Pictures are sometimes called the “dark matter” of the Internet because they remain stubbornly unsearchable. Tagging provides some relief, but still relies on humans to recognize images – sneakers, iPhones, Rebecca Black – and enter them by hand, one picture at a time. With more than 3 trillion images on the Web and 6 billion people in the world (give or take), the odds of getting to all of them aren’t good.
Pixazza, a photo-tagging network that turns pictures into mini shopping hubs, isn’t trying to tag all 3 trillion. But it has come up with a process that makes tagging scalable enough that publishers can start generating revenue from their pictures.
Over the past two years, Pixazza has recruited a small army of stay-at-home workers who go through participating web publishers’ photos one by one tagging every sellable product they see – handbags, hockey sticks, eye shadow, etc. Those tags come with links to retail sites where such items can be bought.
So when a consumer rolls over a picture of, say, Jessica Simpson wearing a red hat, a pop-up box appears next to the image with a link to a site selling a similar red hat. If the user buys the hat, a portion of the sale goes to Pixazza, which then shares a percentage with the publisher.
Jim Everingham, CTO and cofounder of Pixazza, says he got the idea for his company after his wife spent four hours online chasing down a pair of shoes she saw in a web photo from Sex and the City two years ago. “It occurred to me then,” he said, “if she could have just moused right over that photo and bought it then,” she could have saved a lot of time.
Everingham thought his background in voice-recognition technology – he’s a veterans of TellMe Networks, which is now part of Microsoft – would provide an easy transition into image recognition. But he soon realized that image recognition was considerably tougher than voice recognition, he said, “because you have judgment and taste and even context problems.”
Instead, it was his background in crowdsourcing – he and several other Pixazza engineers worked together at LiveOps – that made the idea workable. “We were like, this is a great application for crowdsourcing,” he said. “We can build a big human image-recognition engine.”
Two years later, Pixazza pays about 150 freelance taggers – mostly women, mostly in the U.S. – anywhere from 10 to 35 cents a tag to mark up images submitted by publishers. A proprietary group-chat platform allows the taggers to stay in touch with each other and community manager Catherine Stiteler – Pixazza’s original tagger – while they work. The average tagger makes about $8 to $10 an hour.
The company launched with fewer than 10 full-time employees, but today employs about 50, not including taggers. Pixazza claims about 90 million uniques a month, according to Everingham.
Becoming a tagger is not easy. Everingham says they have received more than 6,000 applications, but only approve people who can claim some expertise in their field (fashion is Pixazza’s primary category, but is moving into sports, travel, and home). Stiteler is one of three moderators who check all tags for relevance and good judgment. As taggers prove themselves, their ratings go up; taggers who do a poor job get low ratings and risk being eliminated.
Shara Johnson , 34, a freelance media producer from Virginia Beach, has been one of Pixazza’s taggers from the company’s early days. She says the job allows her to make money during the downtime between production projects because she is able to work any hours and as frequently as she wants. But there’s a social benefit to the job, too, she says. “There’s a core group of girls, and we’re very close,” she says. “We even talk on the phone or text or talk to each other on Facebook.”
To be sure, Pixazza is not the only company working on in-picture advertising. Image Space Media, GumGum and Like.com (now part of Google) all offer methods of turning static images into shopping hubs.
But Pixazza’s novel use of crowdsourcing to solve the problem is what’s drawn the interest of digital analysts and some investors. “What I love about the Pixazza model is that it’s win win win,” said Saad Khan, a partner at CMEA Capital and an early investor in Pixazza. “You have publishers who are looking to make money, the advertisers who are looking to sell stuff, and then you have this whole new category of work you’re creating for people.”
Of course, those taggers represent an expense that other ad networks don’t have, which could make it difficult for Pixazza to stay competitive. But while maintaining an army of taggers “is a little more expensive than using purely algorithmic results,” said Chas Edwards, who is head of publisher development, the “high-quality experience” it creates “drives much more inventory for us than if we were to do some kind of low-rent ad network that was seen by consumers as advertising rather than a product enhancement.”
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