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Search Analytics for Your Site

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Search Analytics

Frequently Asked Questions

These common questions about site search analytics and their short answers are taken from Louis Rosenfeld's book Search Analytics for your SIte. You can find longer answers to each in your copy of the book, either printed or digital version.

What is site search analytics (SSA)?

If your Web site or intranet has a search engine, then you can log what users are searching for, tally queries to see what's most important to your users, find out if they're succeeding, and if they're not, determine what might be getting in their way. Chapter 2 provides a short introduction to SSA (which is often also known as search log analysis).

Isn't SSA the same as SEO?

Not at all. Search engine optimization looks for ways to make Web-wide searches (for example, via Google and Bing) more likely to find your site. SSA looks for ways to improve how searching works on your site, using your site's own search engine. That said, SSA and SEO share much in common, and can influence each other; for example, pp 90–91 shows how SSA may help you determine better keywords to bid on.

How does SSA differ from other kinds of analytics?

SSA is based on data that comes from actual usage of your site, just like other forms of web analytics. But it's far more semantic, as it is made of search queries—users' expressions of what information they want from your site in their own words. That's why SSA does a better job of depicting and helping you understand users' intentions than any other form of web analytics. Chapter 3 provides some tools for analyzing and understanding users intentions.

Why do I need SSA?

Because SSA can help drive—and back up—your design decisions. Because you already have query data and want to put it to good use. Because you want to make your search engine find content better (Chapter 8), your site easier to navigate (Chapter 9), and your content more effective (Chapter 10). And because your competitors aren't using it, and you'd like to destroy your competitors.

Where does query data come from, and what tools do I need to analyze it?

When someone uses your site's search engine, they type a query that the engine will try to match with your site's content. That query can be (and likely is being) saved. It's either logged in a text file by your search engine or intercepted and kept in a database by your analytics application. Search engines occasionally and analytics tools increasingly provide reports that help you analyze the data, but ideally you'll explore and learn more from the data in a spreadsheet. Unfortunately, there's no one way to get your hands on the data, because how you can get at the data often depends on what search engine you're using. Talk to your organization's IT people for help and show them this book if they ask why you need access to the data.

I'm not a "data person," so why should I read this book?

Organizations are putting more and more pressure on designers to justify their decisions with evidence. Fortunately, SSA is real data that's also semantically rich, so you won't be just looking at numbers. And you won't need to perform statistical tests to learn from it; in fact, it will be immediately obvious to you how it can help improve your design work.

This isn't part of my job description (or anyone else's) so who should do this work?

User researchers and the designers who rely upon user research—such as information architects, content strategists, interaction designers, and knowledge managers—should at least consider SSA as a part of their standard kits of research tools, even if it's not something they use on a regular basis. The same goes for web analytics practitioners: SSA is an important tool, just like clickstream analysis. The best part is that no one needs to do SSA as a job—it scales nicely, depending on the time you have available (see our discussion of the Zipf Distribution on pp.19–21).

How do I actually analyze query data?

First, "play" with the data by looking for patterns and surprises that suggest what's important to your searchers and what kinds of content will best meet their needs (Chapter 3). Then identify and learn from searchers' failures (Chapter 4). See what happens in the course of single search sessions (Chapter 5), and tease out what's important to specific audiences of searchers (Chapter 6). Finally, measure your site's performance better by injecting search metrics into how your site is performing at meeting its goals (Chapter 7).

How does SSA fit with other user research methods?

SSA is based mostly on quantitative behavioral data; therefore, it's useful to combine it with your qualitative user research methods and tools. For example, use query data to help determine candidates' tasks for your task analysis studies or to beef up your personas with real data. Chapter 11 talks about how SSA fits into the broader worlds of both user experience research and web analytics, and how it may be a great means for bringing them together.

If it's so great, how come more people aren't taking advantage of SSA?

Good question. Most people don't know that query data even exists, much less that their organizations likely already own some. Those who do often run into political problems when they try to get their hands on the data, because it is usually owned by IT or some other group. (This is getting easier thanks to ever-improving analytics tools.) Finally, there hasn't been much practical information on how to analyze the data. Maybe this book will help.

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