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Site search analytics workshop presentation

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Workshop presented at Webdagene 2013 (http://webdagene.no/en/) September 9, 2013; UX Lisbon (http://www.ux-lx.com), May 12, 2011; UX Hong Kong (http://www.uxhongkong.com/), February 17, 2011.

Published in: Design, Technology, Business

Site search analytics workshop presentation

  1. 1. Workshop: Search Analytics forYour Site Louis Rosenfeld lou@louisrosenfeld.com • @louisrosenfeld Webdagene • 9 September 2013
  2. 2. Hello, my name is Lou www.louisrosenfeld.com | www.rosenfeldmedia.com
  3. 3. Agenda 1.The basics of Site Search Analytics (SSA) 2.Exercise 1 (pattern analysis) 3.Things you can do with SSA 4.Exercise 2 (longitudinal analysis 5.More things you can do with SSA 6.A case study 7.More on metrics 8.Things you can do today 9.Discussion
  4. 4. Let’s look at the data
  5. 5. No, let’s look at the real data Critical elements in bold: IP address, time/date stamp, query, and # of results: XXX.XXX.X.104 - - [10/Jul/2011:10:25:46 -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/2011:10:25:48 -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. 6. No, let’s look at the real data Critical elements in bold: IP address, time/date stamp, query, and # of results: XXX.XXX.X.104 - - [10/Jul/2011:10:25:46 -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/2011:10:25:48 -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 What are users searching?
  7. 7. No, let’s look at the real data Critical elements in bold: IP address, time/date stamp, query, and # of results: XXX.XXX.X.104 - - [10/Jul/2011:10:25:46 -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/2011:10:25:48 -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 What are users searching? How often are users failing?
  8. 8. SSA is semantically rich data, and...
  9. 9. SSA is semantically rich data, and... Queries sorted by frequency
  10. 10. ...what users want--in their own words
  11. 11. A little goes a long wayA handful of queries/tasks/ways to navigate/features/ documents meet the needs of your most important audiences
  12. 12. A little goes a long wayA handful of queries/tasks/ways to navigate/features/ documents meet the needs of your most important audiences Not all queries are distributed equally
  13. 13. A little goes a long wayA handful of queries/tasks/ways to navigate/features/ documents meet the needs of your most important audiences
  14. 14. A little goes a long wayA handful of queries/tasks/ways to navigate/features/ documents meet the needs of your most important audiences Nor do they diminish gradually
  15. 15. A little goes a long wayA handful of queries/tasks/ways to navigate/features/ documents meet the needs of your most important audiences
  16. 16. A little goes a long wayA handful of queries/tasks/ways to navigate/features/ documents meet the needs of your most important audiences 80/20 rule isn’t quite accurate
  17. 17. (and the tail is quite long)
  18. 18. (and the tail is quite long)
  19. 19. (and the tail is quite long)
  20. 20. (and the tail is quite long)
  21. 21. (and the tail is quite long)
  22. 22. (and the tail is quite long) The Long Tail is much longer than you’d suspect
  23. 23. The Zipf Distribution, textually
  24. 24. Insert Long Tail here
  25. 25. Agenda 1.The basics of Site Search Analytics (SSA) 2.Exercise 1 (pattern analysis) 3.Things you can do with SSA 4.Exercise 2 (longitudinal analysis 5.More things you can do with SSA 6.A case study 7.More on metrics 8.Things you can do today 9.Discussion
  26. 26. Exercise 1 (pattern analysis) Work in pairs • Each pair should have a laptop with Microsoft Excel • Laptop platform (Mac, PC) doesn’t matter Download data files: 2005-October.xls Refer to exercise sheet No right answers Have fun!
  27. 27. Agenda 1.The basics of Site Search Analytics (SSA) 2.Exercise 1 (pattern analysis) 3.Things you can do with SSA 4.Exercise 2 (longitudinal analysis 5.More things you can do with SSA 6.A case study 7.More on metrics 8.Things you can do today 9.Discussion
  28. 28. Tune site-wide navigation
  29. 29. Nailing the basics in top-down navigation
  30. 30. Nailing the basics in top-down navigation
  31. 31. Tune contextual navigation
  32. 32. 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
  33. 33. Tease out common content types
  34. 34. Tease out common content types
  35. 35. Tease out common content types Took an hour to... • Analyze top 50 queries (20% of all search activity) • Ask and iterate: “what kind of content would users be looking for when they searched these terms?” • 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%
  36. 36. Clear content types lead to better contextual navigation artist descriptions album reviews album pages artist biosdiscography TV listings
  37. 37. Make search smarter
  38. 38. Clear content types improve search performance
  39. 39. Clear content types improve search performance
  40. 40. Clear content types improve search performance Content objects related to products
  41. 41. Clear content types improve search performance Content objects related to products Raw search results
  42. 42. Enabling filtering/faceted search
  43. 43. Contextualizing “advanced” features
  44. 44. Session data suggest progression and context
  45. 45. Session data suggest progression and context search session patterns 1. solar energy 2. how solar energy works
  46. 46. Session data suggest progression and context search session patterns 1. solar energy 2. how solar energy works search session patterns 1. solar energy 2. energy
  47. 47. Session data suggest progression and context search session patterns 1. solar energy 2. how solar energy works search session patterns 1. solar energy 2. energy search session patterns 1. solar energy 2. solar energy charts
  48. 48. Session data suggest progression and context search session patterns 1. solar energy 2. how solar energy works search session patterns 1. solar energy 2. energy search session patterns 1. solar energy 2. solar energy charts search session patterns 1. solar energy 2. explain solar energy
  49. 49. Session data suggest progression and context search session patterns 1. solar energy 2. how solar energy works search session patterns 1. solar energy 2. energy search session patterns 1. solar energy 2. solar energy charts search session patterns 1. solar energy 2. explain solar energy search session patterns 1. solar energy 2. solar energy news
  50. 50. Recognizing proper nouns, dates, and unique ID#s
  51. 51. ©2010 Louis Rosenfeld, LLC (www.louisrosenfeld.com). All rights reserved. Identifying a need for a glossary 27
  52. 52. Smarter best bets
  53. 53. ©2010 Louis Rosenfeld, LLC (www.louisrosenfeld.com). All rights reserved. 29 Best bets without guessing
  54. 54. Frequent keywords “recycled” best bets
  55. 55. Learn how audiences differ
  56. 56. Who cares about what? (AIGA.org)
  57. 57. Who cares about what? (AIGA.org)
  58. 58. Who cares about what? (Open U)
  59. 59. Who cares about what? (Open U)
  60. 60. Who cares about what? (Open U)
  61. 61. Who cares about what? (Open U)
  62. 62. Why analyze queries by audience? Fortify your personas with data Learn about differences 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)
  63. 63. Reduce jargon
  64. 64. Save the brand by killing jargon Jargon related to online education: FlexEd, COD, College on Demand Marketing’s solution: expensive campaign to educate public (via posters, brochures) Result: content relabeled, money saved query rank query #22 online* #101 COD #259 College on Demand #389 FlexTrack *“online”part of 213 queries
  65. 65. Agenda 1.The basics of Site Search Analytics (SSA) 2.Exercise 1 (pattern analysis) 3.Things you can do with SSA 4.Exercise 2 (longitudinal analysis 5.More things you can do with SSA 6.A case study 7.More on metrics 8.Things you can do today 9.Discussion
  66. 66. Exercise 2 (longitudinal analysis) Work in pairs • Each pair should have a laptop with Microsoft Excel • Laptop platform (Mac, PC) doesn’t matter Download data files: 2006-February.xls + 2006-June.xls Refer to exercise sheet No right answers Have fun!
  67. 67. Agenda 1.The basics of Site Search Analytics (SSA) 2.Exercise 1 (pattern analysis) 3.Things you can do with SSA 4.Exercise 2 (longitudinal analysis 5.More things you can do with SSA 6.A case study 7.More on metrics 8.Things you can do today 9.Discussion
  68. 68. Know when to publish what
  69. 69. Interest in the football team: going...
  70. 70. Interest in the football team: going... ...going...
  71. 71. Interest in the football team: going... ...going... gone
  72. 72. Interest in the football team: going... ...going... gone Time to study!
  73. 73. Before Tax Day
  74. 74. After Tax Day
  75. 75. Identify trends
  76. 76. Learn from failure
  77. 77. Failed navigation? Examining unexpected searching Look for places searches happen beyond main page What’s going on? • Navigational failure? • Content failure? • Something else?
  78. 78. Where navigation is failing (“Professional Resources” page) Do users and AIGA mean different things by “Professional Resources”?
  79. 79. Comparing what users find and what they want
  80. 80. Comparing what users find and what they want
  81. 81. Failed business goals? Developing custom metrics Netflix asks 1. Which movies most frequently searched? (query count) 2. Which of them most frequently clicked through? (MDP views) 3. Which of them least frequently added to queue? (queue adds)
  82. 82. Failed business goals? Developing custom metrics Netflix asks 1. Which movies most frequently searched? (query count) 2. Which of them most frequently clicked through? (MDP views) 3. Which of them least frequently added to queue? (queue adds)
  83. 83. Failed business goals? Developing custom metrics Netflix asks 1. Which movies most frequently searched? (query count) 2. Which of them most frequently clicked through? (MDP views) 3. Which of them least frequently added to queue? (queue adds)
  84. 84. Learn from search sessions
  85. 85. Sample search session (Teach for America intranet)
  86. 86. Session analysis These queries co-occur within sessions: why? 
 
 

  87. 87. TFAnet session analysis results • Searches for “delta ICEG” perform poorly (way below the fold) • Users then try an (incorrect) alternative (“delta learning team”) 54
  88. 88. Identify content gaps
  89. 89. 0 results report (from behaviortracking.com) Are we missing something? Are we missing a type of something?
  90. 90. Identifying gaps helps force an issue
  91. 91. Identify failed content
  92. 92. 
 
 
 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
  93. 93. 
 
 
 
 
 
 Analyze frequent queries generated from each content sample
  94. 94. Make content owners into stakeholders
  95. 95. Sandia National Labs • Regularly record which documents came up at position #1 for 50 most frequent queries • If and when that top document falls out of position #1, document's owner is alerted • Result: healthy dialogue (often about following policies and procedures and their value)
  96. 96. Connecting pages (and their owners) that are found through search...
  97. 97. ...with how those pages were found
  98. 98. Predict the future
  99. 99. Shaping the FinancialTimes’ 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!
  100. 100. Agenda 1.The basics of Site Search Analytics (SSA) 2.Exercise 1 (pattern analysis) 3.Things you can do with SSA 4.Exercise 2 (longitudinal analysis 5.More things you can do with SSA 6.A case study 7.More on metrics 8.Things you can do today 9.Discussion
  101. 101. Avoiding a disaster atVanguard Vanguard used SSA to help benchmark existing search engine’s performance and help select new engine New search engine “performed” poorly But IT needed convincing to delay launch Information Architect & Dev Team Meeting Search seems to have a few problems… Nah . Where’s the proof? You can’t tell for sure.
  102. 102. What to do? Test performance of most frequent queries Measure using original two sets of metrics 1.relevance: how reliably the search engine returns the best matches first 2.precision: proportion of relevant and irrelevant results clustered at the top of the list
  103. 103. Relevance: 5 metrics (queries tested have “best” result) Mean: Average distance from the top Median: Less sensitive to outliers, but not useful once at least half are ranked #1 Count - Below 1st: How often is the best target something other than 1st? Count – Below 5th: How often is the best target outside the critical area? Count – Below 10th: How often is the best target beyond the first page?
  104. 104. Relevance: 5 metrics (queries tested have “best” result) Mean: Average distance from the top Median: Less sensitive to outliers, but not useful once at least half are ranked #1 Count - Below 1st: How often is the best target something other than 1st? Count – Below 5th: How often is the best target outside the critical area? Count – Below 10th: How often is the best target beyond the first page? OK!
  105. 105. Relevance: 5 metrics (queries tested have “best” result) Mean: Average distance from the top Median: Less sensitive to outliers, but not useful once at least half are ranked #1 Count - Below 1st: How often is the best target something other than 1st? Count – Below 5th: How often is the best target outside the critical area? Count – Below 10th: How often is the best target beyond the first page? OK! Hmmm...
  106. 106. Relevance: 5 metrics (queries tested have “best” result) Mean: Average distance from the top Median: Less sensitive to outliers, but not useful once at least half are ranked #1 Count - Below 1st: How often is the best target something other than 1st? Count – Below 5th: How often is the best target outside the critical area? Count – Below 10th: How often is the best target beyond the first page? OK! Hmmm... Uh oh
  107. 107. Precision: rating scale Evaluate frequent queries’ top search results on this scale • r / Relevant: Based on the information the user provided, the page's ranking is completely relevant • n / Near: The page is not a perfect match, but it’s clearly reasonable for it to be ranked highly • m / Misplaced: You can see why the search engine returned it, but it should not be ranked highly • i / Irrelevant: The result has no apparent relationship to the user’s search
  108. 108. Precision: three metrics Metrics based on degrees of permissiveness 1. strict: only counts completely relevant results 2. loose: counts relevant and near results 3. permissive: counts relevant, near, and misplaced results
  109. 109. Putting it all together: old engine (target) and new Note: low relevance and high precision scores are optimal More on Vanguard case study: http://bit.ly/D3B8c
  110. 110. Agenda 1.The basics of Site Search Analytics (SSA) 2.Exercise 1 (pattern analysis) 3.Things you can do with SSA 4.Exercise 2 (longitudinal analysis 5.More things you can do with SSA 6.A case study 7.More on metrics 8.Things you can do today 9.Discussion
  111. 111. Mapping KPI and metrics: A generic “search success” KPI
  112. 112. Search Metrics: general examples (Lee Romero, blog.leeromero.org) • Total searches for a given time period • Total distinct search terms for a given time period • Total distinct words for a given time period • Average words per search • Top searches for a given time period • Top Searches over time • Not found searches • Error searches • Ratio of searches performed each reporting period to the number of visits for that same time period
  113. 113. Search Metrics: search engine tuning (Jeannine Bartlett, earley.com) Do users not find what they want because the search engine and its ranking and relevance algorithms have not been adequately tuned? Example Benchmarks and Metrics • # of valid queries returning no results / total unique queries • Relative % search results per data source • Relative % click throughs per data source • Pass/fail % for queries using stemming • Pass/fail % for queries with misspellings • Precision measures of“seed”documents sent through the tagging process
  114. 114. Search Metrics: query entry (Jeannine Bartlett, earley.com) Do users not find what they want because the UI for expressing search terms is inadequate or unintuitive? Example Benchmarks and Metrics • % queries in the bottom set of the Zipf Curve (flat vs. hockey-stick distribution) • % queries with no click throughs • % queries using syntactic metadata filtering (date, author, source, document type, geography, etc.) • % queries using Boolean search grammar • % queries using type-ahead against taxonomy terms and synonyms • % queries using faceted semantic refinement • % pages from which search is available
  115. 115. Search Metrics: result sets (Jeannine Bartlett, earley.com) Do users not find what they want because the UI for visualizing result sets is inadequate or unintuitive? Example Benchmarks and Metrics • % queries utilizing multiple results views • % queries with drill-down through clusters • % queries using iterative syntactic metadata filtering (date range, sorting, type or source inclusion/exclusion, etc.) • % queries suggesting broader/narrower terms • % queries suggesting“Best Bets”or“See Also” • % queries using iterative semantic term filtering, inclusion or exclusion
  116. 116. Agenda 1.The basics of Site Search Analytics (SSA) 2.Exercise 1 (pattern analysis) 3.Things you can do with SSA 4.Exercise 2 (longitudinal analysis 5.More things you can do with SSA 6.A case study 7.More on metrics 8.Things you can do today 9.Discussion
  117. 117. Things to do today 1.Set up SSA in Google Analytics 2.Query your queries 3.Start developing a site report card 4.Start incorporating SSA into your user research program
  118. 118. 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
  119. 119. 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?
  120. 120. Use SSA to start work on a site report card
  121. 121. Use SSA to start work on a site report card SSA helps determine common information needs
  122. 122. ©2010 Louis Rosenfeld, LLC (www.louisrosenfeld.com). All rights reserved. From Christian Rohrer, xdstrategy.com
  123. 123. ©2010 Louis Rosenfeld, LLC (www.louisrosenfeld.com). All rights reserved. Augment personas and audience profiles with frequent queries Persona example (from Adaptive Path) Frequent queries added (in green)
  124. 124. Agenda 1.The basics of Site Search Analytics (SSA) 2.Exercise 1 (pattern analysis) 3.Things you can do with SSA 4.Exercise 2 (longitudinal analysis 5.More things you can do with SSA 6.A case study 7.More on metrics 8.Things you can do today 9.Discussion
  125. 125. Comparing referral queries with local queries
  126. 126. Long tail queries: Longer, more complex (fromVanguard) Short head: common queries Long tail: common queries Beneficiary form 401(k) beneficiary career forms amt money market location loans calculator 403(b)(7) account asset transfer authorization automatic investing Wire transfer instructions adoption agreement international wire transfers socially responsible investing Vanguard tax identification number IRA Asset Transfer form fdic insured account early withdrawal penalties
  127. 127. Now on sale Search Analytics forYour Site: Conversations with Your Customers by Louis Rosenfeld (Rosenfeld Media, 2011) www.rosenfeldmedia.com Use code WEBDAGENE2013 for 20% off all Rosenfeld Media books
  128. 128. Louis Rosenfeld lou@louisrosenfeld.com www.louisrosenfeld.com www.rosenfeldmedia.com @louisrosenfeld @rosenfeldmedia This presentation available on SlideShare: http://slidesha.re/otzE2t Say hello

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