For publishers, ad blocking represents the ongoing dance between revenue and user experience. According to IoT for All, that dance may move in a new direction: A.I. ad-blockers that identify ads according to their FTC-mandated signifiers.
Your personal experience of online ads depends heavily on the type and topics of media you consume. If you’ve never played a shoddy iPhone game or accidentally lost yourself in a clickbait farm, perhaps you’ve never seen those low-quality ads that are clumsy at best and deceptive at worst. What do I mean by low-quality? Here are a couple of the programmatic sins that drive millennials to pick up ad-blockers.
The average consumer has no idea how many moving parts a video ad entails. They just see a 30-second spot about deodorant. Poor media software or other structural errors can result in improper formatting, slow loading, and other tears in the seams. Viewers expect a smooth transition; having to sit through an ad longer because the ad is broken is a great way to sour user experience.
Programmatic advertising is a godsend when working with high-volume inventory. However, when marketers aren’t careful in their delivery parameters, strange and annoying things can happen, like repeating the same video for the same customer over and over. Or repeating the same video for the same customer over and over. Or repeating the same video for…
Where’s the eXit?
This one’s especially egregious because it exploits users that don’t know any better. Since certain ad formats include a “skip” or “close” button, some of the more unscrupulous creatives will feature misleading visuals–like a pronounced “X” in a company logo–that fool inattentive consumers into a time-consuming click.
Thanks to infractions like these, the battle of the blockers is going to last us awhile. That’s why publishers need to consider alternate revenue sources, like monetizing their data. The Complementics Data Exchange helps publishers do just that: by installing a bit of code on site, publishers can sell anonymous behavioral data. Best of all, companies use this data to actually improve users’ experiences, instead of perpetuating a race to the bottom.
How Content Classification Adds Value to Mobile Data
If you spend some time on our new site, you’ll probably see a few mentions of our sister company, eContext. Why are we so proud of this partnership? It’s because Complementics takes already valuable Mobile Audience Data and enriches it via eContext to add an extra layer of understanding.
That probably sounds buzzwordy, so let me explain: eContext is a classification system that labels content according to 500,000 (very specific) topics. Those topics are organized into and taxonomy of 25 verticals and 20 tiers of depth. What does that mean? eContext’s structured labelling helps us group and interpret ambiguous content.
For example, suppose you have access to Audiences’ app usage. eContext can go beyond app store categories to get a granular understanding of users’ interests. Then, when you combine that structured, specific intelligence on app use with other dimensions of Complementics data, you end up with a hostile customer profile that reveals spending power, interests, and even brand loyalty.
Complementics Mobile Audiences marries empirical audience data–clear-cut items like location and demographics–with the kind of personal-preference information that’s notoriously tough to pin down. It’s that second half that relies on eContext, adding a deep understanding of content to a huge network of behavioral data.