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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
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
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
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%
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
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)
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 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
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
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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