Search Analytics for Content Strategists

17,083 views
15,706 views

Published on

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

Published in: Design, Technology
6 Comments
49 Likes
Statistics
Notes
No Downloads
Views
Total views
17,083
On SlideShare
0
From Embeds
0
Number of Embeds
394
Actions
Shares
0
Downloads
374
Comments
6
Likes
49
Embeds 0
No embeds

No notes for slide
  • \n
  • \n
  • 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
  • \n
  • \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
  • Amazing drawing by Eva-Lotta Lamm: www.evalotta.net\n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \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
  • 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
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • More great illustrations by Eva-Lotta Lamm\n
  • \n
  • \n
  • Search Analytics for Content Strategists

    1. Search Analytics forContent StrategistsLouis Rosenfeld • Rosenfeld Medialou@louisrosenfeld.com • @louisrosenfeldNYC 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 dataCritical 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 0.02XXX.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 dataCritical elements in bold: IP address, time/date stamp, query, and # of results: What are usersXXX.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 0.02XXX.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 dataCritical elements in bold: IP address, time/date stamp, query, and # of results: What are usersXXX.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 0.02XXX.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 waymeet the needs of your most important audiences
    11. A handful of queries/tasks/ways to navigate/features/ documents A little goes a long waymeet 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 waymeet the needs of your most important audiences
    13. A handful of queries/tasks/ways to navigate/features/ documents A little goes a long waymeet 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 waymeet the needs of your most important audiences
    15. A handful of queries/tasks/ways to navigate/features/ documents A little goes a long waymeet 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. #1Determine 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 areusers seeking?”
    32. Logical content types out ofsite 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. #2Develop contextual navigation
    34. 1.Choose acontent type (e.g.,events) 
2.Ask: “Whereshould users gofrom here?” 
3.Analyze thefrequent queriesfrom this contenttype 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 listingsalbum reviews discography artist bios
    38. Content models also improvesearch performance
    39. Content models also improvesearch performance
    40. Content models also improvesearch performance Content objects related to products
    41. Content models also improvesearch performance Content objects related to products Raw, crappy search results
    42. (Pssst. User studies are anotherway to get at content models)
    43. #3Detect failed content
    44. Unexpected searchingmay 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 andAIGA meandifferentthings by“ProfessionalResources”?
    46. Comparing what users findand what they want
    47. Comparing what users findand what they want
    48. #4Reduce jargon
    49. Saving the brand by killing jargonat a community collegeJargon related to online education: FlexEd, COD, College on DemandMarketing’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 queriesResult: content relabeled, money saved
    50. #5Learn 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 dataLearn 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 programDetermine what’s commonly important to all audiences (these queries better work well)
    58. #6Develop 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 toInterest in the study! football team: going... ...going... gone
    63. BeforeTax Day
    64. AfterTax Day
    65. #7Predict the future
    66. Shaping theFinancial Times’ editorial agendaFT compares these • Spiking queries for proper nouns (i.e., people and companies) • Recent editorial coverage of people and companiesDiscrepancy? • Breaking story?! • Let the editors know!
    67. Again: 7 ways SSA helps you guys1.Determine logical content types2.Develop contextual navigation3.Detect failed content4.Reduce jargon5.Learn how audiences differ6.Develop a publishing schedule7.Predict the future
    68. Some things you can do right away
    69. Some things you can do right away1.Set up SSA in Google Analytics
    70. Some things you can do right away1.Set up SSA in Google Analytics2.Query your queries
    71. Some things you can do right away1.Set up SSA in Google Analytics2.Query your queries3.Start developing a site report card
    72. Turn on SSA in Google AnalyticsSet up GA for your site if you haven’t alreadyThen 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 byquerying your queriesStarter 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 workon a site report card
    75. Use SSA to start work SSA helps determine commonon 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 helloLouis Rosenfeldlou@louisrosenfeld.comwww.louisrosenfeld.comwww.rosenfeldmedia.com@louisrosenfeld@rosenfeldmedia

    ×