help the hotel chain target potential customers is by targeting keywords. For example, they might help the hospitality business target any articles containing keywords related to "travel" or "the Bahamas." This is how contextual targeting by all the major providers such as Oracle, The Trade Desk, and Google works.

The problem with the usual approach — using keywords alone — is its inefficiency. If you rely only on keywords, you'll target plenty of pages on the Bahamas that have nothing to do with high-end travel. Some may even be brand-unsafe. For example, you wouldn't want to remind prospective travelers to the Bahamas about natural disasters in the region.

This more granular approach to contextual advertising drives much more efficient CPCs. For example, a hotel chain like the example we used in this article used us to achieve $0.80 CPCs against a benchmark of $2. 

With these efficient clicks, you can develop a retargeting pool much more quickly and cost-effectively. Then, you can use alternative IDs to find hand raisers wherever they are until you close the deal.

The end of cookies doesn’t mean the end of retargeting. It just means you need smarter contextual solutions to kickstart the process.