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Where communities fit in
& the story of
putting numbers on things
Everybody has goals.




                  http://www.flickr.com/photos/itsgreg/446061432/
Where communities fit in a complete monitoring model
Where communities fit in a complete monitoring model
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
Where communities fit in a complete monitoring model
Where communities fit in a complete monitoring model
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
Where communities fit in a complete monitoring model
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
Where communities fit in a complete monitoring model
$



                                     Media site
     Enrolment                         Targeted
                                 2   embedded ad       5
                                                               $
           6                                       1
                                                                 Ad
                      Visitor
                                                               network
           4
                                 3                         5
                                      Advertiser   $
Departure $                              site


Impact on site
 $     Positive   $   Negative
Analytics is the
measurement of movement
towards those goals.




                http://www.flickr.com/photos/itsgreg/446061432/
Where communities fit in a complete monitoring model
ATTENTION




 SEARCHES
  TWEETS
 MENTIONS
 ADS SEEN
ATTENTION




 SEARCHES
  TWEETS    NUMBER
            OF VISITS
 MENTIONS
 ADS SEEN
ATTENTION




 SEARCHES
  TWEETS    NUMBER
            OF VISITS
 MENTIONS
 ADS SEEN     LOSS
            BOUNCE
             RATE
ATTENTION

              NEW
            VISITORS

 SEARCHES   GROWTH

  TWEETS    NUMBER
            OF VISITS
 MENTIONS
 ADS SEEN     LOSS
            BOUNCE
             RATE
ATTENTION               ENGAGEMENT

              NEW
            VISITORS

 SEARCHES   GROWTH
                        PAGES
  TWEETS    NUMBER
                         PER
            OF VISITS
 MENTIONS                VISIT
 ADS SEEN     LOSS
            BOUNCE
             RATE
ATTENTION               ENGAGEMENT

              NEW
            VISITORS

 SEARCHES   GROWTH
                        PAGES    TIME
  TWEETS    NUMBER
                         PER      ON
            OF VISITS
 MENTIONS                VISIT   SITE
 ADS SEEN     LOSS
            BOUNCE
             RATE
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
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       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
“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
“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
http://www.d-9.com/
These people drive nicer cars
than us. :/




       Source: http://www.webanalyticsdemystified.com/sample/Web_Analytics_Demystified_RESEARCH_-_March_2007_-_Salary_Survey.pdf
Hits
http://bit.ly/5H5Xc6
Hits   Pages
http://www.cs.cmu.edu/~jasonh/blog/evolution-big.png
Where communities fit in a complete monitoring model
Hits   Pages   Sessions
Where communities fit in a complete monitoring model
Where communities fit in a complete monitoring model
Hits   Pages   Sessions   Visitors
Where communities fit in a complete monitoring model
Hits   Pages   Sessions   Visitors   Segments
Where communities fit in a complete monitoring model
e
      ar
     e ts
  es en
Th gm
  se
(You can make your own.)
Where communities fit in a complete monitoring model
Where communities fit in a complete monitoring model
Where communities fit in a complete monitoring model
http://www.human20.com/who-
owns-your-voice-online/
?utm_source=abowyer
&utm_medium=twitter
&utm_content=communication
&utm_campaign=post
Where communities fit in a complete monitoring model
Who would you rather have sending a message?
Who would you rather have sending a message?
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/   http://www.flickr.com/photos/sanchom/2963072255/
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.
Find the site


The three
stages of a     Use the site

unique visit
               Leave the site
Find the site:
How did they get there?
Where communities fit in a complete monitoring model
Where communities fit in a complete monitoring model
Where communities fit in a complete monitoring model
Where communities fit in a complete monitoring model
Where communities fit in a complete monitoring model
Where communities fit in a complete monitoring model
Where communities fit in a complete monitoring model
Where communities fit in a complete monitoring model
Where communities fit in a complete monitoring model
Use the site:
What did they do?
Where communities fit in a complete monitoring model
Where communities fit in a complete monitoring model
Landing page:
View one story
Landing page:
View one story
                 Task: Log in
                  Enter credentials

                       Verify

                     Recovery
Landing page:
View one story
                  Task: Log in
                   Enter credentials

                         Verify

                       Recovery




                       Task:
                 Forward a story
                    Enter recipients

                    Enter message

                         Send
Landing page:
    Task:          View one story
Create account
                                     Task: Log in
    Pick name

   Check if free                      Enter credentials

   Set Password                             Verify

    CAPTCHA                               Recovery

    Send mail

    Get confirm


                                          Task:
                                    Forward a story
                                       Enter recipients

                                       Enter message

                                            Send
Landing page:
    Task:          View one story
Create account
                                     Task: Log in
    Pick name

   Check if free                      Enter credentials

   Set Password                             Verify

    CAPTCHA                               Recovery

    Send mail

    Get confirm


                                          Task:
                                    Forward a story
 Task: Submit                          Enter recipients

  a new story                          Enter message

                                            Send
     Enter URL

      Describe

    Deduplicate

       Post it
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

    Get confirm


                                                Task:
                                          Forward a story
 Task: Submit                                Enter recipients

  a new story                                Enter message

                                                  Send
     Enter URL

      Describe

    Deduplicate

       Post it
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                                Enter message

                                                  Send
     Enter URL

      Describe

    Deduplicate

       Post it
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
Landing page:
Create acct.     View one story
                                      Task: Log in
               Place: View stories




                  Place: Read
               poster comments
                                         Task:
                                     Forward a story
Task: Submit
 a new story       Place: My
                    account
Landing page:
  Create acct.
Create acct.        View one story
   Form uptime    Place: View stories
                                         Task: Log in

      # started
      Bad form
                     Place: Read
    # CAPTCHA     poster comments

    Mail uptime                             Task:
                                        Forward a story
  Mail bounced
Task: Submit
 a new story          Place: My
Confirm & return        account

     Return 3x
Landing page:
Create acct.          View one story
                                              Task: Log in
                    Place: View stories

                    Place: View stories
               Stories/visit
                     Place: Read
                                          # up/down
                     poster comments
               Time/story                 Top stories
                                                Task:
                                             Forward a story
Task: Submit   Refresh time               Views/page
 a new story            Place: My
                         account
Landing page:
Create acct.     View one story
                                      Task: Log in
               Place: View stories




                  Place: Read
               poster comments
                                         Task:
                                     Forward a story
Task: Submit
 a new story       Place: My
                    account
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
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
Where communities fit in a complete monitoring model
Where communities fit in a complete monitoring model
Where communities fit in a complete monitoring model
Where communities fit in a complete monitoring model
Where communities fit in a complete monitoring model
Where communities fit in a complete monitoring model
Leave the site:
Parting is such sweet sorrow
Where communities fit in a complete monitoring model
Pages per visit                         Time on site
   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
26,000                                     5
19,500                                   3.75
13,000                                    2.5
 6,500                                   1.25
    0                                      0
         September   October                    September   October
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
Where communities fit in a complete monitoring model
Where communities fit in a complete monitoring model
“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
How did they do it?
Web Interaction Analytics
http://www.flickr.com/photos/trekkyandy/189717616/
Yes
Perceptual information
                         (did I see it?)




                  No
                                           No               Affordance                  Yes
                                                (was I supposed to interact with it?)         Adapted from Gaver (1991)
Yes

                                                   False
Perceptual information


                                                affordance
                         (did I see it?)




                  No
                                           No               Affordance                  Yes
                                                (was I supposed to interact with it?)         Adapted from Gaver (1991)
Yes

                                                                           Seen
                                                   False
                                                                       (perceptible)
Perceptual information


                                                affordance
                                                                        affordance
                         (did I see it?)




                  No
                                           No               Affordance                  Yes
                                                (was I supposed to interact with it?)         Adapted from Gaver (1991)
Yes

                                                                           Seen
                                                   False
                                                                       (perceptible)
Perceptual information


                                                affordance
                                                                        affordance
                         (did I see it?)




                                                  Correct
                                                 rejection


                  No
                                           No                Affordance                 Yes
                                                (was I supposed to interact with it?)         Adapted from Gaver (1991)
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)
http://www.flickr.com/photos/americanlady/3118301118




consume

 http://

give data

navigate
Usability issue 1:
Visitors don’t see what you
      wanted them to.
Where communities fit in a complete monitoring model
Where communities fit in a complete monitoring model
Where communities fit in a complete monitoring model
Your mileage will vary.
Where communities fit in a complete monitoring model
Usability issue 2:
Visitors don’t interact as you
          intended.
Where communities fit in a complete monitoring model
Usability issue 3:
Visitors don’t input data
Where communities fit in a complete monitoring model
Where communities fit in a complete monitoring model
“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
Voice of the customer
Why did they do it?
Where communities fit in a complete monitoring model
Where communities fit in a complete monitoring model
Where communities fit in a complete monitoring model
Where communities fit in a complete monitoring model
People on the internet do
      weird things
Where communities fit in a complete monitoring model
Where communities fit in a complete monitoring model
So what’s this “VOC” thing?

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

  Analytics         Usability
(what did they   (how did they
  do on the       interact with
    site?)             it?)

         Complete Web Monitoring
     VoC         Communilytics     Competition
 (what were       (what were      (what are they
    their        they saying?)       up to?)
motivations?)

                  “Soft” data
“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
Could they do it?
Performability
Where communities fit in a complete monitoring model
Where communities fit in a complete monitoring model
Where communities fit in a complete monitoring model
Where communities fit in a complete monitoring model
Where communities fit in a complete monitoring model
Where communities fit in a complete monitoring model
Where communities fit in a complete monitoring model
Where communities fit in a complete monitoring model
Where communities fit in a complete monitoring model
Where communities fit in a complete monitoring model
Where communities fit in a complete monitoring model
Websites
 have a dirty
 little secret


http://todaystatus.files.wordpress.com/2009/04/ww11-secret.jpg
Where communities fit in a complete monitoring model
Where communities fit in a complete monitoring model
Where communities fit in a complete monitoring model
Where communities fit in a complete monitoring model
http://www.inquisitr.com/2097/site-meter-causing-internet-explorer-failure/
Everything is interwoven.
Conversion rate
                                 20
                                      and order value
Difference due to optimization




                                 15



                                 10
                                        16.07
                                 5

                                                          5.51
                                 0
                                       Conversion rate   Order value
http://www.flickr.com/photos/spunter/393793587   http://www.flickr.com/photos/laurenclose/2217307446




           KPIs
See Strangeloop deck at
http://bit.ly/cwm-perfstudy
Putting it all together
Big picture analytics
Where communities fit in a complete monitoring model

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Where communities fit in a complete monitoring model

  • 1. Where communities fit in & the story of putting numbers on things
  • 2. Everybody has goals. http://www.flickr.com/photos/itsgreg/446061432/
  • 5. 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
  • 8. 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
  • 10. 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
  • 12. $ Media site Enrolment Targeted 2 embedded ad 5 $ 6 1 Ad Visitor network 4 3 5 Advertiser $ Departure $ site Impact on site $ Positive $ Negative
  • 13. Analytics is the measurement of movement towards those goals. http://www.flickr.com/photos/itsgreg/446061432/
  • 15. ATTENTION SEARCHES TWEETS MENTIONS ADS SEEN
  • 16. ATTENTION SEARCHES TWEETS NUMBER OF VISITS MENTIONS ADS SEEN
  • 17. ATTENTION SEARCHES TWEETS NUMBER OF VISITS MENTIONS ADS SEEN LOSS BOUNCE RATE
  • 18. ATTENTION NEW VISITORS SEARCHES GROWTH TWEETS NUMBER OF VISITS MENTIONS ADS SEEN LOSS BOUNCE RATE
  • 19. ATTENTION ENGAGEMENT NEW VISITORS SEARCHES GROWTH PAGES TWEETS NUMBER PER OF VISITS MENTIONS VISIT ADS SEEN LOSS BOUNCE RATE
  • 20. ATTENTION ENGAGEMENT NEW VISITORS SEARCHES GROWTH PAGES TIME TWEETS NUMBER PER ON OF VISITS MENTIONS VISIT SITE ADS SEEN LOSS BOUNCE RATE
  • 21. 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
  • 23. “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
  • 24. “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
  • 25. “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
  • 27. These people drive nicer cars than us. :/ Source: http://www.webanalyticsdemystified.com/sample/Web_Analytics_Demystified_RESEARCH_-_March_2007_-_Salary_Survey.pdf
  • 28. Hits
  • 30. Hits Pages
  • 33. Hits Pages Sessions
  • 36. Hits Pages Sessions Visitors
  • 38. Hits Pages Sessions Visitors Segments
  • 40. e ar e ts es en Th gm se
  • 41. (You can make your own.)
  • 47. Who would you rather have sending a message?
  • 48. Who would you rather have sending a message?
  • 49. Who would you rather have sending a message?
  • 50. Old analytics: report the news http://www.flickr.com/photos/thomasclaveirole/538819881/
  • 52. Old analytics: New analytics: report the news optimize goals http://www.flickr.com/photos/thomasclaveirole/538819881/ http://www.flickr.com/photos/sanchom/2963072255/
  • 53. 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.
  • 54. Find the site The three stages of a Use the site unique visit Leave the site
  • 55. Find the site: How did they get there?
  • 65. Use the site: What did they do?
  • 69. Landing page: View one story Task: Log in Enter credentials Verify Recovery
  • 70. Landing page: View one story Task: Log in Enter credentials Verify Recovery Task: Forward a story Enter recipients Enter message Send
  • 71. Landing page: Task: View one story Create account Task: Log in Pick name Check if free Enter credentials Set Password Verify CAPTCHA Recovery Send mail Get confirm Task: Forward a story Enter recipients Enter message Send
  • 72. Landing page: Task: View one story Create account Task: Log in Pick name Check if free Enter credentials Set Password Verify CAPTCHA Recovery Send mail Get confirm Task: Forward a story Task: Submit Enter recipients a new story Enter message Send Enter URL Describe Deduplicate Post it
  • 73. 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 Get confirm Task: Forward a story Task: Submit Enter recipients a new story Enter message Send Enter URL Describe Deduplicate Post it
  • 74. 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 Enter message Send Enter URL Describe Deduplicate Post it
  • 75. 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
  • 76. Landing page: Create acct. View one story Task: Log in Place: View stories Place: Read poster comments Task: Forward a story Task: Submit a new story Place: My account
  • 77. Landing page: Create acct. Create acct. View one story Form uptime Place: View stories Task: Log in # started Bad form Place: Read # CAPTCHA poster comments Mail uptime Task: Forward a story Mail bounced Task: Submit a new story Place: My Confirm & return account Return 3x
  • 78. Landing page: Create acct. View one story Task: Log in Place: View stories Place: View stories Stories/visit Place: Read # up/down poster comments Time/story Top stories Task: Forward a story Task: Submit Refresh time Views/page a new story Place: My account
  • 79. Landing page: Create acct. View one story Task: Log in Place: View stories Place: Read poster comments Task: Forward a story Task: Submit a new story Place: My account
  • 80. 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
  • 81. 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
  • 88. Leave the site: Parting is such sweet sorrow
  • 90. Pages per visit Time on site 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 26,000 5 19,500 3.75 13,000 2.5 6,500 1.25 0 0 September October September October
  • 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
  • 94. “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
  • 95. How did they do it? Web Interaction Analytics
  • 97. Yes Perceptual information (did I see it?) No No Affordance Yes (was I supposed to interact with it?) Adapted from Gaver (1991)
  • 98. Yes False Perceptual information affordance (did I see it?) No No Affordance Yes (was I supposed to interact with it?) Adapted from Gaver (1991)
  • 99. Yes Seen False (perceptible) Perceptual information affordance affordance (did I see it?) No No Affordance Yes (was I supposed to interact with it?) Adapted from Gaver (1991)
  • 100. Yes Seen False (perceptible) Perceptual information affordance affordance (did I see it?) Correct rejection No No Affordance Yes (was I supposed to interact with it?) Adapted from Gaver (1991)
  • 101. 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)
  • 103. Usability issue 1: Visitors don’t see what you wanted them to.
  • 109. Usability issue 2: Visitors don’t interact as you intended.
  • 111. Usability issue 3: Visitors don’t input data
  • 114. “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
  • 115. Voice of the customer Why did they do it?
  • 120. People on the internet do weird things
  • 123. So what’s this “VOC” thing? Get new ideas Evaluate things you can’t collect in other ways Evaluate sentiment Collect demographics data
  • 127. “Hard” data Analytics Usability (what did they (how did they do on the interact with site?) it?) Complete Web Monitoring VoC Communilytics Competition (what were (what were (what are they their they saying?) up to?) motivations?) “Soft” data
  • 128. “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
  • 129. Could they do it? Performability
  • 141. Websites have a dirty little secret http://todaystatus.files.wordpress.com/2009/04/ww11-secret.jpg
  • 148. Conversion rate 20 and order value Difference due to optimization 15 10 16.07 5 5.51 0 Conversion rate Order value
  • 149. http://www.flickr.com/photos/spunter/393793587 http://www.flickr.com/photos/laurenclose/2217307446 KPIs
  • 150. See Strangeloop deck at http://bit.ly/cwm-perfstudy
  • 151. Putting it all together Big picture analytics

Notes de l'éditeur

  1. Every business has a goal hidden inside it.
  2. Amazon: what do they want you to do?
  3. Maximize your shopping cart size
  4. They’re a transactional site. They make money when people complete a process, usually involving a purchase or subscription.
  5. But Amazon also wants you to leave reviews
  6. And add something to a wishlist
  7. These are forms of collaboration, where communities create content.
  8. What about another kind of site. What does gmail want you to do?
  9. GMail is first and foremost a SaaS site. It wants you to be productive, so you can get work done and keep using the system. A paid SaaS site is the same thing.
  10. Of course, GMail is also another kind of site -- a media site. That’s an ad up there.
  11. Media sites want you to click on targeted advertising.
  12. Analytics is about measuring.
  13. Here’s the simplest possible analytics model.
  14. Here’s the simplest possible analytics model.
  15. Here’s the simplest possible analytics model.
  16. Here’s the simplest possible analytics model.
  17. Here’s the simplest possible analytics model.
  18. Here’s the simplest possible analytics model.
  19. Here’s the simplest possible analytics model.
  20. Here’s the simplest possible analytics model.
  21. Here’s the simplest possible analytics model.
  22. Here’s the simplest possible analytics model.
  23. Here’s the simplest possible analytics model.
  24. The reality, of course, is that lots of things influence whether you’ll move towards the ultimate goal
  25. In addition to communities and competitors, there are lots of factors -- what people did, how they did it, whether they could do it, and why they did it. We’ve already looked the first two; now, let’s look at the rest.
  26. We’re going to start with analytics, because it’s the “R” in “ROI”. Everything else you do -- blogging, making offers, buying new machines -- is an investment. But the return is the part that keeps score.
  27. •Web performance and availability ensures that visitors can do what they want to, when they want to •Surveys ensure that you understand visitors’ needs and hear their voice •Usability and interaction analysis measures how easily visitors can achieve their goals •Community monitoring links what visitors do elsewhere to your site and brand. If you’re not making decisions about your web presence based on what your analytics tells you, you’re making bad decisions. If you’re not augmenting analytics with other data, you’re making decisions without all the facts.
  28. We’re not the only ones to think that this is the case. The demand for web analysts is so high that a person with less than 12 months of experience can expect to make, on average, about 80,000 USD a year in the United States.
  29. Any server request is considered a hit. For example: When a visitor calls up a web page with six images, the browser generates seven hits—one for the page, and six for the images. Prior to sophisticated web analytics, this legacy term was used to measure the interaction of the user with the website. Today, Hits might be a useful indicator of server load, but are not considered useful for understanding visitor behavior.
  30. things evolved. 1) javascript came into the picture 2) web log analyzers started distinguishing .html now we had pages but we didn’t have the outcome of the visits
  31. if a person looked at shoes, and then took a look at shirts, the visits weren’t related at all. This made amazon’s current structure impossible.
  32. We didn’t just want to know page views. Was that one person viewing a hundred pages, or a hundred people viewing one? Were two people buying clothes, or one person stocking their wardrobe?
  33. by setting a cookie with an arbitrary string value, you can now relate pages together as someone navigates through a site
  34. But we wanted more. We wanted to identify visitors -- so we could tell if people were coming back, and understand patterns of use over time.
  35. Using logins, and tracking returning visitors across sessions, we could measure the actual people visiting a site.
  36. Finally, segments. We wanted to break visitors down into groups -- based on where they came from, what they did, and so on.
  37. Modern analytics tools divide up traffic by keywords, sources, and more -- so you can see which groups do more of what you like. This is the basis for an important step forward in analytics: optimization
  38. These are URI query parameters.
  39. Before marketers got involved, analytics was about reporting what had happened on a site.
  40. When marketing got involved, this changed.
  41. Marketers used analytics for optimization, which tests out different ideas (such as pricing options) to see what works best. This suddenly made it interesting: It wasn’t just about reporting costs and usage, but about improving the business.
  42. terms change from hits to: unique visitor, referrers, landing pages, bounce rates, transactions, conversions, abandonment, segmentation, split testing, optimization, experimentation.
  43. Let’s talk a bit about what people do on a site. All visits consist of three steps: finding, using, and leaving.
  44. (finding the website)
  45. Organic search happens when a user searches for something on the web and clicks on a link that brings them to your site Ensuring that site content is ranked well by search engines, and that the right organic search terms send traffic to the site, is one of the main jobs of online marketers. Web analytics helps you to understand whether your site is properly optimized and which keywords are driving traffic through reports like the one in Figure 5-22.
  46. Paid search or ads is what happens when you click on an advertisement or sponsorship banner If you’re not getting the attention you want organically, you can always pay for it. Three kind of ads make up the bulk of online advertising: •Pay-per-view, in which advertiser pay media sites for the number of times they show an ad. •Pay-per-click, in which the media site gets paid each time a visitor clicks on the advertiser’s message. This is how paid search ads work. •Pay-per-acquisition, in which the media site is compensated each time someone they send to an advertiser completes a transaction. Affiliate marketing is an example of a pay-per-acquisition model.
  47. Affiliate marketing and paid search on book sharing site LibraryThing
  48. Three linkbacks to an article on chrisbrogan.com
  49. If they get referred, you won’t know why.
  50. If they get referred, you won’t know why.
  51. If they get referred, you won’t know why.
  52. If they get referred, you won’t know why.
  53. Type-in traffic: The opposite of navigational search is when a user types a word—such as “pizza”—into a search engine to see what will happen. Depending on the browser or search engine, this will often take them to a named site such as “www.pizza.com” that makes its money from referring visitors to other sites. Type-in traffic is why popular type-in domains are sometimes sold for millions of dollars. •Bookmarking: Users who have visited a site may bookmark it, or the browser may display the most frequently visited sites when it is first launched. Clicking on a bookmark or a “frequently visited”link or a link in the browser creates a visit with no referring site. The visit is classified as direct traffic by the analytics engine. •Desktop client: Some desktop tools, such as Twitter clients, don’t provide any information about a followed link—they simply launch a web browser with a URL that the user received through the desktop tool. As a result, these links are classified as direct traffic even though they were referred through a community. •Javascript redirection: If a user follows a link to your site that was triggered by Javascript, the browser will not pass on referring information and the visit will be mislabeled. There are ways to fix this, but not everyone follows them. •Browser inconsistency: Browser behavior can reduce the accuracy of direct visitor count. For example, in certain conditions Microsoft’s Internet Explorer 7 will not pass along referring information if you load a website in another window or tab. •Bots, spiders, and probes: Scripts and automated visitors, such as search engines indexing your website, may not be properly identified and may be counted as direct visits.
  54. Once you’ve identified visits that came from organic and paid search, advertising campaigns, affiliate referrals, and linkbacks, you’re left with direct traffic. Much of this comes from word-of-mouth mentions and social networks—after all, visitors had to hear about you somewhere. But you can’t tell where their first impressions came from, because their visit is the result of several influences you weren’t monitoring. The conversion funnels we’ve seen so far begin when a visitor first arrives at your site. This isn’t an accurate model for the modern web. Social networks and online communities mean conversion starts long before a visitor reaches your site. Integrating all of these into your understanding of the conversion process is a major challenge for web analysts. All of these traffic sources represent your “long funnel”—the steps your visitors undergo from initial awareness to conversion—which looks something like the illustration in Figure 5-27.
  55. You track what your visitors do so that you can identify patterns and improve the site in ways that increase the outcomes you want (such as inviting friends, creating content, or subscription renewals) while reducing those you don’t (such as departures or calls to customer support.)
  56. Where did they come in? The first page in a visit is particularly important. Different visits have different first pages, and you can segment outcomes based on the pages on which they land. You may even have different landing pages for different traffic sources, which lets you tailor your site’s message to different segments—for example, a visitor from Reddit might see a message or layout that’s designed to appeal to him, while a visitor from Twitter might see a different layout entirely.
  57. Here’s an attempt to summarize the “places” and “tasks” for a social news aggregator like Reddit. You can do specific things on the site; or you can undertake “tasks”
  58. Here’s an attempt to summarize the “places” and “tasks” for a social news aggregator like Reddit. You can do specific things on the site; or you can undertake “tasks”
  59. Here’s an attempt to summarize the “places” and “tasks” for a social news aggregator like Reddit. You can do specific things on the site; or you can undertake “tasks”
  60. Here’s an attempt to summarize the “places” and “tasks” for a social news aggregator like Reddit. You can do specific things on the site; or you can undertake “tasks”
  61. Here’s an attempt to summarize the “places” and “tasks” for a social news aggregator like Reddit. You can do specific things on the site; or you can undertake “tasks”
  62. Here’s an attempt to summarize the “places” and “tasks” for a social news aggregator like Reddit. You can do specific things on the site; or you can undertake “tasks”
  63. Here’s an attempt to summarize the “places” and “tasks” for a social news aggregator like Reddit. You can do specific things on the site; or you can undertake “tasks”
  64. Here’s an attempt to summarize the “places” and “tasks” for a social news aggregator like Reddit. You can do specific things on the site; or you can undertake “tasks”
  65. For a task or a place, there may be things you want to track. <click> For the “create account” task, you have a bunch of metrics that matter - including things like whether your mail server is working, or whether the person who signed up returns. Similarly, for a “place” you care about a different set of metrics.
  66. For a task or a place, there may be things you want to track. <click> For the “create account” task, you have a bunch of metrics that matter - including things like whether your mail server is working, or whether the person who signed up returns. Similarly, for a “place” you care about a different set of metrics.
  67. For a task or a place, there may be things you want to track. <click> For the “create account” task, you have a bunch of metrics that matter - including things like whether your mail server is working, or whether the person who signed up returns. Similarly, for a “place” you care about a different set of metrics.
  68. For a task or a place, there may be things you want to track. <click> For the “create account” task, you have a bunch of metrics that matter - including things like whether your mail server is working, or whether the person who signed up returns. Similarly, for a “place” you care about a different set of metrics.
  69. A place is where visitors hang out. You care about how efficient they are using this place. It could be things like reviewing orders, or retrieving a customer’s account info.
  70. A task happens when a visitor has a specific mission to accomplish. Here, you care about whether they were successful, and if not, why they left.
  71. The ultimate KPI is the goal, or outcome, of a visit. Within your analytics package, you identify the outcomes you want—such as a purchase confirmation page or an enrolment screen—as well as any steps leading up to that outcome. This is often visualized using a funnel graph (Figure 5-31).
  72. Funnels work well, but they’re myopic. They make it hard to identify where users are going, and don’t take into account re-entry into the process. They also focus on web activity alone, while many websites have goals that include emails, subscriptions, and other steps that can’t easily be captured by simple web requests. As web analytics tools adapt to today’s more distributed conversion processes and usage patterns, we’ll likely see place-and-task models that track KPIs for each step in a process. KISSMetrics’ ProductPlanner (seen in Figure 5-32) is a great resource for knowing what metrics to capture for many popular web design patterns.
  73. 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.
  74. Goals and outcomes are essential: If you don’t have a goal in mind, you can’t optimize your site to encourage behaviors that lead to that goal. We typically express the steps a visitor takes towards a goal with a “funnel”, showing how many visitors proceeded through several steps and how many abandoned the process.
  75. Originally had used various permutations of “Free Trial” and “Sign-up for Free Trial”. Then they tested the phrase“See Plans and Pricing” People are afraid that if they click a link that says “Free Trial” they’ll somehow automatically signup for something and be trapped. However, “See Plans and Pricing” encouraged them to explore, without the fear of commitment. Result? 200% increase in conversions.
  76. Web analytics is about measuring this stuff It’s about adjusting what you do to get the better results (like time spent on page, number of pageviews)
  77. Abandonment happens when people leave your site before doing something you wanted them to. They may simply leave out of boredom; because of an error or performance issue; or because they changed their mind about completing a particular task.
  78. Unlike bounces and exits, which happen at the end of a visit, attrition happens when your relationship with a visitor grows stale. There are many ways to measure attrition. You might look at the percentage of returning visitors, but if your site is acquiring new visitors a declining percentage of returning visitors might simply indicate rapid growth. A better measure of attrition is the number of users that haven’t returned in a given period of time. By comparing two time periods’ attrition as shown in Figure 5-34, you can tell whether things are getting better or worse, and whether you’re successfully retaining visitor attention. Many community sites celebrate the number of visitors they have without considering the engagement of those visitors. Any site that depends on user activity and returning visitors must look at attrition carefully and make concerted efforts to re-engage users who haven’t visited the site in a long time.
  79. Unlike bounces and exits, which happen at the end of a visit, attrition happens when your relationship with a visitor grows stale. There are many ways to measure attrition. You might look at the percentage of returning visitors, but if your site is acquiring new visitors a declining percentage of returning visitors might simply indicate rapid growth. A better measure of attrition is the number of users that haven’t returned in a given period of time. By comparing two time periods’ attrition as shown in Figure 5-34, you can tell whether things are getting better or worse, and whether you’re successfully retaining visitor attention. Many community sites celebrate the number of visitors they have without considering the engagement of those visitors. Any site that depends on user activity and returning visitors must look at attrition carefully and make concerted efforts to re-engage users who haven’t visited the site in a long time.
  80. Unlike bounces and exits, which happen at the end of a visit, attrition happens when your relationship with a visitor grows stale. There are many ways to measure attrition. You might look at the percentage of returning visitors, but if your site is acquiring new visitors a declining percentage of returning visitors might simply indicate rapid growth. A better measure of attrition is the number of users that haven’t returned in a given period of time. By comparing two time periods’ attrition as shown in Figure 5-34, you can tell whether things are getting better or worse, and whether you’re successfully retaining visitor attention. Many community sites celebrate the number of visitors they have without considering the engagement of those visitors. Any site that depends on user activity and returning visitors must look at attrition carefully and make concerted efforts to re-engage users who haven’t visited the site in a long time.
  81. Unlike bounces and exits, which happen at the end of a visit, attrition happens when your relationship with a visitor grows stale. There are many ways to measure attrition. You might look at the percentage of returning visitors, but if your site is acquiring new visitors a declining percentage of returning visitors might simply indicate rapid growth. A better measure of attrition is the number of users that haven’t returned in a given period of time. By comparing two time periods’ attrition as shown in Figure 5-34, you can tell whether things are getting better or worse, and whether you’re successfully retaining visitor attention. Many community sites celebrate the number of visitors they have without considering the engagement of those visitors. Any site that depends on user activity and returning visitors must look at attrition carefully and make concerted efforts to re-engage users who haven’t visited the site in a long time.
  82. Some exits are awesome, of course -- the ones that make you money when you’re a media site.
  83. Okay. So that was analytics. What about the other things -- such as usability?
  84. It’s one thing to know what people did on your site. But often you want to know how they did it -- did they click on the red button or the blue text? Did they scroll all the way down?
  85. Designers call things like buttons and doorknobs “affordances.” They worry about things like whether the user perceived the affordance, and whether it was in fact intended as one.
  86. Designers call things like buttons and doorknobs “affordances.” They worry about things like whether the user perceived the affordance, and whether it was in fact intended as one.
  87. Designers call things like buttons and doorknobs “affordances.” They worry about things like whether the user perceived the affordance, and whether it was in fact intended as one.
  88. Designers call things like buttons and doorknobs “affordances.” They worry about things like whether the user perceived the affordance, and whether it was in fact intended as one.
  89. This is where web monitoring comes in. On any given page, visitors can perform four basic actions: •They can consume what’s shown, which may involve scrolling, changing the browser’s size, starting and stopping embedded media, or changing font size. •They can follow a link contained in the HTML to continue their navigation, either within your site or elsewhere. •They can provide data, typically through a form, using controls such as a radio button, checkboxes, text field, or dropdown listboxes. •They can use their mouse and keyboard to interact with elements of the page, which won’t necessarily result in any server actions.
  90. Content is displayed off the visible screen, and visitors don’t scroll down to view it.
  91. only 22% of your users will ever scroll down to the bottom of a page.
  92. the same number of page viewers will tend to scroll halfway or three-quarters through a page, regardless of whether the page size is 5,000 pixels or 10,000 pixels
  93. Visitors’ Attention follows a similar pattern for pages of different heights. It peaks both near the page top, at 540 pixels, and near the bottom, about 500 pixels from the end of the page. Excluding behavior effects at the page top and bottom, attention decreases exponentially as visitors scroll down the page.
  94. the survey doesn’t take into account the four types of sites, and all are unique.
  95. WIA tools help you measure this by giving you a toolset to see how people are using your site.
  96. They don’t notice elements designed for interaction, such as buttons or links; or they try to interact with things that aren’t part of the design you intended.
  97. for example, the “Xiti Pro” and “Xiti Free” links aren’t actually URLs. They’re text that people mistake for hyperlinks.
  98. They put the wrong information into a form, linger a long time on a particular form element, or abandon form completion halfway through.
  99. In recent years, analytics tools have started to look within a single page, to the form elements on that page, in an effort to identify which components of a page drove visitors away. Consider, for example, a form that asks for personal information such as age. If you analyze the abandoned page at the page level, you won’t know that it’s a specific form element that’s the problem. But if you use Web Interaction Analytics tools to do form-level analytics, you’ll have a much better understanding of how visitors are interacting with the site, as shown in Figure 5-33.
  100. You can drill down to the individual form components
  101. Here are some of them. They’ve been doing this much longer than you have.
  102. THIS IS NOT SOCIAL MEDIA SENTIMENT
  103. Companies like Expedia, Travelocity, and Priceline had problems with abandonment. Visitors would search for a hotel, find one they liked, check rates and availability—and then leave. The sites tried offering discounts, changing layouts, modifying the text, and more. Nothing.
  104. “Why did you come to the site?” Visitors weren’t planning on booking a room, only checking availability. The reason they thought visitors were coming to their site was wrong. The site’s operators had a different set of goals in mind than visitors did, and the symptom of this disconnect was the late abandonment.
  105. With this new-found understanding of visitor motivations, travel sites took two important steps. First, they changed the pages of their sites, offering to watch a particular search for thecustomer and tell them when a deal came along, as shown in Figure 7-1. Second, they moved the purchasing or bidding to the front of the process, forcing the buyer to commit to payment or to name their price before they found out which hotel they’d booked. This prevented window-shopping for a brand while allowing them to charge discounted rates. The results were tremendous, and changed how online hotel bookings happen. Today, most travel sites let users watch specific bookings, and many offer deeper discounts than the hotel chains themselves if customers are willing to commit to a purchase before they find out the brand of the hotel.
  106. The lesson here is that your visitors probably aren’t doing what you think they are. While sometimes—as in the travel agency model—they’re still doing something related to your business, there are other times when their reasons for visiting are entirely alien.
  107. PMOG and Webwars. In these games, players install browser plug-ins that let them view websites in different ways than those intended by the site operator. In PMOG, a user can plant traps on your website that other players might trigger, or leave caches of game inventory for teammates to collect.
  108. Other “overlays” to the web let people comment on a site using plug-ins like firef.ly—shown in Figure 7-3—or use site content for address books and phone directories as Skype does.
  109. At its simplest, VOC is just a fancy word for surveys that solicit feedback about your site or your organization. The invitation may come from a pop-up message when they first visit the site, or from a feedback button on a page. It may even come from an email sent to customers. The survey uses a variety of questions and formats to gauge how respondents feel about things. It also collects data on the respondents so that analysts can correlate the responses to specific groups. •Your customers may have motivations or concerns you’re not aware of, and asking them can yield new ideas. Once you have an idea, you need to then find out whether it is valid and applies to a broader audience, or is limited to just a few respondents. •To evaluate things you can’t find out elsewhere—particularly your competitive environment. For example, if you’re running a media site, you may want to identify direct competitors (other media sites, for example) and indirect competitors (television or movie theatres.) •To see whether improvements worked. This may be a simple evaluation—asking for a user’s impression of a new feature—or it may involve comparing satisfaction scores before and after an upgrade to see whether users prefer the new approach. •To collect demographic data such as age and income that you can’t get elsewhere. This information provides new dimensions along which to segment visitors and learn for whom your site is working best or worst. If you’re a media site, you’ll also need independently verified demographic data in order to attract advertisers.
  110. Perhaps the biggest criticism leveled at visitor surveys is their sampling bias. It’s true that only a certain kind of visitor will respond to a survey, however good the invitation. While larger samples can mitigate sampling error, the answers you get still won’t be representative of your user base. You’re less likely to get responses from “power users” who are in a hurry, and even then, they’re probably only going to offer feedback in certain situations. And you’re more likely to hear from zealots and outliers. This criticism misses the point: VOC should capture insights that you may be able to investigate. In the travel site case mentioned above, a few responses saying that visitors were just checking availability prompted the site operators to research further, and confirm that this was in fact the case for many of their customers. Then, through analytics and experimentation, they were able to adapt their sites.
  111. The best way to understand the needs, emotions, and aspirations of your target market is to visit it where it lives—in Facebook groups, chat rooms, Twitter feeds, news aggregators, and blog comment threads. You can use VOC to find out where your market hangs out, or to dig deeper into something you hear online, but you need to marinate in your community to really understand them.
  112. If you need to constantly poll your market to understand its needs, then you should convince visitors to let you contact them through email or RSS feeds. In this way, you can go back to them several times with additional questions and build your own panel of respondents. Remember, however, that enrolled respondents—and friends you interact with on social networks—are more loyal and “tainted” with opinions. After all, they liked you enough to enroll. So while it’s good to survey them, you still need to examine newcomers. In other words, VOC works best on visitors who you don’t yet know, and who are new to the site, as soon as they’ve formed an opinion of you. Ask them too soon, and they won’t have visited your site; ask them too late, and they may have left forever.
  113. One more to look at: performability. While you might thing performance and availability doesn’t affect you, you should think again. Recent research shows that the latency of a web page can change business outcomes dramatically.
  114. AC START
  115. Sites still fail in lots of ways. It’s scary how much things break. This is just a sample of pages for Canadians...
  116. Sites still fail in lots of ways. It’s scary how much things break. This is just a sample of pages for Canadians...
  117. Sites still fail in lots of ways. It’s scary how much things break. This is just a sample of pages for Canadians...
  118. Sites still fail in lots of ways. It’s scary how much things break. This is just a sample of pages for Canadians...
  119. Sites still fail in lots of ways. It’s scary how much things break. This is just a sample of pages for Canadians...
  120. Sites still fail in lots of ways. It’s scary how much things break. This is just a sample of pages for Canadians...
  121. Sites still fail in lots of ways. It’s scary how much things break. This is just a sample of pages for Canadians...
  122. Sites still fail in lots of ways. It’s scary how much things break. This is just a sample of pages for Canadians...
  123. Sites still fail in lots of ways. It’s scary how much things break. This is just a sample of pages for Canadians...
  124. Sites still fail in lots of ways. It’s scary how much things break. This is just a sample of pages for Canadians...
  125. All of this analytics is good. But it’s only half of the job of web monitoring. Because try as you might, websites have a problem.
  126. for example . . . imagine that you decided to launch a kick ass survey. you’ve bought the latest shiny tool you’ve carefully crafted the questions you hired outside help to make sure they’re worded properly you had them sent to a professional copy editor to get the final tone just right it went through legal you segmented your campaign according to the demographic whose voice you need to understand the most and as you sit precariously over the big red send button you can’t help but feel that you’ve covered all your bases. Satisfied, you press the button and out it goes into the world.
  127. that was the case with paypal, recently. We don’t have insight into their numbers, so we can’t tell for sure what the particular conversion rate for this survey was, but we suspect that the pickup wasn’t as good as anticipated. Their web analytics and VOC don’t have the necessary functions built in to determine that their SSL cert was mismatched, cause safari and other browsers to come up with a nasty message saying “we can’t verify the identity of paypal-surveys.com”. After all, think about it; if it’s coming from paypal and the identity can’t be verified, would you go on the site and fill anything out?
  128. we know of a case of a marketing officer who’se job was put in question because of a string of failed campaigns.The company jumped the gun on this one. Thanks to a friend in the web operations department, he was able to show that the network was at fault. Even though the company load tested diligently, they only did from their internal network. It turns out the problems were related to the last mile - something that was hidden until the company implemented synthetic monitoring. Even though overall sentiment was a little more negative than usual during the campaigns, the conversion rates skyrocketed once better transit was installed.
  129. This is a scary one and a true. If you haven’t heard, sitemeter took down every single website that were a client of theirs. If you were on IE and wanted to access sites like TechCrunch, Gizmodo and so on, you were out of luck in August, because the code crashed the browser. Think about it - your site isn’t just vulnerable to whatever goofy code your development team throws at the Internet, it’s also vulnerable to your very own web analytics tracking codes! This would take hours of troubleshooting to reveal without synthetic monitoring - or one simply alert would be triggered with the proper tools in place. I don’t mean to pick on SiteMeter btw, I’m sure they have a great service - but these types of errors can kill substantial amounts of revenue until you catch it.
  130. By now, we know that everything matters. Usability, page latency, visitor mindset, and even sentiment on social media platforms all contribute to the business results you get from a site.
  131. On a second e-commerce site running roughly the same experiment, conversions were 16 percent higher and orders were 5.5% higher.
  132. By tying performance and availability to Key Performance Indicators – KPIs – business and operations can finally have a conversation. But KPIs are different for different sites.
  133. So if your brain’s full, don’t be surprised. There’s a huge amount of data to consolidate.