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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.

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.

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  • 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
  • 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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  • Examples\n “OO7” versus “007”\n Porn-related (not carried by Netflix)\n “yoga”: not stocking enough? Or not indexing enough record content? Some other problem?\n
  • Examples\n “OO7” versus “007”\n Porn-related (not carried by Netflix)\n “yoga”: not stocking enough? Or not indexing enough record content? Some other problem?\n
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  • More great illustrations by Eva-Lotta Lamm\n
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Site search analytics workshop presentation Site search analytics workshop presentation Presentation Transcript

  • Workshop: Search Analytics forYour Site Louis Rosenfeld lou@louisrosenfeld.com • @louisrosenfeld Webdagene • 9 September 2013
  • Hello, my name is Lou www.louisrosenfeld.com | www.rosenfeldmedia.com
  • 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
  • Let’s look at the data
  • 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
  • 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?
  • 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?
  • SSA is semantically rich data, and...
  • SSA is semantically rich data, and... Queries sorted by frequency
  • ...what users want--in their own words
  • A little goes a long wayA handful of queries/tasks/ways to navigate/features/ documents meet the needs of your most important audiences
  • 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
  • A little goes a long wayA handful of queries/tasks/ways to navigate/features/ documents meet the needs of your most important audiences
  • 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
  • A little goes a long wayA handful of queries/tasks/ways to navigate/features/ documents meet the needs of your most important audiences
  • 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
  • (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)
  • (and the tail is quite long) The Long Tail is much longer than you’d suspect
  • The Zipf Distribution, textually
  • Insert Long Tail here
  • 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
  • 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!
  • 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
  • Tune site-wide navigation
  • Nailing the basics in top-down navigation
  • Nailing the basics in top-down navigation
  • Tune contextual navigation
  • 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
  • Tease out common content types
  • Tease out common content types
  • 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%
  • Clear content types lead to better contextual navigation artist descriptions album reviews album pages artist biosdiscography TV listings
  • Make search smarter
  • Clear content types improve search performance
  • Clear content types improve search performance
  • Clear content types improve search performance Content objects related to products
  • Clear content types improve search performance Content objects related to products Raw search results
  • Enabling filtering/faceted search
  • Contextualizing “advanced” features
  • Session data suggest progression and context
  • Session data suggest progression and context search session patterns 1. solar energy 2. how solar energy works
  • 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
  • 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
  • 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
  • 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
  • Recognizing proper nouns, dates, and unique ID#s
  • ©2010 Louis Rosenfeld, LLC (www.louisrosenfeld.com). All rights reserved. Identifying a need for a glossary 27
  • Smarter best bets
  • ©2010 Louis Rosenfeld, LLC (www.louisrosenfeld.com). All rights reserved. 29 Best bets without guessing
  • Frequent keywords “recycled” best bets
  • Learn how audiences differ
  • Who cares about what? (AIGA.org)
  • Who cares about what? (AIGA.org)
  • Who cares about what? (Open U)
  • Who cares about what? (Open U)
  • Who cares about what? (Open U)
  • Who cares about what? (Open U)
  • 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)
  • Reduce jargon
  • 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
  • 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
  • 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!
  • 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
  • Know when to publish what
  • Interest in the football team: going...
  • Interest in the football team: going... ...going...
  • Interest in the football team: going... ...going... gone
  • Interest in the football team: going... ...going... gone Time to study!
  • Before Tax Day
  • After Tax Day
  • Identify trends
  • Learn from failure
  • Failed navigation? Examining unexpected searching Look for places searches happen beyond main page What’s going on? • Navigational failure? • Content failure? • Something else?
  • 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
  • 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)
  • 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)
  • 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)
  • Learn from search sessions
  • Sample search session (Teach for America intranet)
  • Session analysis These queries co-occur within sessions: why? 
 
 

  • TFAnet session analysis results • Searches for “delta ICEG” perform poorly (way below the fold) • Users then try an (incorrect) alternative (“delta learning team”) 54
  • Identify content gaps
  • 0 results report (from behaviortracking.com) Are we missing something? Are we missing a type of something?
  • Identifying gaps helps force an issue
  • Identify failed content
  • 
 
 
 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
  • Make content owners into stakeholders
  • 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)
  • Connecting pages (and their owners) that are found through search...
  • ...with how those pages were found
  • Predict the future
  • 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!
  • 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
  • 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.
  • 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
  • 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?
  • 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!
  • 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...
  • 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
  • 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
  • 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
  • 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
  • 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
  • Mapping KPI and metrics: A generic “search success” KPI
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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 on a site report card SSA helps determine common information needs
  • ©2010 Louis Rosenfeld, LLC (www.louisrosenfeld.com). All rights reserved. From Christian Rohrer, xdstrategy.com
  • ©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)
  • 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
  • Comparing referral queries with local queries
  • 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
  • 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
  • Louis Rosenfeld lou@louisrosenfeld.com www.louisrosenfeld.com www.rosenfeldmedia.com @louisrosenfeld @rosenfeldmedia This presentation available on SlideShare: http://slidesha.re/otzE2t Say hello