My company recently added a client for whom Like.com is a direct competitor.
The Web site was much-hyped and reviewed 10 months ago, when it fired up, including being dubbed the "First True Visual Image Search," but little has been made of it since.
If you believe the traffic trend data from Alexa, traffic to Like.com has mirrored interest by the media and blogosphere, having a spike at launch, followed by a marked decline.
Like.com employs the technology ofthat focused primarily--until Like.com became a factor--on facial recognition. Now that same recognition software is used to characterize fashion design and trends for the purpose of online shopping.
See a pair of pants or dress you like? Use Like.com's selection box to highlight the area of the apparel that catches your eye, and the engine will go out to find other pieces of clothing with similar features (at one point, this included photos of celebrities upon which to focus, but those, evidently, are no longer part of the site).
The question is, does this functionality have any real use, or is it just a neat Web application? Does it increase the likelihood of shoppers being paired with what they will buy, or is it simply another Web 2.0 exercise that will fall to the wayside with the other cool-but-useless applications out there?
Shopping for clothes is not a main focus in my life, but regardless, I decided to take two test runs on Like.com in an attempt to find something that I might at least consider buying.
First, I tried to find an item I'm familiar with by using the interface to guide me from the home page to the particular item. Second, I used Like.com in an attempt to match me with something I had no need for or knowledge of previous to the search engagement. The results were interesting.
In the first experiment, I tried to find a popular brand of pants I had tried on (and not purchased) in a store last weekend: a pair of Levi's Low Boot Cut 527 Jeans. Like.com has a photo of a pair of men's jeans on its home page, so I began there, highlighting the upper part of the photograph that most matched the Levi's. The "Likeness" menu popped up, giving me one of three choices to apply to this visual search: Color, Shape, or Both. I chose Both.
The results were wide-ranging, to say the least. With approximately 1,800 results returned to me, I was no closer to my particular Levi's, much less any Levi's. After scrolling through several pages of results (already outside of the image search boundaries), I finally came upon a pair of Levi's. I selected that pair of jeans, hoping to narrow my search.
At that point, however, I was no longer offered the visual search interface. Instead, I was taken to a purchase page at Amazon.com for that particular pair of jeans. Search over. Not even close. I found no way of using the visual-search interface to narrow my results.
The second time around, I looked for a pair of running shoes. I'm not sure what I want, in that regard, so I clicked on the Men's Shoes category and found a running shoe that looked good to me. It was a tennis shoe style, and I selected the toe of the shoe with the visual search interface. Lo and behold, I got a page full of tennis shoes to choose from. I clicked on a pair of Converse that caught my eye and, like last time, was taken outside of the Like.com Web site (to Zappos.com purchase page this time). I didn't buy the sneakers, but they did fulfill the general requirements of what I was looking for--I liked them!
With these experiences in mind, I have to say that as a pure form of search, this type of visual search is extremely limited. Like.com could certainly make improvements to its ability to narrow a search using the tools, but even then, I suspect that sooner rather than later, a wall would be found.
While visual search is useful in some shopping searches--browsing searches as opposed to specific searches--it is only in tandem with traditional textual search that the tool has any real use. Still, it's an interface that could grow beyond a "cool app," especially if it is branded as just one search tool among many, rather than as a destination in its own right.
It could make for a nice complement to Google's search tool set; perhaps Google should acquire it?