Search Analytics: Powerful diagnostics for your site

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    Search Analytics: Powerful diagnostics for your site - Presentation Transcript

    1. Search Analytics: Powerful diagnostics for your site Search Solutions Jumpstart Conference Call Series November 3, 2006 Louis Rosenfeld www.louisrosenfeld.com www.rosenfeldmedia.com/books/searchanalytics
    2. About Me
      • Information architecture (IA) consultant; formerly president Argus Associates
      • Publisher and founder, Rosenfeld Media (www.rosenfeldmedia.com)
      • Background in librarianship/information science; consult for Fortune 500s
      • Co-author, Information Architecture for the World Wide Web (3rd edition out this fall)
      • Co-founder, Information Architecture Institute (www.iainstitute.org) and UXnet (www.uxnet.org)
    3. 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 here: http://www.rosenfeldmedia.com/books/searchanalytics/blog/log_sample_google_appliance/
    4. The Head, the Long Tail, and the Interesting Stuff in Between Sorting queries by frequency results in a Zipf Distribution Can we improve performance for the most popular queries?
    5. 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
    6. 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
    7. 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
    8. 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” develop- ment
    9. 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…
    10. 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)
    11. 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
    12. 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
    13. Navigation: Any improvements?
      • Michigan State University builds A-Z index automatically based on frequent queries
    14. 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
    15. 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)
    16. 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
    17. 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
    18. 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
    19. 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
    20. 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
    21. 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
    22. The Ideal SLA report 1/2 (Avi Rappoport, SearchTools.com)
      • # searches for each week/month/quarter/year
      • Top 1% of queries (cluster by stem if possible)
      • Top 10% of no-matches queries
      • Top 10% of low-matches queries? (one to 4 hits, or more depending on site size)
      • # empty searches
      • Changes in these over the last week/month/quarter/year
      • Changes’ correlation to changes in the site, search engine, company profile
    23. The Ideal SLA report 2/2 (Avi Rappoport , SearchTools.com)
      • Queries showing significant increases
      • Patterns in less-frequent queries -- names? places? web site addresses?
      • Top pages retrieved in search results and the queries that retrieved them
      • Queries that retrieved the best/most important pages
      • For search zones, create reports for each zone (will have significant impact on no-matches data)
    24. 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)
    25. 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) www.rosenfeldmedia.com/books/searchanalytics/blog/search_analytics_survey_result/
    26. 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
      • … and more
    27. Contact Information
      • Louis Rosenfeld LLC
      • 902 Miller Avenue
      • Ann Arbor, Michigan 48103 USA
      • [email_address]
      • www.louisrosenfeld.com
      • +1.734.302.3323 voice
      • +1.734.661.1655 fax

    Louis RosenfeldLouis Rosenfeld, 3 years ago

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