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You toolbox for eBusiness
    success - Web Analytics
             Congress 2010


w/Sean Power & Jeroen Tjepkema
01 traditional analytics
I betook
myself to
linking...
...tis some
visitor
entreating
entrance at
my chamber
door
01 traditional analytics
01 traditional analytics
01 traditional analytics
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
Market research (what people think and why)
Support
Other
What we’ll cover

 Analytics, interaction, UX, Voice of the Customer
 EUEM, synthetic tests, RUM
 Online communities, internal communities
 Competitive analysis
 Integrating data sources
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     Launch
Known ways to                  Known set of
 satisfy them                  requirements



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



 Problem
statement     Build    Test    Viable?     Launch


                  Sprints

                      Adjust
Unclear how to                           Unknown set
 satisfy them                           of requirements



 Problem
statement    Build     Test   Viable?       Launch


        Iterations & pivots

  Redefine problem, business
Most new startups
don’t know even know what problem they solve.
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
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 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
!"#                    !"#$"%&'()*!+',-(,(
                                                     !"#                                !"
                                                                                                                            !"#$%&'
                           !"#$%&'
                          ()*+",-.
                                                         !"#$%                                        !"#$%&'(

                  !""#$%$&'()*+#                                                                                        !"#$%&'()$*'()+
                                                                      !"#$%

                                                                                                                                                              !"#$"%&'()*!+',-(,(
                                            !"#$%&'                             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
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: They stay for longer
Loyalty: They come back
Contribution: They add content
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
How much did visitors
benefit my business?
Internal analytics
 Conversion and           Billing and account use
 abandonment
 Click-throughs
 Offline activity
 User-generated content
 Subscriptions
Where’s my traffic coming
from?
External analytics
 Referring websites
 Inbound links from social networks
 Visitor motivation
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
How good is my relationship
with my market?
Customer surveys, community monitoring
 Loyalty
 Enrollment
 Reach and rewards
01 traditional analytics
How healthy is my
infrastructure?
Performance monitoring
 Availability and           Impact of performance
 performance                on outcomes
 SLA compliance
 Content delivery
 Capacity and flash traffic
How am I doing against my
competitors?
Performance monitoring
 Site popularity and ranking
 How are people finding my competitors?
 Relative site performance
 Competitor activity
Where are my risks?
Search, alerting
 Trolling and spamming
 Copyright and legal liability
 Fraud, privacy, and account sharing
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 illegal syndication
 Integration with legacy systems
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          8          9        10


           http://www.rvca.com/anp/wp-content/plugins/wp-o-matic/cache/57226_07+proof+1a+hb+beach+day.jpg
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 cream consumption          Drownings   Temperature
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/
01 traditional analytics
01 traditional analytics
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
01 traditional analytics
01 traditional analytics
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
01 traditional analytics
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
01 traditional analytics
$



                                     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/
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
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
01 traditional analytics
Hits   Pages   Sessions
01 traditional analytics
01 traditional analytics
Hits   Pages   Sessions   Visitors
01 traditional analytics
Hits   Pages   Sessions   Visitors   Segments
e
      ar
     e ts
  es en
Th gm
  se
(You can make your own.)
01 traditional analytics
01 traditional analytics
http://www.human20.com/who-
owns-your-voice-online/
?utm_source=abowyer
&utm_medium=twitter
&utm_content=communication
&utm_campaign=post
01 traditional analytics
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?
01 traditional analytics
01 traditional analytics
01 traditional analytics
01 traditional analytics
01 traditional analytics
“Direct” traffic isn’t.

 Type-In Traffic
 Bookmarking
 JavaScript redirect
 Browser Inconsistencies
 Bots, Spiders and Probes
01 traditional analytics
source: the conversation prism by Brian Solis and JESS3
                  http://www.theconversationprism.com
Use the site:
What did they do?
01 traditional analytics
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.
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
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
01 traditional analytics
01 traditional analytics
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
01 traditional analytics
01 traditional analytics
01 traditional analytics
Leave the site:
Parting is such sweet sorrow
01 traditional analytics
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
01 traditional analytics
“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

                                                                           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.
01 traditional analytics
01 traditional analytics
01 traditional analytics
Your mileage will vary.
01 traditional analytics
Usability issue 2:
Visitors don’t interact as you
          intended.
01 traditional analytics
Usability issue 3:
Visitors don’t input data
01 traditional analytics
01 traditional analytics
“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?
01 traditional analytics
01 traditional analytics
01 traditional analytics
01 traditional analytics
People on the internet do
      weird things
01 traditional analytics
01 traditional analytics
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
01 traditional analytics
“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

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01 traditional analytics

  • 1. You toolbox for eBusiness success - Web Analytics Congress 2010 w/Sean Power & Jeroen Tjepkema
  • 8. About you We won’t tweet about it. Honest.
  • 10. What kind of site are you?
  • 12. 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
  • 13. What we’ll cover Analytics, interaction, UX, Voice of the Customer EUEM, synthetic tests, RUM Online communities, internal communities Competitive analysis Integrating data sources
  • 14. Which pretty much means We’re going to waste some of your time.
  • 16. Ries, Mclure, and Blank are often misquoted.
  • 17. They never said “fail faster”
  • 20. Waterfall, agile, and lean Three approaches for three situations
  • 21. Waterfall methodologies Know the problem and the solution
  • 22. Known ways to Known set of satisfy them requirements Spec Build Test Launch
  • 23. Known ways to Known set of satisfy them requirements Spec Build Test Launch
  • 24. Agile methodologies Know the problem, iterate on the solution
  • 25. Unclear how Known set of to satisfy them requirements Problem statement Build Test Viable? Launch Sprints Adjust
  • 26. Unclear how to Unknown set satisfy them of requirements Problem statement Build Test Viable? Launch Iterations & pivots Redefine problem, business
  • 27. Most new startups don’t know even know what problem they solve.
  • 28. 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
  • 29. As we become more agile, we need to be more aware.
  • 30. 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
  • 31. !"# !"#$"%&'()*!+',-(,( !"# !" !"#$%&' !"#$%&' ()*+",-. !"#$% !"#$%&'( !""#$%$&'()*+# !"#$%&'()$*'()+ !"#$% !"#$"%&'()*!+',-(,( !"#$%&' 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
  • 33. 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
  • 34. 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
  • 35. How much did visitors benefit my business? Internal analytics Conversion and Billing and account use abandonment Click-throughs Offline activity User-generated content Subscriptions
  • 36. Where’s my traffic coming from? External analytics Referring websites Inbound links from social networks Visitor motivation
  • 37. 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
  • 38. How good is my relationship with my market? Customer surveys, community monitoring Loyalty Enrollment Reach and rewards
  • 40. How healthy is my infrastructure? Performance monitoring Availability and Impact of performance performance on outcomes SLA compliance Content delivery Capacity and flash traffic
  • 41. How am I doing against my competitors? Performance monitoring Site popularity and ranking How are people finding my competitors? Relative site performance Competitor activity
  • 42. Where are my risks? Search, alerting Trolling and spamming Copyright and legal liability Fraud, privacy, and account sharing
  • 43. What are people saying about me? Search, community monitoring Site reputation Trends Social network activity
  • 44. How is my content being used elsewhere? Search, external analytics API access and usage Mashups, stolen content, and illegal syndication Integration with legacy systems
  • 47. 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
  • 50. Ice cream and drownings 10000 1000 100 10 1 Ice cream consumption Drownings
  • 54. True causality 10000 1000 100 10 1 Jan Feb Mar Apr May Jun Jul Aug Sept Oct Nov Dec Ice cream consumption Drownings Temperature
  • 58. Everybody has goals. http://www.flickr.com/photos/itsgreg/446061432/
  • 61. 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
  • 64. 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
  • 66. 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
  • 68. $ Media site Enrolment Targeted 2 embedded ad 5 $ 6 1 Ad Visitor network 4 3 5 Advertiser $ Departure $ site Impact on site $ Positive $ Negative
  • 69. Analytics is the measurement of movement towards those goals. http://www.flickr.com/photos/itsgreg/446061432/
  • 70. 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
  • 72. “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
  • 73. “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
  • 75. These people drive nicer cars than us. :/ Source: http://www.webanalyticsdemystified.com/sample/Web_Analytics_Demystified_RESEARCH_-_March_2007_-_Salary_Survey.pdf
  • 76. Hits
  • 78. Hits Pages
  • 81. Hits Pages Sessions
  • 84. Hits Pages Sessions Visitors
  • 86. Hits Pages Sessions Visitors Segments
  • 87. e ar e ts es en Th gm se
  • 88. (You can make your own.)
  • 93. Who would you rather have sending a message?
  • 94. Old analytics: report the news http://www.flickr.com/photos/thomasclaveirole/538819881/
  • 96. Old analytics: New analytics: report the news optimize goals http://www.flickr.com/photos/thomasclaveirole/538819881/ http://www.flickr.com/photos/sanchom/2963072255/
  • 97. 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.
  • 98. Find the site The three stages of a Use the site unique visit Leave the site
  • 99. Find the site: How did they get there?
  • 105. “Direct” traffic isn’t. Type-In Traffic Bookmarking JavaScript redirect Browser Inconsistencies Bots, Spiders and Probes
  • 107. source: the conversation prism by Brian Solis and JESS3 http://www.theconversationprism.com
  • 108. Use the site: What did they do?
  • 110. 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
  • 111. 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
  • 112. 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
  • 113. 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
  • 116. 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
  • 120. Leave the site: Parting is such sweet sorrow
  • 122. 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
  • 124. “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
  • 125. How did they do it? Web Interaction Analytics
  • 127. 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)
  • 129. Usability issue 1: Visitors don’t see what you wanted them to.
  • 135. Usability issue 2: Visitors don’t interact as you intended.
  • 137. Usability issue 3: Visitors don’t input data
  • 140. “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
  • 141. Voice of the customer Why did they do it?
  • 146. People on the internet do weird things
  • 149. So what’s this “VOC” thing? Get new ideas Evaluate things you can’t collect in other ways Evaluate sentiment Collect demographics data
  • 153. “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