The Search Lounge



Snap (No, not the old Cnet/NBC Snap. This is a new Snap)
Type of Engine: Popularity based on user data* and shopping.
Overall: Good.
If this engine were a drink it would be…a Bloody Mary. There’s different ways to make it; there’s lots of ingredients inside it; and you can see all the various colors and vegetables floating around inside whether you want to or not.

*I couldn’t think of a better way to say this, but basically Snap incorporates data into their algorithm about which sites users clicked on. In the search industry that’s known as CTR (click-through rate). If you know a cleaner way to express what kind of engine Snap is, please email me.

Snap uses click data, including which sites get clicked on and how many sub-pages are viewed by users, to help determine relevancy. They state they have data from 1 million users going back to January, 2004. This is an idea that has been kicked around and used to various degrees by other companies including LookSmart and Google AdWords (maybe others too). But Snap has taken things a step further; not only do they incorporate click-through rates but you can also manipulate your results by data from other users. I’ll explain more later.

--A quick aside: If you’re starting a new company why use the name of a deceased company that did business in the same industry?, the departed search engine that was jointly owned by Cnet and NBC and later became part of NBCi, existed for several years. So I was very surprised to see that a new Phoenix had arisen from its ashes (there is no affiliation between the two Snaps as far as I know).--

UI and Features
Because there’s a lot going on with Snap’s UI I’ve broken this section into subsections.
The Home Page
The first thing you’ll notice is their cluttered homepage. It’s jumbled compared to the nowadays ubiquitous Google type of UI. But if you take a moment there’s some pretty good stuff to see. On the left of the page are a few summaries of popular queries: Top Products, Top People and Top Music. On the right are the latest articles from Snap’s Blog. In the middle of the homepage there’s a lot…there is a running tally of the number of searches done. There are also graphs; click on those and you’ll see all kinds of things like stats about Revenue, Search and Advertisers. Check it out. Seeing as how I have a weird hobby of analyzing search queries, I like the keyword statistics page, but I’m not sure the average user has much use for it. But it’s all part of Snap’s goal of transparency.

It’s certainly a different type of homepage, but I think they’d do better to put some of this information into their About Us section or elsewhere. When I first logged onto the Snap site I wasn’t sure if I’d reached a regular search engine or if it was something else like an enterprise search where I was being sold a search product. I am glad that they have all this information, but I wish they’d remove it from the landing page and put it elsewhere.

Related Keywords/Count
After you do a search you’ll see in the upper right corner a list of similar searches and their frequency. Interestingly enough, I remember that the original (the Cnet/NBC one) had a similar feature that displayed related searches. I tried a search for library disaster plans and the related keywords were things like library of congress, floor plans, wet, libraries, etc. None of the terms were relevant to my search as stand-alone terms. I clicked on wet because I was curious about it, and of course the related keywords for wet were porn queries that I won’t repeat here.

Refining Queries
After you do a search there’s a search box called Type Here To Refine where you can search within results, almost like a “Control F” find functionality merged with a search functionality. Give it a try, it’s fun to play around with.

On the search results page there are several columns that appear along with the site results. Clicking on columns lets you sort results in different ways. This is a nice feature, but again it’s one that most searches really don’t need in my opinion. I can see its value for advertisers who may want to purchase a keyword –think of the Overture bidding model – but for the average searcher sorting by conversion rate isn’t necessary. (By the way, the founder of Snap is Bill Gross, the same guy who founded Overture.) You can even reverse sort by rank, but again I’m not sure why I’d really want to do that. The columns in the results are as follows (note: I started to define each one but then realized Snap has a nice glossary so I took it directly from them.):

1. No. of Clicks is the count of users in the Snap Network since January 2004 who did the same search you just did, and who then clicked on this listing. Typically, the higher the better. An asterisk* indicates that the data is estimated.

2.Average Page Views is the average number of pages of this site that were viewed by users in the Snap Network who did the same search that you just did. Again, higher numbers are typically better. An asterisk* indicates that the data is estimated.

3.Cost to Advertiser is the amount that an advertiser pays Snap for referring a customer who subscribes, purchases, bids, registers, or downloads.

4.Conversion Rate is the percentage of users who subscribe, purchase, bid, register, or download at an advertiser's site. You guessed it, higher numbers are better — they typically mean that this advertiser is better at fulfilling customer needs.

5.Domain is the top level domain of the site. Most commercial sites in the United States are '.com' sites. However, depending on the search term, there are often excellent results in other domains, such as .edu (educational) and .gov (government).

You can filter multiple columns simultaneously. For example, in a column that displays numbers, type in '>' (greater-than), '=' (equal-to), or '<' (less-than), followed by a number, and at the same time filter on a phrase in another column.

Shopping Queries
Shopping queries, like ipod, produce different columns. I could sort by price range, type of memory, amount of memory, weight, etc. These columns vary based on the shopping query, so if you search for laptops you’ll see columns like OS, screen size, etc. Very nice because what they’re doing is customizing the UI based on the query, which I love. If you click on one of the results you’ll get a nice preview lower on the page. Although all the results were relevant in that they were indeed portable music players, many of them were actually ipod competitors. It’s a common practice for shopping engines to do this, but I think it impacts negatively on relevance. If I wanted to see ipod competitors I’d search for something like portable MP3 players. Many commercial searches, such as bose wave didn’t get any special shopping columns. Not so good but maybe they’re still building their shopping list.

Corporate Queries
Another type of query that gets special treatment are companies like Amazon. The interface is different: there is a list of most popular sub-pages, a cached snapshot of Amazon’s homepage, a company snapshot, and news headlines. A nice feature, but again it’s inconsistent because when I searched for Nike I got a regular results page.

Next to each result there’s a spot for a logo. It’s at the domain level so that AOL member personal pages have the AOL logo even though they’re not corporate-sponsored. But as with other things on Snap this is inconsistent. For my Nike query the only sites that had logos were a sub-page from MSN and a sub-page from Amazon.

Query Examples
I cheated a little and included several query examples in the UI and Features section in order to illustrate Snap’s features. But in terms of relevancy, Snap is pretty good. For my library disaster plans query the results were good. Though I should point out that result #1 wasn’t so good because it’s a FEMA information sub-page titled “library.” But I can see why Snap returned it so I’ll let it slide. I reordered results by number of clicks and things looked slightly better because the FEMA site went away. Though again I should point out that for this obscure query the highest number of clicks was only 5. For context the highest number of clicks for Nike was 1,735 followed in second place by 917.

I tried cliff house restaurant san francisco and the results were good. The official page was #1, though it’s interesting to note that it had 1 click whereas the second result, a Yahoo Travel restaurant review, was highest with 8 clicks. This is a good example of when click data can be misleading for algorithms. Even if more people click on the review the official page needs to be the first result. And Snap got it right.

I like what Snap’s trying to do, but I fear they’re overloading the average user with too much information. They’re showing the guts of their technology rather than incorporating it seamlessly behind the scenes. After you do a search and are waiting for the results, which can be slow, the screen will say things like Preparing Data for Display, De-Duplicating Listings and Getting Top Listings. I appreciate the honesty but I’m not sure it enhances my experience.

The columns are fun to play around with but I think they take up valuable real estate that could be used for displaying more metadata and text about the sites. Maybe Snap can play around with its UI the way A9 has and make it more customizable so that I can eliminate or add columns as I see fit.

They also need to take some of their nice features, such as the differentiated results page for shopping queries, and roll that out for many more terms. I’ll give them the benefit of the doubt and assume they’ll get to this soon.

I want to say one more thing though: Snap is pushing things and I really appreciate that. I like very much that they’re making an effort to be unique and I will definitely pop in periodically to see how they’re doing and what new innovations they've implemented.


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