The Context in which you would come to Wize, is Product Research. The key to Wize is the Wize Rank, which is algorithmically derived from Expert Reviews and User Reviews culled from all over the web.
If you drill one level down, however, there is not enough expectation setting at the sub category level. For example, on competitor Become.com, Belgian Chocolates with Orange Peels is advertised right at the top in a somewhat seductive way. It drew my attention straight away, and I tried the same search on Wize, which returned zero result.
What that means is that Wize is stronger in some categories than others. This is very normal. But they need to do a much better job of setting the users’ expectations. For example, TheFind, which we reviewed earlier, focuses on LifeStyle shopping, and bypasses the Electronics, Computers and Gadgets categories more or less. Wize needs to also align its segmentation strategy accordngly, and decide where it is the strongest. [To be entirely accurate, TheFind also doesn’t set the expectations well.]
Commerce, in my opinion, is one of the biggest problems with Wize. I did a search on a 17″ Flat Panel LCD Monitor. I found reviews, along with price tags in the $170 range. I bought the monitor for $80 on eBAY. I did not like the experience of not being able to do the product research alongside a compelling price comparison capability. I also did not like the fragmented user experience of doing the functinality research in one place, the price comparison shopping at another, and the actual transaction in a third place.
I am trying to move the web to as much of an integrated user experience as possible. I don’t want to expend 50 clicks for something that should be done in 5. Wize has a long way to go towards creating this integrated commerce experience.
Wize’s whole premise is based on accumulating the Community’s opinion from all corners of the web, which is quite powerful and useful. It, however, doesn’t really engage the community on its site per se. I don’t have a huge problem with this approach.
The juxtaposition of User Content and Expert Content is good. Often, when we research products, we need fairly detailed feature function that does not come from reviews, but rather, come directly from manufacturers and retailers. These “Specs” are also available on the Wize site, nice and cleanly organized.
I like the “Wize Rating”, “Buzz Monitor”, and “Users Love/Like/Hate It” features as quick, high level metrics based upon which to narrow down the choices.
I would say, Content is a strong suit for the company.
This gets us back to earlier discussion on Context, and Context-specific Search is precisely where the opportunity lies. Wize does a great job searching for pieces of content that are Product Reviews of various kinds (User, Expert), that’s only one dimension of vertical search.
Beyond that, in certain categories, they also do an excellent job in identifying the key vectors along which users search for items. An example is a Digital Camera, which can be searched by Price, Resolution, Brand, Camera Type, Optical Zoom, etc.
But this capability, as you broaden out of their core competency verticals, starts to fade, which is precisely why, expectation setting is quite important. [I tend to set expectations via Positioning and Messaging.]
Personalization exists in the ability to save research in a personal space. Not much beyond that rather simplistic capability. What about capturing preferences? If I have already purchased a Canon Powershot 8.3 Megapixel camera, perhaps storing and using that information for future sessions would be very useful. Then, you can show me accessories that are compatible with what I already own.
Even though the company identifies Become and ConsumerSearch as its chief competition, I would say it also competes with TheFind and other shopping engines to an extent, simply because, in the consumers’ mind, the Context is “Shopping”.
Nonetheless, for a young company, Wize shows promise, and I will give them the following Web 3.0 scores: Context: B-, Commerce: C, Content: A, Community: A-, Vertical Search: A-, and Personalization: B-.