Using Search Analytics to Diagnose What’s Ailing your Information Architecture

Using Search Analytics to Diagnose What’s Ailing your Information Architecture ASIS&T IA Summit Las Vegas, Nevada March 24, 2007 Rich Wiggins & Lou Rosenfeld www.rosenfeldmedia.com/books/searchanalytics

Trying to Fit into the IA Summit 2007 Theme… Rich Information Rich Interaction Rich Relationships Rich Wiggins

Thesis
By analyzing search logs, you engage in a conversation with your customers
At best, it’s a two way conversation:
Your users tell you what they seek
You tune your search engine (and your site) to give them what they seek the most
If you’re not analyzing your search logs, then you aren’t listening to your customers
Search is too important to leave in the hands of robots

The Wonderful Things Search Engines Do
Help harness massive amounts of content
Thousands, millions, billions of URLs
Cut across barriers
Document structure
Topical structure
Institutional structure, silos

The Horrible Things that Search Engines Do
Confuse low-value content with vital content
And point to obsolete content
And draft, internal, duplicative content
Rank leaf pages ahead of starting points
Rank popular or personal pages ahead of official content

MSU Keywords: Accidental Thesaurus
Circa 1999 MSU’s local AltaVista stopped scaling
Search for “human resources” and you get resume for a student in the HR program
We had to do something
We asked AltaVista for a way to goose the real HR site to the top of the hit list
They didn’t deliver
So we rolled our own Best Bets service, called it MSU Keywords
And it worked!

Methodology
Study the most popular unique searches
Map each to appropriate URL
“human resources” -> hr.msu.edu
“campus map” -> www.msu.edu/maps
Watch the results:
User complaints go down
So do content provider complaints
Continue to watch, learn, and act

Google Has Trained ’Em to Search First
Top 10 searches, www.msu.edu , Jan 2007
“ map” is a top search even with a map logo on the home page
MSU Usability Center, testing 2006 redesign, ordered testers to stay away from the search box
Nielsen 50% theory may underestimate
cata 3204 angel 3229 spartantrak 3575 bookstore 3584 schedule of courses 3690 study abroad 3745 library 4320 im west 5184 map 5859 campus map 7218 Unique Query Count

The Zipf Curve: Short Head, Torso, and Long Tail

Keep It In Proportion
7218 campus map
5859 map
5184 im west
4320 library
3745 study abroad
3690 schedule of courses
3584 bookstore
3575 spartantrak
3229 angel
3204 cata

Find the Sweet Spot; Avoid Diminishing Returns department of surgery 7 80.00 7877 hotels 124 50.02 500 msu union 295 40.05 221 computer center 650 30.01 98 webenroll 1351 20.18 42 housing 2464 10.53 14 campus map 7218 1.40 1 Query Count Cumulative Percent Rank

Look for Topical Patterns and Seasonal Changes

Does Best Bets Apply to Everyone?
Walter Underwood, former chief architect of Ultraseek:
Instead of Best Bets, Get a Better Search Engine
Best Bets requires human labor
Commitment of time and attention
… so do good search engine implementations

We Didn’t Start the Fire; credit to:
Vilfredo Pareto, circa 1890 – “the law of the vital few” (simplified as “80-20 rule”)
George Kingsley Zipf, Harvard, circa 1932 – counting the words used in Joyce’s Ulysses
“ the” is more common than “no” or “Dublin”
Bradford’s Law of Scattering, circa 1934 – a small number of journals accounts for a large percent of all important papers
Cited, most importantly, by the pricing model of Elsevier for leading scientific journals
Many other best bet pioneers: Microsoft, Raytheon, BBC, ESPN, AOL

Where will you Capture Search Queries
The search logs that your search engine naturally captures and maintains as searches take place
Search keywords or phrases that your users execute, that you capture into your own local database
Search keywords or phrases that your commercial search solution captures, records, and reports on (Mondosoft, Web Side Story, Ultraseek, Google Appliance, etc.)

Anatomy of a Search Log (from Google Search Appliance)
Critical elements in bold: IP address , time/date stamp , query , and # of results:
XXX.XXX.X.104 - - [ 10/Jul/2006:10:25:46 -0800] "GET /search?access=p&entqr=0&output=xml_no_dtd&sort=date%3AD%3AL%3Ad1&ud=1&site=AllSites&ie=UTF-8&client=www&oe=UTF-8&proxystylesheet=www&q= lincense+plate &ip=XXX.XXX.X.104 HTTP/1.1" 200 971 0 0.02
XXX.XXX.X.104 - - [ 10/Jul/2006:10:25:48 -0800] "GET /search?access=p&entqr=0&output=xml_no_dtd&sort=date%3AD%3AL%3Ad1&ie=UTF-8&client=www&q= license+plate &ud=1&site=AllSites&spell=1&oe=UTF-8&proxystylesheet=www&ip=XXX.XXX.X.104 HTTP/1.1" 200 8283 146 0.16
XXX.XXX.XX.130 - - [ 10/Jul/2006:10:24:38 -0800] "GET /search?access=p&entqr=0&output=xml_no_dtd&sort=date%3AD%3AL%3Ad1&ud=1&site=AllSites&ie=UTF-8&client=www&oe=UTF-8&proxystylesheet=www&q= regional+transportation+governance+commission &ip=XXX.XXX.X.130 HTTP/1.1" 200 9718 62 0.17
Full legend and more examples available from book site

Sample Query Analysis Report Excel template available from book site

Querying your Queries: Some basic questions 1/2
What are the most common unique queries?
Do any interesting patterns emerge from analyzing these common queries?
When common queries are searched, are the results the ones your users should be seeing?
Which common queries retrieve zero results?
Which common queries retrieve a large number of results, say 100 or more?

Querying your Queries: Some basic questions 2/2
Which common queries retrieve results that don’t get clicked through?
What page is the top source (referrer) per common query?
What is the number of click-throughs per common query?
Which result is most frequently clicked-through per common query?
What’s the average query length (number of terms, number of characters)?
Which URLs are users searching for?

Tune your Questions: Broad to specific
Netflix asks:
Which movies most frequently searched?
Which of them most frequently clicked through?
Which of them least frequently added to queue (and why)?
Examples:
“ OO7” versus “007”
Porn-related (not carried by Netflix)
“ yoga”: not stocking enough? or not indexing enough record content?

SA as Diagnostic Tool: What can you fix or improve?
User Research
Interface Design: search entry interface, search results
Retrieval Algorithm Modification
Navigation Design
Metadata Development
Content Development

User Research: What do they want?…
SA is a true expression of users’ information needs (often surprising: e.g., SKU numbers at LL Bean; URLs at IBM)
Provides context by displaying aspects of single search sessions

User Research: …who wants it?…
What can you learn from knowing these things?
What specific segments want; determined by:
Security clearance
IP address
Job function
Account information
Which pages they initiate searches from

User Research: …and when do they want it?
Time-based variation (and clustered queries)
By hour, by day, by season
Helps determine “best bets” and “guide” development

Search Entry Interface Design: “The Box” or something else?
SA identifies “dead end” points (e.g., 0 hits, 2000 hits) where assistance could be added (e.g., revise search, browsing alternative)
Syntax of queries informs selection of search features to expose (e.g., use of Boolean operators, fielded searching)
… OR…

Search Results Interface Design: Which results where?
#10 result is clicked through more often than #s 6, 7, 8, and 9 (ten results per page)
From SLI Systems (www.sli-systems.com)

Search Results Interface Design: How to sort results?
Financial Times has found that users often include dates in their queries
Obvious but effective improvement: allow users to sort by date

Search System: What to change?
Identify new functionality: Financial Times added spell checking
Retrieval algorithm modifications:
Deloitte, Barnes & Noble use SA to demonstrate that basic improvements (e.g., Best Bets) are insufficient
Financial Times weights company names higher

Navigation: Any improvements?
Michigan State University builds A-Z index automatically based on frequent queries

Navigation: Where does it fail?
Track and study pages (excluding main page) where search is initiated
Are there obvious issues that would cause a “dead end”?
Are there user studies that could test/validate problems on these pages?
Sandia Labs analyzes most requested documents to test content independent of site structure; results used to improve structure

Metadata Development: How do users express their needs?
SA provides a sense of tone: how users’ needs are expressed
Jargon (e.g., “cancer” vs. “oncology,” “lorry” vs. “truck,” acronyms)
Length (e.g., number of terms/query)
Syntax (e.g., Boolean, natural language, keyword)

Metadata Development: Which metadata values?
SA helps in the creation of controlled vocabularies
Terms are fodder for metadata values (e.g., “cell phone,” “JFK” vs. “John Kennedy,” “country music”), especially for determining preferred terms
Works with tools that cluster synonyms (example from www.behaviortracking.com), enabling concept searching and thesaurus development

Metadata Development: Which metadata attributes?
SA helps in the creation of vocabularies
Simple cluster analysis can detect metadata attributes (e.g., “product,” “person,” “topic”)
Look for variations between short head and long tail (Deloitte intranet: “known-item” queries are common; research topics are infrequent)
known-item queries research queries

Content Development: Do we have the right content?
SA identifies content that can’t be found (0 results)
Does the content exist? If so, there are wording, metadata, or spidering problems
If not, why not?
www.behaviortracking.com

Content Development: Are we featuring the right stuff?
Clickthrough tracking helps determine which results should rise to the top (example: SLI Systems)
Also suggests which “best bets” to develop to address common queries

Organizational Impact: Educational opportunities
SA is a way to “reverse engineer” how your site performs in order to:
Sensitize organization to analytics, specifically related to findability
Sensitize content owners/authors to benefits of good practices around content titling, tagging, and navigational placement

Organizational Impact: Rethinking how you do things
Financial Times learns about breaking stories from their logs by monitoring spikes in company names and individuals’ names and comparing with their current coverage
Discrepancy = possible breaking story; reporter is assigned to follow up
Next step? Assign reporters to “beats” that emerge from SA

SA as User Research Method: Sleeper, but no panacea
Benefits
Non-intrusive
Inexpensive and (usually) accessible
Large volume of “real” data
Represents actual usage patterns
Drawbacks
Provides an incomplete picture of usage: was user satisfied at session’s end?
Difficult to analyze: where are the commercial tools?
Ultimately an excellent complement to qualitative methods (e.g., task analysis, field studies)

SA Headaches: What gets in the way?
Lack of time
Few useful tools for parsing logs, generating reports
Tension between those who want to perform SA and those who “own” the data (chiefly IT)
Ignorance of the method
Hard work and/or boredom of doing analysis
From summer 2006 survey (134 responses), available at book site.

Please Share Your SA Knowledge: Visit our “book in progress” site
Search Analytics for Your Site: Conversations with your Customers by Louis Rosenfeld and Richard Wiggins (Rosenfeld Media, 2007)
Site URL: www.rosenfeldmedia.com/books/searchanalytics/
Feed URL: feeds.rosenfeldmedia.com/searchanalytics/
Site contains:
Reading list
Survey results
Perl script for parsing logs
Log samples
Report templates
… and more

Contact Information
Rich Wiggins
[email_address]
Louis Rosenfeld
[email_address]
http://rosenfeldmedia.com/books/searchanalytics

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Using Search Analytics to Diagnose What’s Ailing your Information Architecture

by Louis Rosenfeld

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