Search Analytics for Fun and Profit

Search Analytics for Fun and Profit An Event Apart Chicago, Illinois August 27, 2007 Lou Rosenfeld www.rosenfeldmedia.com

Who I Am
Information architecture consultant to Fortune 500s
Publisher and founder, Rosenfeld Media
Blog at www.louisrosenfeld.com
Co-author, Information Architecture for the World Wide Web (3rd ed., 2006; O’Reilly)
New book: Search Analytics for Your Site: Conversations with your customers (2008; Rosenfeld Media): www.rosenfeldmedia.com/books/searchanalytics

Anatomy of a Search Log (from Google Search Appliance)
Critical elements in pink : 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

The Zipf Curve: Short Head, Middle Torso, 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

What’s the Sweet Spot? 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 Cumul. % Rank

Topical Patterns and Seasonal Changes

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, Visual Sciences, Ultraseek, Google Appliance, etc.)

Querying your Queries: Getting started
What are the most frequent unique queries?
Are frequent queries retrieving quality results?
Click-through rates per frequent query?
Most frequently clicked result per query?
Which frequent queries retrieve zero results?
What are the referrer pages for frequent queries?
Which queries retrieve popular documents?
What interesting patterns emerge in general?

Tune your Questions: From generic to specific
Netflix asks
Which movies most frequently searched?
Which of them most frequently clicked through?
Which of them least frequently added to queue?

Diagnose This: Fixing and improving the UX
User Research
Content Development
Interface Design: search entry interface, search results
Retrieval Algorithm Modification
Navigation Design
Metadata Development

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

User Research: …what else do they want?… BBC provides reports to determine other terms searched within same session (tracked by cookies)

User Research: …who wants it?…
Specific segments needs as determined by:
Security clearance
IP address
Job function
Account information
Alternatively, you may be able to extrapolate segments directly from SA
Pages they initiate searches from

User Research: …who wants it?… BBC’s top queries report from children’s section of site

User Research: …and when do they want it?
Time-based variation (and clustered queries) from MSU
By hour, by day, by season
Helps determine “best bets” development
Also can help tune main page and other editorial content

Content Development: Do we have the right content? From www.behaviortracking.com
Analyze 0 result queries
Does the content exist?
If so, there are titling, wording, metadata, or indexing problems
If not, why not?

Content Development: Are we featuring the right stuff? Track clickthroughs to determine which results should rise to the top (example: SLI Systems) Also suggests which “best bets” to develop to address common queries BBC removes navigation pages from search results

Search Entry Interface Design: “The Box” or something else?
Identify “dead end” points (e.g., 0 hits, 2000 hits) where assistance could be added
Query syntax helps you select search features to expose (e.g., use of Boolean operators)
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?
Add functionality: Financial Times added spell checking
Retrieval algorithm modifications
Financial Times weights company names higher
Netflix determines better weighting for unique terms and phrases
Deloitte, Barnes & Noble, Vanguard demonstrate that basic improvements (e.g., Best Bets) are insufficient (and justify increased $$$)

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
What do they search? (e.g., acronyms, jargon)
Are there other issues that would cause a “dead end”? (e.g., tagging and titling problems)
Are there user studies that could test/validate problems on these pages? (e.g., “Where did you want to go next?)

Metadata Development: How do searchers express their needs?
Tone and jargon (e.g., “cancer” vs. “oncology,” “lorry” vs. “truck,” acronyms)
Syntax (e.g., Boolean, natural language, keyword)
Length (e.g., number of terms/query; Long Tail queries longer and more complex than Short Head)
Everything we know from analyzing folksonomic tags applies here, and vice versa

Metadata Development: Which values and attributes?
Uncover hierarchy and identify
Metadata values (e.g., mobile vs. cell)
Metadata attributes (e.g., genre, region)
Content types (e.g., spec, price sheet)
SA combines with AI tools for clustering, enabling concept searching and thesaurus development

Metadata Development: Leveraging differences in the curve
Variations in information needs emerge between Short Head and Long Tail
Example: Deloitte intranet’s “known-item” queries are common; research topics are infrequent
known-item queries research queries

Organizational Impact: Educational opportunities
“ Reverse engineer” performance problems
Vanguard
Tests “best” results for common queries
Determines why these results aren’t retrieved or clicked-through
Demonstrates problem and solutions to content owners/authors benefits
Sandia Labs does same, only with top results that are losing rank in search results pages

Organizational Impact: Reexamining assumptions
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?
Complements qualitative methods (e.g., persona development, task analysis, field studies)

SA Headaches: What gets in the way?
Problems*
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
Most of these are going away…
* 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, 2008)
Site URL: www.rosenfeldmedia.com/books/searchanalytics/
Feed URL: feeds.rosenfeldmedia.com/searchanalytics/

Contact Information
Louis Rosenfeld
Rosenfeld Media, LLC
705 Carroll Street, #2L
Brooklyn, NY 11215 USA
+1.718.306.9396
[email_address]
www.louisrosenfeld.com
www.rosenfeldmedia.com

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Search Analytics for Fun and Profit

by Louis Rosenfeld on Sep 07, 2007

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Search Analytics for Fun and Profit — Presentation Transcript