I seem to have had the same discussion over and over lately here at Yahoo about site search. Yahoo has (or had, anyway) a high-profile web search technology and design team. So when we discuss these site-specific searches, talk always seems to circle back to the question, “How much should this site’s search resemble—or maybe even be—the Yahoo web search?”
On one hand, consistency and efficiency across the company are good, and the web search team has spent a lot of money and time optimizing their experience. On the other hand, sites such as Y! Music and Y! Sports have the luxury of knowing more context behind the user’s search requests than Yahoo.com does, and so more content-specific results seem like an easy win for users.
These conversations got me thinking quite a bit about search. Although search has only one user action—typing terms into a search box and submitting it to get results—the motivations behind that action can differ. As such, I think it’s wrong to try to find a single pattern for all search results. I propose that there are three distinct categories of search, and that each deserves its own set of patterns.
1 – Web/Exploratory Search
The first and most familiar type of search is the web or exploratory search. The user goals for this kind of search are “I’m looking for something/anything about [search term]” or “I’m looking for a specific [search term] and think [search engine] can find it.” The available data set (usually the entire known web) is vast and largely undefined. The user’s query is general, usually because s/he didn’t know what exactly to look for or didn’t think the search engine needed any more detail. The large number of results that are returned are formatted as web pages, mostly agnostic of the content those pages contain.
Google, Yahoo and others have spent a LOT of money and time optimizing the pattern for this type of search result. This is the reason why these companies’ search results all look virtually identical—compare searches for ‘UX’ on Yahoo!, Google, Bing, and AOL.
In the Google search results for ‘health care’ you can find pages containing articles, videos, local care centers, books, a web utility, the term definition, and more. Google changes the standard document result display, however, when they understand the content contained within the pages, such as local results, news, videos, books, etc., which leads me to the next category of search.
2 – Targeted Search
The second type of search is the Targeted search. The user goals for this type of search would be, “I’m looking for a specific subset of information on [search term]” or “I’m trying to find the optimal one of [search term].” This kind of search is possible when there is a single overall structure to the data because the user has explicitly selected a data type, e.g. books, flights, images, etc. There is usually a large set of results that are formatted to help users scan and compare the content on the resulting pages without pogo-sticking to and from detail pages. Good examples of this are Kayak, Amazon, New Egg, and sub-domain searches of the main search engines such as images.yahoo.com and shopping.google.com.
Obviously these results experiences should differ depending on the content they are searching. Much has been studied and written about optimizing these types of results, but it appears that even for the most popular data types, definitive patterns haven’t yet emerged (see video on Google vs. video on Bing). However, one widely adopted best practice is to surface important data facets (price, size, departure time, etc., often to the left of the results) and provide easy filtering of results based on those facets.
Google and Bing Video Results
3 – Shortcut search
The third type of search is the Shortcut search. The user goal in this instance is “I’m trying to find [search term] on this site.” Examples of shortcut searches are searching for a specific movie title on IMDb or a team name on ESPN. The data has a defined structure and the results set is very small—hopefully one exact match and fewer than 10 partial matches.
Interestingly, there is also overlap with the user goal I mentioned above for web searches, “I’m looking for a specific [search term] and think [search engine] can find it.” The user is searching for a single known or remembered entity, and is essentially counting on the search engine to produce it faster than browsing or even than using bookmarks.
Currently, most search experiences—both top-tier search engines and single-site searches—try to meet this search need by adding a custom result module at or near the top of the page. Notice the large Lakers module at the top of the ESPN results, for example. Or if you search for ‘Prius’ on the Toyota website you get a big module above the results with a nice picture and some price information. Similarly, the first Google result on a search for ‘Golden Gate Bridge Photos’ is a module that features and links to results from Google’s image search. In all of these cases the full results page contains a mix of either shortcut or targeted results and more general page results.
Shortcut results above the standard results on ESPN and Toyota
Additionally, many sites provide auto-complete functionality that is especially helpful for shortcut searches. This drop-down that appears as you type, containing matching popular or available terms, is called “search assist” by Yahoo and “search suggestions” by Google. Search assists help people avoid typos, discover possible terms, and sometimes allow them to jump directly to an item while completely bypassing the search results page, which makes them a true shortcut.
Where This Leads Me…
Understanding these three distinct types of search—web/exploratory search, targeted search, and shortcut search—has clarified my understanding of how results in different contexts should be formatted. It has also given me a framework for clarifying and making progress on our internal site-search discussions.
Additionally, I think that there is an opportunity to extend this differentiation into the design and implementation of search assists. In my next post, I will propose a new best practice for search assists, based on this understanding of the different types of searches. In the mean time, what are your thoughts on this? Is there a whole category of search that I’ve forgotten? I look forward to your comments and seeing you back here for part 2!
{ 4 comments… read them below or add one }
looking forward to part 2!
Quick observation, in your example search for the term “UX”, the Yahoo! results seem very off
Shhhh… I noticed that as well. Maybe part of the reason why the Y! results will soon be powered by Bing.
Great post, Sarah! How do you think federated searches fit into these three modes? For example: http://in.glue.yahoo.com/?query=apples
Interesting example, Stephen! I think that the federated search results are really just a collection of targeted search results. In the example you give above, there are separate modules for each assumed data type – recipes, images, answers, blogs. In some ways it’s similar to the current Google or Y! search result pages that present a mix of targeted results (a small bucket of image results or news results, for example), with the web results. Except in this case, the page interestingly leaves out the general web results.