It’s Sheer Genius! Machine Learning Tech Will Make Boredom Profitable

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Researchers in Barcelona, Spain have developed a “boredom detector” that they plan to present at the UbiComp conference next week in Japan. The technology is based on an algorithm that looks at your mobile activity and determines your boredom levels. Because boredom leads to depression, the technology could essentially save lives. But it could be profitable too

The algorithm in question looks at usage patterns and considers the time of day, the last time the user had a call or texted someone, etc. With 40,000,000 usage logs and 4398 boredom self-reports from 54 mobile phone users, they had enough data to consider their findings conclusive. So what did they discover?

An opportunity to monetize boredom

The technology leverages a machine-learning model that can infer boredom with an accuracy of up to 82.9%, which is pretty amazing in and of itself. But, the real icing on the cake is that the accuracy levels allow you to monetize the user’s boredom – essentially sell them stimuli they need. From the paper:

The cure to boredom is curiosity” is a famous quote by Dorothy Parker, an American writer and poet, which highlights that boredom has an actual purpose: it is an emotional state that signals that current activities or goals are not sufficiently satisfying and motivating. Potential benefits of boredom include the initiation of creative processes and self-reflection [34]. Given that bored people long for stimuli and that human attention has become scarce and increasingly valuable [11], there also is commercial value in knowing when a person is bored.

So, if mobile phones are able to detect when users are trying to kill time seeking random stuff on the web, imagine the potential to monetize this boredom by suggesting articles to read, videos to watch, games to play – in a word, serve them ads. But not just any type of ad: an ad that takes you by the hand directly to the product you’re about to consume anyway, out of boredom. It’s sheer genius!

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