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


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Presented by Lou Rosenfeld and Rich Wiggins at the 2007 ASIS&T IA Summit, Las Vegas, Nevada, USA, March 24, 2007.

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  • Louis, Rich, very nicely done. I enjoyed seeing everything put together in a single place. I am working hard on simplifying this process for clients, but it's never easy. There is a rather strong 'chasm' to cross before people realize that 'a-ha' moment where the first few improvements bear fruit. A systematic method is required because of the way so many of us have to switch gears so often, so I think in-house analytics professionals stand the best chance of successfully deploying comprehensive strategies based on these recommendations. But, still, even as a consultant I am trying to put it in a 'box' I can bring to the table... Scott of someday I may succeed!
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  • Understanding the long tail of search and the concept of best bets. I like this.
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Using Search Analytics to Diagnose What’s Ailing your Information Architecture

  1. 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
  2. Trying to Fit into the IA Summit 2007 Theme… Rich Information Rich Interaction Rich Relationships Rich Wiggins
  3. Thesis <ul><li>By analyzing search logs, you engage in a conversation with your customers </li></ul><ul><li>At best, it’s a two way conversation: </li></ul><ul><ul><li>Your users tell you what they seek </li></ul></ul><ul><ul><li>You tune your search engine (and your site) to give them what they seek the most </li></ul></ul><ul><li>If you’re not analyzing your search logs, then you aren’t listening to your customers </li></ul><ul><li>Search is too important to leave in the hands of robots </li></ul>
  4. The Wonderful Things Search Engines Do <ul><li>Help harness massive amounts of content </li></ul><ul><ul><li>Thousands, millions, billions of URLs </li></ul></ul><ul><li>Cut across barriers </li></ul><ul><ul><li>Document structure </li></ul></ul><ul><ul><li>Topical structure </li></ul></ul><ul><ul><li>Institutional structure, silos </li></ul></ul>
  5. The Horrible Things that Search Engines Do <ul><li>Confuse low-value content with vital content </li></ul><ul><ul><li>And point to obsolete content </li></ul></ul><ul><ul><li>And draft, internal, duplicative content </li></ul></ul><ul><li>Rank leaf pages ahead of starting points </li></ul><ul><li>Rank popular or personal pages ahead of official content </li></ul>
  6. MSU Keywords: Accidental Thesaurus <ul><li>Circa 1999 MSU’s local AltaVista stopped scaling </li></ul><ul><li>Search for “human resources” and you get resume for a student in the HR program </li></ul><ul><li>We had to do something </li></ul><ul><li>We asked AltaVista for a way to goose the real HR site to the top of the hit list </li></ul><ul><li>They didn’t deliver </li></ul><ul><li>So we rolled our own Best Bets service, called it MSU Keywords </li></ul><ul><li>And it worked! </li></ul>
  7. Methodology <ul><li>Study the most popular unique searches </li></ul><ul><li>Map each to appropriate URL </li></ul><ul><ul><li>“human resources” -> </li></ul></ul><ul><ul><li>“campus map” -> </li></ul></ul><ul><li>Watch the results: </li></ul><ul><li>User complaints go down </li></ul><ul><li>So do content provider complaints </li></ul><ul><li>Continue to watch, learn, and act </li></ul>
  8. Google Has Trained ’Em to Search First <ul><li>Top 10 searches, , Jan 2007 </li></ul><ul><li>“ map” is a top search even with a map logo on the home page </li></ul><ul><li>MSU Usability Center, testing 2006 redesign, ordered testers to stay away from the search box </li></ul><ul><li>Nielsen 50% theory may underestimate </li></ul>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
  9. The Zipf Curve: Short Head, Torso, and Long Tail
  10. Keep It In Proportion <ul><li>7218 campus map </li></ul><ul><li>5859 map </li></ul><ul><li>5184 im west </li></ul><ul><li>4320 library </li></ul><ul><li>3745 study abroad </li></ul><ul><li>3690 schedule of courses </li></ul><ul><li>3584 bookstore </li></ul><ul><li>3575 spartantrak </li></ul><ul><li>3229 angel </li></ul><ul><li>3204 cata </li></ul>
  11. 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
  12. Look for Topical Patterns and Seasonal Changes
  13. Does Best Bets Apply to Everyone? <ul><li>Walter Underwood, former chief architect of Ultraseek: </li></ul><ul><li>Instead of Best Bets, Get a Better Search Engine </li></ul><ul><li>Best Bets requires human labor </li></ul><ul><ul><li>Commitment of time and attention </li></ul></ul><ul><ul><li>… so do good search engine implementations </li></ul></ul>
  14. We Didn’t Start the Fire; credit to: <ul><li>Vilfredo Pareto, circa 1890 – “the law of the vital few” (simplified as “80-20 rule”) </li></ul><ul><li>George Kingsley Zipf, Harvard, circa 1932 – counting the words used in Joyce’s Ulysses </li></ul><ul><ul><li>“ the” is more common than “no” or “Dublin” </li></ul></ul><ul><li>Bradford’s Law of Scattering, circa 1934 – a small number of journals accounts for a large percent of all important papers </li></ul><ul><ul><li>Cited, most importantly, by the pricing model of Elsevier for leading scientific journals </li></ul></ul><ul><li>Many other best bet pioneers: Microsoft, Raytheon, BBC, ESPN, AOL </li></ul>
  15. Where will you Capture Search Queries <ul><li>The search logs that your search engine naturally captures and maintains as searches take place </li></ul><ul><li>Search keywords or phrases that your users execute, that you capture into your own local database </li></ul><ul><li>Search keywords or phrases that your commercial search solution captures, records, and reports on (Mondosoft, Web Side Story, Ultraseek, Google Appliance, etc.) </li></ul>
  16. Anatomy of a Search Log (from Google Search Appliance) <ul><li>Critical elements in bold: IP address , time/date stamp , query , and # of results: </li></ul><ul><li>XXX.XXX.X.104 - - [ 10/Jul/2006:10:25:46 -0800] &quot;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&quot; 200 971 0 0.02 </li></ul><ul><li>XXX.XXX.X.104 - - [ 10/Jul/2006:10:25:48 -0800] &quot;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&quot; 200 8283 146 0.16 </li></ul><ul><li>XXX.XXX.XX.130 - - [ 10/Jul/2006:10:24:38 -0800] &quot;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&quot; 200 9718 62 0.17 </li></ul>Full legend and more examples available from book site
  17. Sample Query Analysis Report Excel template available from book site
  18. Querying your Queries: Some basic questions 1/2 <ul><li>What are the most common unique queries? </li></ul><ul><li>Do any interesting patterns emerge from analyzing these common queries? </li></ul><ul><li>When common queries are searched, are the results the ones your users should be seeing? </li></ul><ul><li>Which common queries retrieve zero results? </li></ul><ul><li>Which common queries retrieve a large number of results, say 100 or more? </li></ul>
  19. Querying your Queries: Some basic questions 2/2 <ul><li>Which common queries retrieve results that don’t get clicked through? </li></ul><ul><li>What page is the top source (referrer) per common query? </li></ul><ul><li>What is the number of click-throughs per common query? </li></ul><ul><li>Which result is most frequently clicked-through per common query? </li></ul><ul><li>What’s the average query length (number of terms, number of characters)? </li></ul><ul><li>Which URLs are users searching for? </li></ul>
  20. Tune your Questions: Broad to specific <ul><li>Netflix asks: </li></ul><ul><ul><li>Which movies most frequently searched? </li></ul></ul><ul><ul><li>Which of them most frequently clicked through? </li></ul></ul><ul><ul><li>Which of them least frequently added to queue (and why)? </li></ul></ul><ul><li>Examples: </li></ul><ul><ul><li>“ OO7” versus “007” </li></ul></ul><ul><ul><li>Porn-related (not carried by Netflix) </li></ul></ul><ul><ul><li>“ yoga”: not stocking enough? or not indexing enough record content? </li></ul></ul>
  21. SA as Diagnostic Tool: What can you fix or improve? <ul><li>User Research </li></ul><ul><li>Interface Design: search entry interface, search results </li></ul><ul><li>Retrieval Algorithm Modification </li></ul><ul><li>Navigation Design </li></ul><ul><li>Metadata Development </li></ul><ul><li>Content Development </li></ul>
  22. User Research: What do they want?… <ul><li>SA is a true expression of users’ information needs (often surprising: e.g., SKU numbers at LL Bean; URLs at IBM) </li></ul><ul><li>Provides context by displaying aspects of single search sessions </li></ul>
  23. User Research: …who wants it?… <ul><li>What can you learn from knowing these things? </li></ul><ul><ul><li>What specific segments want; determined by: </li></ul></ul><ul><ul><ul><li>Security clearance </li></ul></ul></ul><ul><ul><ul><li>IP address </li></ul></ul></ul><ul><ul><ul><li>Job function </li></ul></ul></ul><ul><ul><ul><li>Account information </li></ul></ul></ul><ul><ul><li>Which pages they initiate searches from </li></ul></ul>
  24. User Research: …and when do they want it? <ul><li>Time-based variation (and clustered queries) </li></ul><ul><li>By hour, by day, by season </li></ul><ul><li>Helps determine “best bets” and “guide” develop- ment </li></ul>
  25. Search Entry Interface Design: “The Box” or something else? <ul><li>SA identifies “dead end” points (e.g., 0 hits, 2000 hits) where assistance could be added (e.g., revise search, browsing alternative) </li></ul><ul><li>Syntax of queries informs selection of search features to expose (e.g., use of Boolean operators, fielded searching) </li></ul>… OR…
  26. Search Results Interface Design: Which results where? <ul><li>#10 result is clicked through more often than #s 6, 7, 8, and 9 (ten results per page) </li></ul>From SLI Systems (
  27. Search Results Interface Design: How to sort results? <ul><li>Financial Times has found that users often include dates in their queries </li></ul><ul><li>Obvious but effective improvement: allow users to sort by date </li></ul>
  28. Search System: What to change? <ul><li>Identify new functionality: Financial Times added spell checking </li></ul><ul><li>Retrieval algorithm modifications: </li></ul><ul><ul><li>Deloitte, Barnes & Noble use SA to demonstrate that basic improvements (e.g., Best Bets) are insufficient </li></ul></ul><ul><ul><li>Financial Times weights company names higher </li></ul></ul>
  29. Navigation: Any improvements? <ul><li>Michigan State University builds A-Z index automatically based on frequent queries </li></ul>
  30. Navigation: Where does it fail? <ul><li>Track and study pages (excluding main page) where search is initiated </li></ul><ul><ul><li>Are there obvious issues that would cause a “dead end”? </li></ul></ul><ul><ul><li>Are there user studies that could test/validate problems on these pages? </li></ul></ul><ul><li>Sandia Labs analyzes most requested documents to test content independent of site structure; results used to improve structure </li></ul>
  31. Metadata Development: How do users express their needs? <ul><li>SA provides a sense of tone: how users’ needs are expressed </li></ul><ul><ul><li>Jargon (e.g., “cancer” vs. “oncology,” “lorry” vs. “truck,” acronyms) </li></ul></ul><ul><ul><li>Length (e.g., number of terms/query) </li></ul></ul><ul><ul><li>Syntax (e.g., Boolean, natural language, keyword) </li></ul></ul>
  32. Metadata Development: Which metadata values? <ul><li>SA helps in the creation of controlled vocabularies </li></ul><ul><li>Terms are fodder for metadata values (e.g., “cell phone,” “JFK” vs. “John Kennedy,” “country music”), especially for determining preferred terms </li></ul><ul><li>Works with tools that cluster synonyms (example from, enabling concept searching and thesaurus development </li></ul>
  33. Metadata Development: Which metadata attributes? <ul><li>SA helps in the creation of vocabularies </li></ul><ul><li>Simple cluster analysis can detect metadata attributes (e.g., “product,” “person,” “topic”) </li></ul><ul><li>Look for variations between short head and long tail (Deloitte intranet: “known-item” queries are common; research topics are infrequent) </li></ul>known-item queries research queries
  34. Content Development: Do we have the right content? <ul><li>SA identifies content that can’t be found (0 results) </li></ul><ul><li>Does the content exist? If so, there are wording, metadata, or spidering problems </li></ul><ul><li>If not, why not? </li></ul>
  35. Content Development: Are we featuring the right stuff? <ul><li>Clickthrough tracking helps determine which results should rise to the top (example: SLI Systems) </li></ul><ul><li>Also suggests which “best bets” to develop to address common queries </li></ul>
  36. Organizational Impact: Educational opportunities <ul><li>SA is a way to “reverse engineer” how your site performs in order to: </li></ul><ul><ul><li>Sensitize organization to analytics, specifically related to findability </li></ul></ul><ul><ul><li>Sensitize content owners/authors to benefits of good practices around content titling, tagging, and navigational placement </li></ul></ul>
  37. Organizational Impact: Rethinking how you do things <ul><li>Financial Times learns about breaking stories from their logs by monitoring spikes in company names and individuals’ names and comparing with their current coverage </li></ul><ul><li>Discrepancy = possible breaking story; reporter is assigned to follow up </li></ul><ul><li>Next step? Assign reporters to “beats” that emerge from SA </li></ul>
  38. SA as User Research Method: Sleeper, but no panacea <ul><li>Benefits </li></ul><ul><ul><li>Non-intrusive </li></ul></ul><ul><ul><li>Inexpensive and (usually) accessible </li></ul></ul><ul><ul><li>Large volume of “real” data </li></ul></ul><ul><ul><li>Represents actual usage patterns </li></ul></ul><ul><li>Drawbacks </li></ul><ul><ul><li>Provides an incomplete picture of usage: was user satisfied at session’s end? </li></ul></ul><ul><ul><li>Difficult to analyze: where are the commercial tools? </li></ul></ul><ul><li>Ultimately an excellent complement to qualitative methods (e.g., task analysis, field studies) </li></ul>
  39. SA Headaches: What gets in the way? <ul><li>Lack of time </li></ul><ul><li>Few useful tools for parsing logs, generating reports </li></ul><ul><li>Tension between those who want to perform SA and those who “own” the data (chiefly IT) </li></ul><ul><li>Ignorance of the method </li></ul><ul><li>Hard work and/or boredom of doing analysis </li></ul><ul><li>From summer 2006 survey (134 responses), available at book site. </li></ul>
  40. Please Share Your SA Knowledge: Visit our “book in progress” site <ul><li>Search Analytics for Your Site: Conversations with your Customers by Louis Rosenfeld and Richard Wiggins (Rosenfeld Media, 2007) </li></ul><ul><li>Site URL: </li></ul><ul><li>Feed URL: </li></ul><ul><li>Site contains: </li></ul><ul><li>Reading list </li></ul><ul><li>Survey results </li></ul><ul><li>Perl script for parsing logs </li></ul><ul><li>Log samples </li></ul><ul><li>Report templates </li></ul><ul><li>… and more </li></ul>
  41. Contact Information <ul><li>Rich Wiggins </li></ul><ul><li>[email_address] </li></ul><ul><li>Louis Rosenfeld </li></ul><ul><li>[email_address] </li></ul><ul><li> </li></ul>