You toolbox for eBusiness
    success - Web Analytics
             Congress 2010


w/Sean Power & Jeroen Tjepkema
I betook
myself to
linking...
...tis some
visitor
entreating
entrance at
my chamber
door
About you
We won’t tweet about it. Honest.
@seanpower
@jeroentjepkema
   #wac2010
What kind of site are you?
About your company
What’s your job?
Webops (it’s up, and it’s fast)
User experience (it’s easy to use)
Community management and monitoring
Ma...
What we’ll cover

 Analytics, interaction, UX, Voice of the Customer
 EUEM, synthetic tests, RUM
 Online communities, inte...
Which pretty much means
We’re going to waste some of your time.
What’s complete web
monitoring?
Ries, Mclure, and Blank
are often misquoted.
They never said “fail faster”
Instead:
Learn and adapt.
Waterfall, agile, and lean
Three approaches for three situations
Waterfall methodologies
Know the problem and the solution
Known ways to                  Known set of
 satisfy them                  requirements



   Spec         Build   Test   ...
Known ways to                  Known set of
 satisfy them                  requirements



   Spec         Build   Test   ...
Agile methodologies
Know the problem, iterate on the solution
Unclear how                             Known set of
to satisfy them                          requirements



 Problem
sta...
Unclear how to                           Unknown set
 satisfy them                           of requirements



 Problem
s...
Most new startups
don’t know even know what problem they solve.
Possible viable
                               offering

                                                                 ...
As we become more agile,
we need to be more aware.
Startups 101: as seen by Eric Ries & Sean Ellis
                      ps: the concepts in the next two slides are full of ...
!"#                    !"#$"%&'()*!+',-(,(
                                                     !"#                       ...
Complete Web Monitoring
The big picture
Users do what we wanted

Enrolment: They sign up
Purchases: They buy stuff
Invitations: They tell their friends
Stickiness...
What could we watch?
What we’d like to know                      Tool set
How much did visitors benefit my business?   Inte...
How much did visitors
benefit my business?
Internal analytics
 Conversion and           Billing and account use
 abandonmen...
Where’s my traffic coming
from?
External analytics
 Referring websites
 Inbound links from social networks
 Visitor motivat...
What’s working best (and
worst)?
Usability testing, A/B testing
 Site effectiveness         Trouble ticketing and
        ...
How good is my relationship
with my market?
Customer surveys, community monitoring
 Loyalty
 Enrollment
 Reach and rewards
How healthy is my
infrastructure?
Performance monitoring
 Availability and           Impact of performance
 performance   ...
How am I doing against my
competitors?
Performance monitoring
 Site popularity and ranking
 How are people finding my compe...
Where are my risks?
Search, alerting
 Trolling and spamming
 Copyright and legal liability
 Fraud, privacy, and account sh...
What are people saying
about me?
Search, community monitoring
 Site reputation
 Trends
 Social network activity
How is my content being
used elsewhere?
Search, external analytics
 API access and usage
 Mashups, stolen content, and ill...
The difference between
accounting and optimization
http://www.flickr.com/photos/roryfinneren/65729247
Chair rentals per day
 50


37,5


 25


12,5


  0
       1      2          3          4         5          6         7  ...
http://www.imdb.com/media/rm3768753408/tt0073195
http://www.flickr.com/photos/kapungo/2287237966
Ice cream and drownings
10000


1000


 100


  10


   1
        Ice cream consumption        Drownings
http://www.flickr.com/photos/25159787@N07/3766111564
http://www.flickr.com/photos/wheressteve/3284532080
http://www.flickr.com/photos/wtlphotos/1086968783
True causality
10000


1000


 100


  10


   1
        Jan Feb Mar Apr May Jun Jul Aug Sept Oct Nov Dec
             Ice...
http://www.flickr.com/photos/stuttermonkey/57096884
http://www.flickr.com/photos/germanuncut77/3785152581
http://www.flickr.com/photos/fasteddie42/2421039207
Everybody has goals.




             http://www.flickr.com/photos/itsgreg/446061432/
Organic                                  Ad
                       Campaigns
     search                                 n...
Bad
                                                                                   $
                                 ...
Enterprise subscriber $

                                         1

                              End user (employee) $
 ...
$



                                     Media site
     Enrolment                         Targeted
                     ...
Analytics is the measurement of
movement towards those goals.




                   http://www.flickr.com/photos/itsgreg/4...
ATTENTION               ENGAGEMENT CONVERSION

              NEW
            VISITORS

 SEARCHES   GROWTH                 ...
http://www.flickr.com/photos/itsgreg/446061432/




Lots of moving parts.
“Hard” data

  Analytics         Usability     Performability
(what did they   (how did they    (could they do
  do on the...
“Hard” data

  Analytics         Usability     Performability
(what did they   (how did they    (could they do
  do on the...
http://www.d-9.com/
These people drive nicer cars
than us. :/




       Source: http://www.webanalyticsdemystified.com/sample/Web_Analytics_De...
Hits
http://bit.ly/5H5Xc6
Hits   Pages
http://www.cs.cmu.edu/~jasonh/blog/evolution-big.png
Hits   Pages   Sessions
Hits   Pages   Sessions   Visitors
Hits   Pages   Sessions   Visitors   Segments
e
      ar
     e ts
  es en
Th gm
  se
(You can make your own.)
http://www.human20.com/who-
owns-your-voice-online/
?utm_source=abowyer
&utm_medium=twitter
&utm_content=communication
&ut...
Who would you rather have sending a message?
Old analytics:
report the news

http://www.flickr.com/photos/thomasclaveirole/538819881/
http://www.flickr.com/photos/23883605@N06/2317982570/sizes/l/
Old analytics: New analytics:
report the news optimize goals

http://www.flickr.com/photos/thomasclaveirole/538819881/   ht...
blah blah blah ...
A unique visitor arrives at your website, possibly after following a link that
referred them. They land...
Find the site


The three
stages of a     Use the site

unique visit
               Leave the site
Find the site:
How did they get there?
“Direct” traffic isn’t.

 Type-In Traffic
 Bookmarking
 JavaScript redirect
 Browser Inconsistencies
 Bots, Spiders and Prob...
source: the conversation prism by Brian Solis and JESS3
                  http://www.theconversationprism.com
Use the site:
What did they do?
Landing page:
    Task:            View one story
Create account
                                             Task: Log in...
Landing page:
  Create acct.
Create acct.        View one story
   Form uptime    Place: View stories
                    ...
Places
Efficiency matters
  How quickly, how many,
  productivity
  Learning curve OK
Leave when they’re bored
Collect “aha...
Tasks
Effectiveness matters
  Completion, abandonment
  Intuitiveness rules
Leave when they change their
mind or it breaks...
Now suppose that you have a specific goal, such as a visitor filling out a survey on your website. You can analyze how many ...
Leave the site:
Parting is such sweet sorrow
Pages per visit                         Time on site



           :-D                                       :-)
   16    ...
“Hard” data

  Analytics         Usability     Performability
(what did they   (how did they    (could they do
  do on the...
How did they do it?
Web Interaction Analytics
http://www.flickr.com/photos/trekkyandy/189717616/
Yes

                                                                           Seen
                                     ...
http://www.flickr.com/photos/americanlady/3118301118




consume

 http://

give data

navigate
Usability issue 1:
Visitors don’t see what you
      wanted them to.
Your mileage will vary.
Usability issue 2:
Visitors don’t interact as you
          intended.
Usability issue 3:
Visitors don’t input data
“Hard” data

  Analytics         Usability     Performability
(what did they   (how did they    (could they do
  do on the...
Voice of the customer
Why did they do it?
People on the internet do
      weird things
So what’s this “VOC” thing?

 Get new ideas
 Evaluate things you can’t collect in other ways
 Evaluate sentiment
 Collect ...
http://4.bp.blogspot.com/_0iHpQZ3MU1E/SnJxr-HYeoI/AAAAAAAAAAw/pnMWYdWi75A/s320/oldlady.jpg
http://threeminds.organic.com/virtual%20online%20community2.jpg
“Hard” data

  Analytics         Usability     Performability
(what did they   (how did they     (could they
  do on the  ...
01 traditional analytics
01 traditional analytics
01 traditional analytics
01 traditional analytics
01 traditional analytics
01 traditional analytics
01 traditional analytics
01 traditional analytics
01 traditional analytics
01 traditional analytics
01 traditional analytics
01 traditional analytics
01 traditional analytics
01 traditional analytics
01 traditional analytics
01 traditional analytics
01 traditional analytics
01 traditional analytics
01 traditional analytics
01 traditional analytics
01 traditional analytics
01 traditional analytics
01 traditional analytics
01 traditional analytics
01 traditional analytics
01 traditional analytics
01 traditional analytics
01 traditional analytics
01 traditional analytics
01 traditional analytics
01 traditional analytics
01 traditional analytics
01 traditional analytics
01 traditional analytics
01 traditional analytics
01 traditional analytics
01 traditional analytics
01 traditional analytics
01 traditional analytics
01 traditional analytics
01 traditional analytics
01 traditional analytics
01 traditional analytics
01 traditional analytics
01 traditional analytics
01 traditional analytics
Upcoming SlideShare
Loading in...5
×

01 traditional analytics

856

Published on

Published in: Technology, Business
0 Comments
2 Likes
Statistics
Notes
  • Be the first to comment

No Downloads
Views
Total Views
856
On Slideshare
0
From Embeds
0
Number of Embeds
1
Actions
Shares
0
Downloads
0
Comments
0
Likes
2
Embeds 0
No embeds

No notes for slide

01 traditional analytics

  1. 1. You toolbox for eBusiness success - Web Analytics Congress 2010 w/Sean Power & Jeroen Tjepkema
  2. 2. I betook myself to linking...
  3. 3. ...tis some visitor entreating entrance at my chamber door
  4. 4. About you We won’t tweet about it. Honest.
  5. 5. @seanpower @jeroentjepkema #wac2010
  6. 6. What kind of site are you?
  7. 7. About your company
  8. 8. What’s your job? Webops (it’s up, and it’s fast) User experience (it’s easy to use) Community management and monitoring Market research (what people think and why) Support Other
  9. 9. What we’ll cover Analytics, interaction, UX, Voice of the Customer EUEM, synthetic tests, RUM Online communities, internal communities Competitive analysis Integrating data sources
  10. 10. Which pretty much means We’re going to waste some of your time.
  11. 11. What’s complete web monitoring?
  12. 12. Ries, Mclure, and Blank are often misquoted.
  13. 13. They never said “fail faster”
  14. 14. Instead:
  15. 15. Learn and adapt.
  16. 16. Waterfall, agile, and lean Three approaches for three situations
  17. 17. Waterfall methodologies Know the problem and the solution
  18. 18. Known ways to Known set of satisfy them requirements Spec Build Test Launch
  19. 19. Known ways to Known set of satisfy them requirements Spec Build Test Launch
  20. 20. Agile methodologies Know the problem, iterate on the solution
  21. 21. Unclear how Known set of to satisfy them requirements Problem statement Build Test Viable? Launch Sprints Adjust
  22. 22. Unclear how to Unknown set satisfy them of requirements Problem statement Build Test Viable? Launch Iterations & pivots Redefine problem, business
  23. 23. Most new startups don’t know even know what problem they solve.
  24. 24. Possible viable offering You are Trial startup t here vo Pi Possible Possible Possible viable Trial startup problem Trial startup viable offering space offering Trial startup Possible viable offering
  25. 25. As we become more agile, we need to be more aware.
  26. 26. Startups 101: as seen by Eric Ries & Sean Ellis ps: the concepts in the next two slides are full of awesome. Look Sean, Eric and Dave up. IDEAS Learn  Faster Code  Faster LEARN Growth BUILD Unit  Tests Split  Tests Customer  Interviews Transition to Usability  Tests Customer  Development Growth Con7nuous  Integra7on Five  Whys  Root  Cause  Analysis Incremental  Deployment Customer  Advisory  Board Free  &  Open-­‐Source  Components Falsifiable  Hypotheses Product/Market Fit Cloud  Compu7ng by: Sean Ellis Cluster  Immune  System Product  Owner  Accountability Customer  Archetypes Just-­‐in-­‐7me  Scalability DATA CODE Refactoring Cross-­‐func7onal  Teams Semi-­‐autonomous  Teams Developer  Sandbox Smoke  Tests Measure  Faster MEASURE Split  Tests Funnel  Analysis Clear  Product  Owner Cohort  Analysis Con7nuous  Deployment Net  Promoter  Score Usability  Tests Search  Engine  Marke7ng Real-­‐7me  Monitoring Real-­‐Time  Aler7ng Customer  Liaison Predic7ve  Monitoring
  27. 27. !"# !"#$"%&'()*!+',-(,( !"# !" !"#$%&' !"#$%&' ()*+",-. !"#$% !"#$%&'( !""#$%$&'()*+# !"#$%&'()$*'()+ !"#$% !"#$"%&'()*!+',-(,( !"#$%&' 1.  ACQUISITION RAL FER 4.  RE Emails  &  Alerts 2.  A !"#$%&'$()(*&+,-+'(.&'$ ctiv !"#$%&'(')$*+,-& atio ON NTI !"#$%&'( E TE )*+'%"*, 3.  R n System  Events  &  Time-­‐based   Features Blogs,  New  Content !"#$%&' !"#$%&'("%)'*$% +,-#./01203*#$%'2. 5.  R ev e Website.com nue  $$$ AARRR! by Dave McClure
  28. 28. Complete Web Monitoring The big picture
  29. 29. Users do what we wanted Enrolment: They sign up Purchases: They buy stuff Invitations: They tell their friends Stickiness: They stay for longer Loyalty: They come back Contribution: They add content
  30. 30. What could we watch? What we’d like to know Tool set How much did visitors benefit my business? Internal analytics Where is my traffic coming from? External analytics What’s working best (and worst?) Usability testing How good’s my relationship with my market? Customer surveys, community How healthy is my infrastructure? Performance monitoring How am I doing against my competitors? Search, external testing Where are my risks? Search, alerting What are people saying about me? Search, community monitoring How is my content being used elsewhere? Search, external analytics
  31. 31. How much did visitors benefit my business? Internal analytics Conversion and Billing and account use abandonment Click-throughs Offline activity User-generated content Subscriptions
  32. 32. Where’s my traffic coming from? External analytics Referring websites Inbound links from social networks Visitor motivation
  33. 33. What’s working best (and worst)? Usability testing, A/B testing Site effectiveness Trouble ticketing and escalation Upselling effectiveness Content popularity Ad and campaign effectiveness Usability Findability and search User productivity effectiveness Community ranking and rewards
  34. 34. How good is my relationship with my market? Customer surveys, community monitoring Loyalty Enrollment Reach and rewards
  35. 35. How healthy is my infrastructure? Performance monitoring Availability and Impact of performance performance on outcomes SLA compliance Content delivery Capacity and flash traffic
  36. 36. How am I doing against my competitors? Performance monitoring Site popularity and ranking How are people finding my competitors? Relative site performance Competitor activity
  37. 37. Where are my risks? Search, alerting Trolling and spamming Copyright and legal liability Fraud, privacy, and account sharing
  38. 38. What are people saying about me? Search, community monitoring Site reputation Trends Social network activity
  39. 39. How is my content being used elsewhere? Search, external analytics API access and usage Mashups, stolen content, and illegal syndication Integration with legacy systems
  40. 40. The difference between accounting and optimization
  41. 41. http://www.flickr.com/photos/roryfinneren/65729247
  42. 42. Chair rentals per day 50 37,5 25 12,5 0 1 2 3 4 5 6 7 8 9 10 http://www.rvca.com/anp/wp-content/plugins/wp-o-matic/cache/57226_07+proof+1a+hb+beach+day.jpg
  43. 43. http://www.imdb.com/media/rm3768753408/tt0073195
  44. 44. http://www.flickr.com/photos/kapungo/2287237966
  45. 45. Ice cream and drownings 10000 1000 100 10 1 Ice cream consumption Drownings
  46. 46. http://www.flickr.com/photos/25159787@N07/3766111564
  47. 47. http://www.flickr.com/photos/wheressteve/3284532080
  48. 48. http://www.flickr.com/photos/wtlphotos/1086968783
  49. 49. True causality 10000 1000 100 10 1 Jan Feb Mar Apr May Jun Jul Aug Sept Oct Nov Dec Ice cream consumption Drownings Temperature
  50. 50. http://www.flickr.com/photos/stuttermonkey/57096884
  51. 51. http://www.flickr.com/photos/germanuncut77/3785152581
  52. 52. http://www.flickr.com/photos/fasteddie42/2421039207
  53. 53. Everybody has goals. http://www.flickr.com/photos/itsgreg/446061432/
  54. 54. Organic Ad Campaigns search network $ 1 1 1 Advertiser site Visitor 2 O er 3 $ 8 Upselling 4 Abandonment Reach 5 Purchase step $ Mailing, alerts, Purchase step $ 9 promotions $ Conversion $ Disengagement 7 Enrolment 6 Impact on site $ Positive $ Negative
  55. 55. Bad $ 4 content Social Search Invitation network link results 4 Good content 1 $ 1 1 Collaboration site 2 Visitor Content creation Moderation $ 3 Spam & trolls $ Engagement 5 Viral 6 Social graph spread 7 Disengagement $ Impact on site $ Positive $ Negative
  56. 56. Enterprise subscriber $ 1 End user (employee) $ Refund $ 2 Renewal, upsell, SLA reference SaaS site violation Performance Good Bad 3 Helpdesk Support 5 $ Usability escalation costs 7 4 Good Bad Productivity Good Bad 6 Churn $ Impact on site $ Positive $ Negative
  57. 57. $ Media site Enrolment Targeted 2 embedded ad 5 $ 6 1 Ad Visitor network 4 3 5 Advertiser $ Departure $ site Impact on site $ Positive $ Negative
  58. 58. Analytics is the measurement of movement towards those goals. http://www.flickr.com/photos/itsgreg/446061432/
  59. 59. ATTENTION ENGAGEMENT CONVERSION NEW VISITORS SEARCHES GROWTH CONVERSION PAGES TIME RATE TWEETS NUMBER OF VISITS PER ON x MENTIONS VISIT SITE GOAL ADS SEEN LOSS VALUE BOUNCE RATE
  60. 60. http://www.flickr.com/photos/itsgreg/446061432/ Lots of moving parts.
  61. 61. “Hard” data Analytics Usability Performability (what did they (how did they (could they do do on the interact with what they site?) it?) wanted to?) Complete Web Monitoring VoC Communilytics Competition (what were (what were (what are they their they saying?) up to?) motivations?) “Soft” data
  62. 62. “Hard” data Analytics Usability Performability (what did they (how did they (could they do do on the interact with what they site?) it?) wanted to?) Complete Web Monitoring VoC Communilytics Competition (what were (what were (what are they their they saying?) up to?) motivations?) “Soft” data
  63. 63. http://www.d-9.com/
  64. 64. These people drive nicer cars than us. :/ Source: http://www.webanalyticsdemystified.com/sample/Web_Analytics_Demystified_RESEARCH_-_March_2007_-_Salary_Survey.pdf
  65. 65. Hits
  66. 66. http://bit.ly/5H5Xc6
  67. 67. Hits Pages
  68. 68. http://www.cs.cmu.edu/~jasonh/blog/evolution-big.png
  69. 69. Hits Pages Sessions
  70. 70. Hits Pages Sessions Visitors
  71. 71. Hits Pages Sessions Visitors Segments
  72. 72. e ar e ts es en Th gm se
  73. 73. (You can make your own.)
  74. 74. http://www.human20.com/who- owns-your-voice-online/ ?utm_source=abowyer &utm_medium=twitter &utm_content=communication &utm_campaign=post
  75. 75. Who would you rather have sending a message?
  76. 76. Old analytics: report the news http://www.flickr.com/photos/thomasclaveirole/538819881/
  77. 77. http://www.flickr.com/photos/23883605@N06/2317982570/sizes/l/
  78. 78. Old analytics: New analytics: report the news optimize goals http://www.flickr.com/photos/thomasclaveirole/538819881/ http://www.flickr.com/photos/sanchom/2963072255/
  79. 79. blah blah blah ... A unique visitor arrives at your website, possibly after following a link that referred them. They land on a web page, and either bounce (leave immediately) or request additional pages. In time, they may complete a transaction that’s good for your business, converting them from a mere buyer into something more—a customer, a user, a member, or a contributor—depending on the kind of site you’re running. On the other hand, they may abandon that transaction and ultimately exit the website. That visitor has many external attributes—such as the browser they’re using, or where they’re surfing from—that let you group them into segments. They may also see different offers or pages during their visit, which are the basis for further segmentation. The goal of analytics, then, is to maximize conversions by optimizing your website, often by experimenting with different content, layout, and campaigns, and analyzing the results of those experiments on various internal and external segments.
  80. 80. Find the site The three stages of a Use the site unique visit Leave the site
  81. 81. Find the site: How did they get there?
  82. 82. “Direct” traffic isn’t. Type-In Traffic Bookmarking JavaScript redirect Browser Inconsistencies Bots, Spiders and Probes
  83. 83. source: the conversation prism by Brian Solis and JESS3 http://www.theconversationprism.com
  84. 84. Use the site: What did they do?
  85. 85. Landing page: Task: View one story Create account Task: Log in Pick name Place: View stories Check if free Enter credentials Vote up Next 25 Set Password Verify Vote down Last 25 CAPTCHA Recovery Send mail Place: Read Get confirm poster comments Vote up Next 25 Task: Vote down Last 25 Forward a story Task: Submit Enter recipients a new story Place: My Enter message Enter URL account Send Describe Change My address comments Deduplicate Change PW See karma Post it
  86. 86. Landing page: Create acct. Create acct. View one story Form uptime Place: View stories Task: Log in # started Place: View stories Bad form Stories/visit # up/down Place: Read # CAPTCHA poster comments Time/story Mail uptime Top stories Task: Forward a story Task: Submit Refresh time Mail bounced Views/page a new story Place: My Confirm & return account Return 3x
  87. 87. Places Efficiency matters How quickly, how many, productivity Learning curve OK Leave when they’re bored Collect “aha” feedback A/B test content for pages/session, exits
  88. 88. Tasks Effectiveness matters Completion, abandonment Intuitiveness rules Leave when they change their mind or it breaks Collect “motivation” feedback A/B test layouts for conversion
  89. 89. Now suppose that you have a specific goal, such as a visitor filling out a survey on your website. You can analyze how many people completed that goal over time and measure the success of your business in a report like the one in
  90. 90. Leave the site: Parting is such sweet sorrow
  91. 91. Pages per visit Time on site :-D :-) 16 2,1 15 1,6 Minutes 14 1,1 13 0,5 12 0 September October September October Email opt-outs Days between visits :-| O_o 26.000 5 19.500 3,75 13.000 2,5 6.500 1,25 0 0 September October September October
  92. 92. “Hard” data Analytics Usability Performability (what did they (how did they (could they do do on the interact with what they site?) it?) wanted to?) Complete Web Monitoring VoC Communilytics Competition (what were (what were (what are they their they saying?) up to?) motivations?) “Soft” data
  93. 93. How did they do it? Web Interaction Analytics
  94. 94. http://www.flickr.com/photos/trekkyandy/189717616/
  95. 95. Yes Seen False (perceptible) Perceptual information affordance affordance (did I see it?) Unseen Correct (hidden) rejection affordance No No Affordance Yes (was I supposed to interact with it?) Adapted from Gaver (1991)
  96. 96. http://www.flickr.com/photos/americanlady/3118301118 consume http:// give data navigate
  97. 97. Usability issue 1: Visitors don’t see what you wanted them to.
  98. 98. Your mileage will vary.
  99. 99. Usability issue 2: Visitors don’t interact as you intended.
  100. 100. Usability issue 3: Visitors don’t input data
  101. 101. “Hard” data Analytics Usability Performability (what did they (how did they (could they do do on the interact with what they site?) it?) wanted to?) Complete Web Monitoring VoC Communilytics Competition (what were (what were (what are they their they saying?) up to?) motivations?) “Soft” data
  102. 102. Voice of the customer Why did they do it?
  103. 103. People on the internet do weird things
  104. 104. So what’s this “VOC” thing? Get new ideas Evaluate things you can’t collect in other ways Evaluate sentiment Collect demographics data
  105. 105. http://4.bp.blogspot.com/_0iHpQZ3MU1E/SnJxr-HYeoI/AAAAAAAAAAw/pnMWYdWi75A/s320/oldlady.jpg
  106. 106. http://threeminds.organic.com/virtual%20online%20community2.jpg
  107. 107. “Hard” data Analytics Usability Performability (what did they (how did they (could they do on the interact with do what they site?) it?) wanted to?) Complete Web Monitoring VoC Communilytics Competition (what were (what were (what are they their they saying?) up to?) motivations?) “Soft” data

×