of your search activity.
Use pattern analysis, session analysis, failure analysis, and audience analysis to analyze your query data, diagnose problems, and determine ways to improve your site’s content, navigation, and search system.
Use goal-based analysis to determine new ways to measure your site’s performance and connect search to your organization’s KPI (Key Performance Indicators).
Chapter 3. Pattern Analysis
Getting Started with Pattern Analysis
Finding Patterns in the Long Tail
Anti-Pattern Analysis: Surprises and Outliers
The next five chapters (including this one) cover various ways to analyze and derive new insights from your query data that can directly improve your site’s user experience. We’ll start by looking at the patterns and oddities that emerge from your data if you play with it (and stare at it long enough).
Analysis as a Form of Play
In pattern analysis, we look for what our queries have in common: tone, length, topic, type, and more. We also explore what’s odd—are there queries that don’t fit with the rest? We then study those groups and misfits to see if we can learn something new about our searchers and the content they want and need. Does their language match the tone of our content? Are they requesting certain types of content more than others? Do searchers demonstrate certain kinds of information needs at particular times of the year? Or day?
How does pattern analysis work? We simply play with our data and see what emerges. Yes, it’s essentially that simple: we play. And it’s as fun as it sounds.
The Simple Part
The simple part about pattern analysis is that as humans we’re naturally built to detect patterns, especially semantic ones. Don’t believe me? Well, take a quick look at this list of words in Table 3-1.
Table 3-1.
| Google Common Queries | |
|---|---|
| #1 Indiana earthquake | #11 earthquakes today |
| #2 isabelle caro photos | #12 laura govan |
| #3 candace cameron bure | #13 moshe katsav |
| #4 lily shang | #14 indystar |
| #5 amazon eve | #15 happy new years |
| #6 isabelle caro before anorexia | #16 new year quotes funny |
| #7 new years eve 2011 | #17 brie iarson |
| #8 billy taylor | #18 Christine O donnell |
| #9 jamie foxx | #19 billy boyd |
| #10 2011 predictions | #20 feliz ano nuevo |
| A set of common queries logged by Google Trends, December 30, 2010. |
What did you notice? Any patterns emerge? Any outliers?
You may have noticed many queries that were people’s names (for example, laura govan and billy taylor), while others were related to the end of the year (new year quotes funny and 2011 predictions), and the rest were an odd mix of stuff pertaining to mostly earthquakes.
Or you might have divided the people into different categories (for example, politicians, musicians, comedians) or by gender. You might have sussed out some geographic issues, such as queries that have some regional or local significance (for example, former Israeli president moshe katsav and indiana earthquake). Or something else completely different.
Whatever patterns emerged for you, you probably performed some or all of these four pattern analysis tasks without even realizing it:
You sampled the content to get a sense of what was there.
You grouped things that seemed to go together.
You may have sorted them to get a different collective look at them.
And you likely iterated your way through a few passes at them before you were satisfied with what you came up with.
If you’re not sure how to begin pattern analysis, try these four tasks and let your mind wander through the data. And remember, no single pattern is the “right” one!
The Fun Part
When I teach workshops on site search analytics, I have my students do a hands-on pattern analysis exercise. Even though I intentionally provide minimal instructions, it’s amazing how quickly they become absorbed in the process of detecting patterns and categorizing queries. (And no, my students aren’t exclusively data modelers, librarians, or other data nerds.) They arrive at conclusions that aren’t the same—their groups overlap, their interpretations differ—and they greatly enjoy comparing their results. Some revel in their differences; others are, frankly, a little uncomfortable with the lack of a “correct” set of patterns. That’s the precise moment at which I recommend that they consider following up their pattern analysis with a more qualitative technique, like card sorting, to determine the most common, if not “correct,” groupings.
Some of my students are skeptical of
