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
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
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
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
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
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)
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
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?
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
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