Admit it. By now you’re so used to being ‘followed’ by ads associated with your online searches that you shrug it off. You search for a hotel for your trip to Disney World and within minutes see a sponsored post for an Orlando hotel on Facebook. This is the new normal. It’s the way web marketing works, and only a small percentage of web users still find it a little creepy. But it looks like things are about to get weirder: advertisers can now not only sell space based on your search history but based on how you’re feeling.
Yup. You read that right. Here’s the download. The New York Times recently announced it has been using machine learning and surveys to predict the emotions of those who’ve read its stories over the past year. They’ve used the data to create a list of 30 commonly experienced emotions, more than half of which are now up for sale to advertisers, according to Adweek. The list, in alphabetical order, follows:
- Feeling love
- In the mood to spend
So, a company selling expensive handbags may target someone feeling “indulgent” or “in the mood to spend,” while a streaming service may target someone feeling “bored.” It’s a potential gold mine for an industry that relies on creating or identifying people’s emotions to drive relationships and ultimately sales. But this is taking it to the next level, don’t you think? Yikes.
The New York Times isn’t the first media outlet to offer this kind of data; USA TODAY has been doing it since 2017, and The Daily Beast is currently testing something similar, but they haven’t been very public about it.
This new metric will allow brands more control where their ads are seen by pairing with articles that match their brand story and the predicted emotions of the reader. But while advertisers are on board with this new tool, consumers quite logically feel nervous about the idea. An article in Vox details one reporter’s issue with emotions being boiled down into such simplistic categories, but the reality is that this is a smart new data point, and here to stay. Mark me down as “wowed.”