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Search Analytics for
Content Strategists


Louis Rosenfeld • Rosenfeld Media
lou@louisrosenfeld.com • @louisrosenfeld
NYC Content Strategy Meetup • September 27, 2012
Hello, my name is Lou




 www.louisrosenfeld.com | www.rosenfeldmedia.com
Let’s look at the data
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
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
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
SSA is semantically rich data, and...
SSA is semantically rich data, and...




       Queries
      sorted by
      frequency
...what users want in their own words
A handful of queries/tasks/ways to navigate/features/ documents
 A little goes a long way
meet the needs of your most important audiences
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
A handful of queries/tasks/ways to navigate/features/ documents
 A little goes a long way
meet the needs of your most important audiences
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
A handful of queries/tasks/ways to navigate/features/ documents
 A little goes a long way
meet the needs of your most important audiences
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
(and the tail is quite long)
(and the tail is quite long)
(and the tail is quite long)
(and the tail is quite long)
(and the tail is quite long)
The Zipf Curve, textually
Hey content strategists:
ever heard this one?
Hey content strategists:
ever heard this one?
        unverified rumor alert
Hey content strategists:
ever heard this one?
         unverified rumor alert

     90% of Microsoft.com content
Hey content strategists:
ever heard this one?
         unverified rumor alert

     90% of Microsoft.com content
       has never been accessed...
Hey content strategists:
ever heard this one?
         unverified rumor alert

     90% of Microsoft.com content
       has never been accessed...
             not even once
Hey content strategists:
ever heard this one?
         unverified rumor alert

     90% of Microsoft.com content
       has never been accessed...
             not even once
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
#1
Determine logical content types
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
“What types of content are
users seeking?”
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%
#2
Develop contextual navigation
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
                                              






                             





                                                          

                                 





                                     
                    




Analyze frequent queries generated from each content sample
Hello, desire lines...
Content types + contextual navigation
= content models

  album pages   artist descriptions
                                            TV listings




album reviews   discography           artist bios
Content models also improve
search performance
Content models also improve
search performance
Content models also improve
search performance
                   Content objects
                 related to products
Content models also improve
search performance
                   Content objects
                 related to products



                            Raw, crappy
                           search results
(Pssst. User studies are another
way to get at content models)
#3
Detect failed content
Unexpected searching
may indicate failed content


                              Look for
                              critical pages
                              (beyond main
                              page) that
                              generate lots of
                              search traffic
                              What’s going on?
Where navigation is failing
   (“Professional Resources” page)

Do users and
AIGA mean
different
things by
“Professional
Resources”?
Comparing what users find
and what they want
Comparing what users find
and what they want
#4
Reduce jargon
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
#5
Learn how audiences differ
Who cares about what? (AIGA.org)
Who cares about what? (AIGA.org)
Who cares about what?
Who cares about what?
Who cares about what?
Who cares about what?
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)
#6
Develop a publishing schedule
Search Analytics for Content Strategists
Interest in the
 football team:
    going...
Interest in the
 football team:
    going...


                  ...going...
Interest in the
 football team:
    going...


                  ...going...




                                gone
Time to
Interest in the                         study!
 football team:
    going...


                  ...going...




                                gone
Search Analytics for Content Strategists
Search Analytics for Content Strategists
Before
Tax Day
Search Analytics for Content Strategists
After
Tax Day
#7
Predict the future
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!
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
Some things you can do right away
Some things you can do right away

1.Set up SSA in Google Analytics
Some things you can do right away

1.Set up SSA in Google Analytics
2.Query your queries
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
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
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?
Use SSA to start work
on a site report card
Use SSA to start work        SSA helps
                        determine common
on a site report card    information needs
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
Say hello
Louis Rosenfeld
lou@louisrosenfeld.com

www.louisrosenfeld.com
www.rosenfeldmedia.com

@louisrosenfeld
@rosenfeldmedia

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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
  • 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%
  • 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
  • 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)
  • 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
  • 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
  • 51. Who cares about what? (AIGA.org)
  • 52. Who cares about what? (AIGA.org)
  • 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)
  • 60. Interest in the football team: going...
  • 61. Interest in the football team: going... ...going...
  • 62. Interest in the football team: going... ...going... gone
  • 63. Time to Interest in the study! football team: going... ...going... gone
  • 70. 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!
  • 71. 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
  • 72. Some things you can do right away
  • 73. Some things you can do right away 1.Set up SSA in Google Analytics
  • 74. Some things you can do right away 1.Set up SSA in Google Analytics 2.Query your queries
  • 75. 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
  • 76. 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
  • 77. 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?
  • 78. Use SSA to start work on a site report card
  • 79. Use SSA to start work SSA helps determine common on a site report card information needs
  • 80. 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

Hinweis der Redaktion

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  3. We get two major things out of this data: SESSIONS and FREQUENT QUERIES\n
  4. Your brain on data: what will it do?\n
  5. Your brain on data: what will it do?\n
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  8. Amazing drawing by Eva-Lotta Lamm: www.evalotta.net\n
  9. Amazing drawing by Eva-Lotta Lamm: www.evalotta.net\n
  10. Amazing drawing by Eva-Lotta Lamm: www.evalotta.net\n
  11. Amazing drawing by Eva-Lotta Lamm: www.evalotta.net\n
  12. Amazing drawing by Eva-Lotta Lamm: www.evalotta.net\n
  13. Amazing drawing by Eva-Lotta Lamm: www.evalotta.net\n
  14. Amazing drawing by Eva-Lotta Lamm: www.evalotta.net\n
  15. Amazing drawing by Eva-Lotta Lamm: www.evalotta.net\n
  16. Amazing drawing by Eva-Lotta Lamm: www.evalotta.net\n
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  44. 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
  45. 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
  46. 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
  47. 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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  65. More great illustrations by Eva-Lotta Lamm\n
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