have a good idea of which statistical tests you should be applying.
So let’s have some fun.
[8] See Wikipedia for more on Exploratory Data Analysis: http://en.wikipedia.org/wiki/Exploratory_data_analysis
Getting Started with Pattern Analysis
Good news: you already have the tools—your brain included—necessary for pattern analysis. I’ll wager that you already own a copy of Microsoft Excel; if not, you could certainly create a spreadsheet in a free tool like Google Documents or OpenOffice. To get started, you’ll need some minimal data: queries (at least from the short head) and how frequently they were searched on your site. You might grab these by exporting them from your analytics application or by using this PERL script
Next, create two columns in your spreadsheet—one for your unique queries, the other for their frequency counts—and import or paste in your data. If you know the date range for your data sample, mention it in the spreadsheet so you won’t forget it later. Here’s an example of such a spreadsheet that contains common queries from Michigan State University’s Web site in Figure 3-1. We’ll return to the MSU example throughout this chapter.
Figure 3-1. A week’s worth of Michigan State University queries, sorted by frequency.
I’ve created a souped-up version of this spreadsheet (shown in Figure 3-2), which I encourage you to download and use as a template. (You can get it here:
Rank: Each query’s rank in terms of frequency.
Percent: The percentage of overall search activity that each unique query is responsible for (out of all your site’s search activity).
Cumulative Percent: The percentages of all the queries added up. If you’re looking at query #3 (registrar), the Cumulative Percent shows the sum of the first three queries’ percentages (4.6391 = 3.0436 + 0.8509 + 0.7446).
Count: How often each unique query was searched.
Unique Query: The query itself.
Link: I’ve done a little fancy programming to provide a live link to execute each unique query on the Michigan State Web site. This just makes it easier to test each query.
I’ve also provided some other information at the top—such as the average number of terms per query—as a pair of fancy Zipf distributions to help you visualize the data.
Figure 3-2. The same data as in Figure 3-1—now all gussied-up.
Patterns to Consider
Now go ahead and take a deeper look at and start playing with the MSU queries. Stare at them for a bit, scan up and down a bit, and then stare again. Do you detect anything interesting, or surprising, about the language searchers are using in their queries? Were you surprised that stuinfo is used more frequently than stu info? Or that map was as high (or low) as it was? Did you happen to notice lots of queries that seemed to deal with places on campus and others that seemed to be about courses?
With each new pass at the data, you’ll come up with more questions. Following are some of the types of patterns you might encounter when analyzing your own query data.
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