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While out and about, you notice a pair of shoes that you like. Maybe they're on a shelf in a store; maybe they're by your coworker's desk on the floor. You pull out your phone, take a picture, and an app helps you track down those shoes or another similar pair.
The idea sounds simple enough. The execution of that idea is not, though, and the approaches to tackling the problem have been varied.
A number of startups and larger companies have launched apps in recent years that attempt to do a version of the above. Some, like Pounce, let users scan products in circulars to pull up additional information.
Others like The Hunt let users snap pictures of products offline and get feedback from a community of users online.
Then there's Amazon, which released a standalone app called Flow in 2011, which lets users scan products and pull up results from Amazon listings. That effort apparently proved worthwhile enough that Amazon decided to fold the feature into its flagship iPhone app last month.
The challenge for these image-scanning shopping efforts, according to Sucharita Mulpuru, a retail analyst with Forrester Research, is building up a large enough database products and a seamless enough user experience that the app is actually useful and not frustrating.
"It's going to be really, really tough to pull it off," Mulpuru says. "You're better off putting keywords in Google. Is that solution that bad right now?"
If you judge strictly based on investment trends, then the answer may be yes.
Two startups in the space recently announced raising significant funding rounds to advance their image-recognition shopping tools. Slyce, a Toronto-based startup, raised $10.75 million this week — with plans to raise even more in the next few months — to continue developing a platform that would help brands take advantage of visual search. Mark Elfenbein, chief digital officer at Slyce, described it as a Shazam for shopping, though he prefers to liken it to Amazon Flow.
"We have seen a fair amount of image recognition that exists online," Elfenbein says. "They tend to require a lot of customer input."
Slyce is trying to create something more seamless: Technology that brands can incorporate into apps and mobile websites to let shoppers photograph real-world objects with their phones and get relevant information from the retailer's inventory. Slyce partners with retailers to get attribution details tied to particular product SKU numbers. Elfenbein offers the example of one retailer who is interested in incorporating this technology for wedding registries, so customers could quickly add items to the list on the go.
ASAP54, a UK startup, raised $3 million last month to help users track down shopping suggestions based on real-world images. It has also been likened to a Shazam for shopping, though the secret sauce is the human element: If the image-recognition technology doesn't pull up a solid result, then employees at the startup will come up with a response on their own.
"You fall in love, you have an emotional feeling," Daniela Cecílio, founder of ASAP54, told Mashable in an earlier interview. "It's a painful process to go to Google and put it into words."
All of which brings us back to the original problems laid out by Forrester's Mulpuru. Both startups promise more sophisticated image-recognition shopping experiences, but will either be able to provide broad and accurate enough results to cater to user needs? Part of ASAP54's plan is to scale up its responses by bringing on more personal shoppers over time. Slyce, on the other hand, will focus on building out solutions for particular retailers.
"Good companies solve existing problems; great companies help you discover a problem you didn't know you had," Mulpuru says. "This could fall in that latter bucket, or they could be solutions looking for a problem that never takes off."
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