Originally presented at SXSW March 13, 2011, on panel with Fred Beecher and Austin Govella. Modified and updated for Web 2.0 Expo talk, October 12, 2011, UX Web Summit September 26, 2012.
Originally presented at SXSW March 13, 2011, on panel with Fred Beecher and Austin Govella. Modified and updated for Web 2.0 Expo talk, October 12, 2011, UX Web Summit September 26, 2012.
handicraftNice presentation. it gave me new ways to use analytic to understand the visitor trend. we are trying hard to increase visitors for website http://www.mallxs.com thanks a lot. do share some more topics lie this.. i would bookmark your profile here to follow8 months ago
Are you sure you want to
OsoOscarno puedo hablar el ingles hasi que no entiendo lo que dicen9 months ago
Louis Rosenfeld, Publisher / information architect at Rosenfeld Media, LLCI don't think it'll ever get there, Matthew--there are simply too many ad hoc needs (and reports), as well as the need to 'play' with the data (e.g., Exploratory Data Analysis) that you should apply at least to the short head queries. And because you're working with the short head, it's not necessarily much work.2 years ago
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
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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
Site Search Analytics: 8 things you can doPresentation Transcript
Search Analyticsfor Your Site:8 things you can doLouis Rosenfeldlou@louisrosenfeld.com • @louisrosenfeldUX Web Summit • September 26, 2012
Hello, my name is Lou www.louisrosenfeld.com | www.rosenfeldmedia.com
Agenda
Agenda1.The basics of Site Search Analytics (SSA)
Agenda1.The basics of Site Search Analytics (SSA)2.8 things you can do with SSA
Agenda1.The basics of Site Search Analytics (SSA)2.8 things you can do with SSA3.Some things you can do today
Agenda1.The basics of Site Search Analytics (SSA)2.8 things you can do with SSA3.Some things you can do today4.A short plea for peace, love and harmony
Let’s look at the data
No, let’s look at the real dataCritical 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 0.02XXX.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 look at the real dataCritical elements in bold: IP address, time/date stamp, query, and # of results: What are usersXXX.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 0.02XXX.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 look at the real dataCritical elements in bold: IP address, time/date stamp, query, and # of results: What are usersXXX.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 0.02XXX.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 waymeet the needs of your most important audiences
A handful of queries/tasks/ways to navigate/features/ documents A little goes a long waymeet 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 waymeet the needs of your most important audiences
A handful of queries/tasks/ways to navigate/features/ documents A little goes a long waymeet 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 waymeet the needs of your most important audiences
A handful of queries/tasks/ways to navigate/features/ documents A little goes a long waymeet 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 Distribution, textually
8 things you can do with SSA 1.Make it harder to get lost in deep content 2.Make search smarter 3.Reduce jargon 4.Learn how your audiences differ 5.Know when to publish what 6.Own and enjoy your failures 7.Avoid disaster 8.Predict the future
#1Make it harder to get lost
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 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 tobetter contextual navigation album pages artist descriptions TV listingsalbum reviews discography artist bios
Clear content types improvesearch performance
Clear content types improvesearch performance Content objects related to products Raw search results
#2Make search smarter
Contextualizing “advanced” features
Session data suggestprogression and context search session patterns search session patterns 1. solar energy 1. solar energy 2. solar energy charts 2. how solar energy works search session patterns search session patterns 1. solar energy 1. solar energy 2. explain solar energy 2. energy search session patterns 1. solar energy 2. solar energy news
Recognizingproper nouns,dates, andunique ID#s
#3Reduce jargon
Saving the brand by killing jargonat a community collegeJargon related to online education: FlexEd, COD, College on DemandMarketing’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 queriesResult: content relabeled, money saved
#4Learn how your audiences differ
Who cares about what?
Who cares about what?
Who cares about what?
Why analyze queries by audience?Fortify your personas with dataLearn 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 programDetermine what’s commonly important to all audiences (these queries better work well)
#5Know 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
Time toInterest in the study! football team: going... ...going... gone
Where navigation is failing (“Professional Resources” page)Do users andAIGA meandifferentthings by“ProfessionalResources”?
Comparing what users findand what they want
Comparing what users findand 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)
#7Avoid disasters
The new and improved search enginethat wasn’tVanguard used SSA to help benchmark existing search engine’s performance and help select new engineNew search engine “performed” poorlyBut IT needed Information Architect & Dev Team Meeting Where’s the proof? convincing You can’t tell to delay for sure. Search seems to have a few problems… Nah launch .
What to do?Test performance of common queries“Before and after” testing using two sets of metrics 1.Relevance: how reliably the search engine returns the best matches first 2.Precision: proportion of relevant results clustered at the top of the list
Old engine (target) and new compared Note: low relevance and high precision scores are optimal More on Vanguard case study: http://bit.ly/D3B8c
Old engine (target) and new compared uh-oh Note: low relevance and high precision scores are optimal More on Vanguard case study: http://bit.ly/D3B8c
Old engine (target) and new compared uh-oh better Note: low relevance and high precision scores are optimal More on Vanguard case study: http://bit.ly/D3B8c
#8Predict the future
Shaping theFinancial Times’ editorial agendaFT compares these • Spiking queries for proper nouns (i.e., people and companies) • Recent editorial coverage of people and companiesDiscrepancy? • Breaking story?! • Let the editors know!
8 things you can do with SSA 1.Make it harder to get lost in deep content 2.Make search smarter 3.Reduce jargon 4.Learn how your audiences differ 5.Know when to publish what 6.Own and enjoy your failures 7.Avoid disaster 8.Predict the future
3 things you can do today1.Set up SSA in Google Analytics2.Query your queries3.Start developing a site report card
Turn on SSA in Google AnalyticsSet up GA for your site if you haven’t alreadyThen 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 byquerying your queriesStarter 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 workon a site report card
Use SSA to start work SSA helps determine commonon a site report card information needs
Can SSA bring us together?
Lou’s TABLE OF OVERGENERALIZED Web Analytics User Experience DICHOTOMIES Users intentions and Users behaviors (whatsWhat they analyze happening) motives (why those things happen) Qualitative methods forWhat methods they Quantitative methods to explaining why things employ determine whats happening happen Helps users achieve goalsWhat theyre trying Helps the organization meet (expressed as tasks or to achieve goals (expressed as KPI) topics of interest) Uncover patterns and Measure performance (goal-How they use data driven analysis) surprises (emergent analysis) Statistical data ("real" data Descriptive data (in smallWhat kind of data in large volumes, full of volumes, generated in lab they use errors) environment, full of errors)
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 helloLouis Rosenfeldlou@louisrosenfeld.comwww.louisrosenfeld.comwww.rosenfeldmedia.com@louisrosenfeld@rosenfeldmediaThis presentation available on SlideShare:http://slidesha.re/otzE2t
Guilherme Bueno
Thanks！ 2 years ago