Brand Pit: Giving companies a clear picture of their fans

Brand Pit: Giving companies a clear picture of their fans

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Back in September we told you about Brand Pit, a startup which at that time won the Startup Sauna Tokyo pitch competition. It’s an analytics tool for brands that are trying to learn more about their fans, relying on image recognition technology instead of text or keyword analysis.

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Brand Pit image recognition finding Starbucks logos

Currently their focus is on analyzing images shared on Instagram, with some Facebook and Twitter analysis as well. By detecting brand logos in socially-shared images, any company that uses Brand Pit can gain valuable insights about who their fans and influences are, or even view a geographic heat-map of activity around their brand. In this way, they can get around the many problems associated with keyword analytics such as language barriers or spam.

I had a chance to catch up with the company’s founder TT Chu (he’s the one in the video above), when they were pitching at e27’s Echelon Tokyo Satellite event last week [1].

He tells me that in the future, they plan to expand the scope of their image recognition technology in a way that will also expand its value for brands:

We intend to detect more than just logos, brands and products. we are trying to extract and identify other information presented in the photo, such as the environment and the objects surrounding the branded products. This peripheral information will allow us a more accurate in-sight into the real situations/conditions where the products are being used/consumed. This piece of information is critical in segmenting the customer base.

I understand that their image recognition technology has been developed in house (primarily by Chu himself), and one of its key advantages is that it can perform well even when applied to user-generated images. So even when the images are poor – either too dark, maybe obscured by another object, or even if they’re too small, that Brand Pit can detect them where other technology might not.

When it comes to detecting peripheral objects for context, Chu tells me that they can even detect low-contrast objects like wine glasses, which is certainly an impressive feat.

They’re currently looking to raise funds to take their startup to the next level. And given the size and growth of the business analytics market, and the fact that they don’t really have many competitors, I expect that it won’t be too long before we have some more good news to share about Brand Pit.

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  1. Shamefully I couldn’t quite remember where I knew him from when we met again this time. I’m horrible with faces, much to my embarrassment.  ↩