Search Analytics for Fun and Profit
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Search Analytics for Fun and Profit



Lou Rosenfeld's presentation on local site search analytics; An Event Apart Chicago, August 27, 2007.

Lou Rosenfeld's presentation on local site search analytics; An Event Apart Chicago, August 27, 2007.



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Search Analytics for Fun and Profit Search Analytics for Fun and Profit Presentation Transcript

  • Search Analytics for Fun and Profit An Event Apart Chicago, Illinois August 27, 2007 Lou Rosenfeld
  • Who I Am
    • Information architecture consultant to Fortune 500s
    • Publisher and founder, Rosenfeld Media
    • Blog at
    • 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):
  • 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
    • 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)
  • 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 (
  • 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:
    • Feed URL:
  • Contact Information
    • Louis Rosenfeld
    • Rosenfeld Media, LLC
    • 705 Carroll Street, #2L
    • Brooklyn, NY 11215 USA
    • +1.718.306.9396
    • [email_address]