Machine learning reveals crazy advertising ideas that actually work | Industry

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There are a lot of expectations among advertisers: They target certain demographics of people to buy certain products at certain price points. And they're often wrong. “It's pretty hard to be world class at this as a human. So we kind of say, let the machines do the job,” said Julie Shumaker, VP of advertiser solutions at Unity during VentureBeat's Transform 2018 AI conference.

The supplier of video game-creation software is reaching consumers through 3 billion devices every day, she said, providing rich opportunities for advertisers — and lots of data for learning about user behavior. “We're taking a step before [the ad] to [understand] ‘what is the optimal outcome for the player or user?'” said Shumaker.

Advertisers might have very specific goals, like selling a $17 game install to a 22-year-old player, she said. They wouldn't think about a 65-year old woman. But learning may reveal that she is likely to spend about $3.99 over the course of three days. And if the cost of acquisition is 75 cents, it yields as good an ROI as higher-dollar goals for stereotypical ad targets.

“You're able to try the stuff,” said John Koetsier, VP of insights at marketing data platform Singular. One crazy example: A client that ran an advertisement for a game, which didn't show any actual gameplay. But it generated a huge amount of conversation about the game among a certain group of players. “You can try many, many things, because you can let the machine [learning] then figure out in real time what's generating impact,” he said. “You can do stupid stuff, and sometimes stupid stuff is smart stuff.”

Guessing also happens on the creative side, said Rishi Shiva, CMO of Bidalgo, which provides ad automation technology to in-app marketers. The company recently rolled out a machine learning service called Creative AI that analyzes images to identify approaches that are likely to succeed. “Before you go investing hundreds of thousands of dollars developing video assets, you can actually run your historical images and videos through our system, and it will actually give you insights,” he said. “What actually had a positive impact on the audience? What is it that people liked?” It can get as granular as the poses people take in images, he said. The software then develops a creative brief for content teams “that talks their language.”

Using AI to develop new types of insights is important because of new privacy requirements like Europe's General Data Protection Regulation (GDPR) rules and the California Consumer Privacy Act of 2018. “In light of GDPR and California's GDPR-light and laws coming up in different states in the U.S., the idea of using behavior data in advertising is under peril for a number of reasons,” said Ben Plomion, CMO of GumGum.

The company intuits the context of a particular online session by analyzing the images on the page or in the app. It then places relevant advertisements over a portion of the image. (Users can click to close the ad.) For its client Jeep, for instance, GumGum could place ads for the Cherokee on images of competing models, such as the Toyota RAV4. So instead of building a big behavioral model of the consumer, it uses machine learning to understand the particular context in that moment.

A clever example was a campaign for Vodafone in the U.K. last year. The telecom company wanted to advertise that it would carry the iPhone X, but Apple's restrictive advertising guidelines made it difficult to mention the product. So GumGum's machine learning-based technology found images of the iPhone X and placed Vodaphone ads on them. “So by that association, even though we never mentioned the words ‘Apple iPhone X,' consumers knew that they were selling service for the iPhone X,” said Plomion.

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