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Search Analytics for Content Strategists

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Given at Confab 2012, Minneapolis, USA; May 16, 2012, NYC Content Strategy Meetup, September 27, 2012. Slides highly subject to change.

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Search Analytics for Content Strategists

  1. Search Analytics for Content Strategists Louis Rosenfeld • Rosenfeld Media lou@louisrosenfeld.com • @louisrosenfeld NYC Content Strategy Meetup • September 27, 2012
  2. Hello, my name is Lou www.louisrosenfeld.com | www.rosenfeldmedia.com
  3. Let’s look at the data
  4. No, let’s really look at the data 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 /searchaccess=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
  5. No, let’s really look at the data Critical elements in bold: IP address, time/date stamp, query, and # of results: What are users XXX.XXX.X.104 - - [10/Jul/2006:10:25:46 -0800] "GET /search?access=p&entqr=0 searching? &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 /searchaccess=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
  6. No, let’s really look at the data Critical elements in bold: IP address, time/date stamp, query, and # of results: What are users XXX.XXX.X.104 - - [10/Jul/2006:10:25:46 -0800] "GET /search?access=p&entqr=0 searching? &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 /searchaccess=p&entqr=0 &output=xml_no_dtd&sort=date%3AD%3AL How often are %3Ad1&ie=UTF-8&client=www& users failing? 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
  7. SSA is semantically rich data, and...
  8. SSA is semantically rich data, and... Queries sorted by frequency
  9. ...what users want in their own words
  10. A handful of queries/tasks/ways to navigate/features/ documents A little goes a long way meet the needs of your most important audiences
  11. A handful of queries/tasks/ways to navigate/features/ documents A little goes a long way meet the needs of your most important audiences Not all queries are distributed equally
  12. A handful of queries/tasks/ways to navigate/features/ documents A little goes a long way meet the needs of your most important audiences
  13. A handful of queries/tasks/ways to navigate/features/ documents A little goes a long way meet the needs of your most important audiences Nor do they diminish gradually
  14. A handful of queries/tasks/ways to navigate/features/ documents A little goes a long way meet the needs of your most important audiences
  15. A handful of queries/tasks/ways to navigate/features/ documents A little goes a long way meet the needs of your most important audiences 80/20 rule isn’t quite accurate
  16. (and the tail is quite long)
  17. (and the tail is quite long)
  18. (and the tail is quite long)
  19. (and the tail is quite long)
  20. (and the tail is quite long)
  21. The Zipf Curve, textually
  22. Hey content strategists: ever heard this one?
  23. Hey content strategists: ever heard this one? unverified rumor alert
  24. Hey content strategists: ever heard this one? unverified rumor alert 90% of Microsoft.com content
  25. Hey content strategists: ever heard this one? unverified rumor alert 90% of Microsoft.com content has never been accessed...
  26. Hey content strategists: ever heard this one? unverified rumor alert 90% of Microsoft.com content has never been accessed... not even once
  27. Hey content strategists: ever heard this one? unverified rumor alert 90% of Microsoft.com content has never been accessed... not even once
  28. 7 ways SSA helps content strategists 1.Determine logical content types 2.Develop contextual navigation 3.Detect failed content 4.Reduce jargon 5.Learn how audiences differ 6.Develop a publishing schedule 7.Predict the future
  29. #1 Determine logical content types
  30. Start with basic SSA data: queries and query frequency Percent: volume of search activity for a unique query during a particular time period Cumulative Percent: running sum of percentages
  31. “What types of content are users seeking?”
  32. Logical content types out of site search analytics Take an hour to... • Cluster and analyze top 50 queries (20% of all search activity) • Ask and iterate: “what types of content would users be looking for when searching these queries?” • Add cumulative percentages Result: prioritized list of potential content types #1) application: 11.77% #2) reference: 10.5% #3) instructions: 8.6% #4) main/navigation pages: 5.91% #5) contact info: 5.79% #6) news/announcements: 4.27%
  33. #2 Develop contextual navigation
  34. 1.Choose a content type (e.g., events) 
 2.Ask: “Where should users go from here?” 
 3.Analyze the frequent queries from this content type from aiga.org 

  35. 
 
 
 
 
 
 Analyze frequent queries generated from each content sample
  36. Hello, desire lines...
  37. Content types + contextual navigation = content models album pages artist descriptions TV listings album reviews discography artist bios
  38. Content models also improve search performance
  39. Content models also improve search performance
  40. Content models also improve search performance Content objects related to products
  41. Content models also improve search performance Content objects related to products Raw, crappy search results
  42. (Pssst. User studies are another way to get at content models)
  43. #3 Detect failed content
  44. Unexpected searching may indicate failed content Look for critical pages (beyond main page) that generate lots of search traffic What’s going on?
  45. Where navigation is failing (“Professional Resources” page) Do users and AIGA mean different things by “Professional Resources”?
  46. Comparing what users find and what they want
  47. Comparing what users find and what they want
  48. #4 Reduce jargon
  49. Saving the brand by killing jargon at a community college Jargon related to online education: FlexEd, COD, College on Demand Marketing’s solution: expensive campaign to educate public (via posters, brochures) The Numbers query rank query (from SSA): #22 online* #101 COD #259 College on Demand #389 FlexTrack * “online” part of 213 queries Result: content relabeled, money saved
  50. #5 Learn how audiences differ
  51. Who cares about what? (AIGA.org)
  52. Who cares about what? (AIGA.org)
  53. Who cares about what?
  54. Who cares about what?
  55. Who cares about what?
  56. Who cares about what?
  57. Why analyze queries by audience? Fortify your personas with data Learn about differences--including tone and voice--between audiences • Open University “Enquirers”: 16 of 25 queries are for subjects not taught at OU • Open University Students: search for course codes, topics dealing with completing program Determine what’s commonly important to all audiences (these queries better work well)
  58. #6 Develop a publishing schedule
  59. Interest in the football team: going...
  60. Interest in the football team: going... ...going...
  61. Interest in the football team: going... ...going... gone
  62. Time to Interest in the study! football team: going... ...going... gone
  63. Before Tax Day
  64. After Tax Day
  65. #7 Predict the future
  66. Shaping the Financial Times’ editorial agenda FT compares these • Spiking queries for proper nouns (i.e., people and companies) • Recent editorial coverage of people and companies Discrepancy? • Breaking story?! • Let the editors know!
  67. Again: 7 ways SSA helps you guys 1.Determine logical content types 2.Develop contextual navigation 3.Detect failed content 4.Reduce jargon 5.Learn how audiences differ 6.Develop a publishing schedule 7.Predict the future
  68. Some things you can do right away
  69. Some things you can do right away 1.Set up SSA in Google Analytics
  70. Some things you can do right away 1.Set up SSA in Google Analytics 2.Query your queries
  71. Some things you can do right away 1.Set up SSA in Google Analytics 2.Query your queries 3.Start developing a site report card
  72. Turn on SSA in Google Analytics Set up GA for your site if you haven’t already Then teach it to parse and capture your search engine’s queries (not set by default) References • http://is.gd/cR0qr • http://is.gd/cR0qP
  73. Seed your analysis by querying your queries Starter questions 1. What are the most frequent unique queries? 2. Are frequent queries retrieving quality results? 3. Click-through rates per frequent query? 4. Most frequently clicked result per query? 5. Which frequent queries retrieve zero results? 6. What are the referrer pages for frequent queries? 7. Which queries retrieve popular documents? 8. What interesting patterns emerge in general?
  74. Use SSA to start work on a site report card
  75. Use SSA to start work SSA helps determine common on a site report card information needs
  76. Read this Search Analytics for Your Site: Conversations with Your Customers by Louis Rosenfeld (Rosenfeld Media, 2011) www.rosenfeldmedia.com Use code FOLBR2020 for 20% off all Rosenfeld Media products
  77. Say hello Louis Rosenfeld lou@louisrosenfeld.com www.louisrosenfeld.com www.rosenfeldmedia.com @louisrosenfeld @rosenfeldmedia

Editor's Notes

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  • We get two major things out of this data: SESSIONS and FREQUENT QUERIES\n
  • Your brain on data: what will it do?\n
  • Your brain on data: what will it do?\n
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  • Amazing drawing by Eva-Lotta Lamm: www.evalotta.net\n
  • Amazing drawing by Eva-Lotta Lamm: www.evalotta.net\n
  • Amazing drawing by Eva-Lotta Lamm: www.evalotta.net\n
  • Amazing drawing by Eva-Lotta Lamm: www.evalotta.net\n
  • Amazing drawing by Eva-Lotta Lamm: www.evalotta.net\n
  • Amazing drawing by Eva-Lotta Lamm: www.evalotta.net\n
  • Amazing drawing by Eva-Lotta Lamm: www.evalotta.net\n
  • Amazing drawing by Eva-Lotta Lamm: www.evalotta.net\n
  • Amazing drawing by Eva-Lotta Lamm: www.evalotta.net\n
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  • Personas: http://www.uie.com/images/blog/YahooExamplePersona.gif\nTable: From Jarrett, Quesenbery, Stirling, and Allen’s report “Search Behaviour at OU;” April 6, 2007.\n
  • Personas: http://www.uie.com/images/blog/YahooExamplePersona.gif\nTable: From Jarrett, Quesenbery, Stirling, and Allen’s report “Search Behaviour at OU;” April 6, 2007.\n
  • Personas: http://www.uie.com/images/blog/YahooExamplePersona.gif\nTable: From Jarrett, Quesenbery, Stirling, and Allen’s report “Search Behaviour at OU;” April 6, 2007.\n
  • Personas: http://www.uie.com/images/blog/YahooExamplePersona.gif\nTable: From Jarrett, Quesenbery, Stirling, and Allen’s report “Search Behaviour at OU;” April 6, 2007.\n
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  • More great illustrations by Eva-Lotta Lamm\n
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