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EventLogging Workshop
Dario Taraborelli      Ori Livneh         Maryana Pinchuk
dario@wikimedia.org   ori@wikimedia.org   mpinchuk@wikimedia.org


            Wikimedia Foundation
                      March 7, 2013

                  http://bit.ly/ELWorkshop
Program
1.30-3.00. EventLogging tutorial

   • Intro and rationale (Dario)
   • From an idea to a data model: Designing a schema (Maryana)
   • Instrumenting code, collecting and validating EventLogging data (Ori)
   • Accessing, QA'ing and analyzing EventLogging data (Dario)


3.00-3.30 Coffee break

   • Coffee, cookies and snacks


3.30-5.00 Hacking session

   • Hands-on session to learn how to use EventLogging
Service announcements
IRC channel
  #wikimedia-e3 @ freenode.net


Streaming/recording
   You're on the air (1.30-3pm)
  http://www.youtube.com/watch?v=ZBuTjl0lh9Y


Mailing list
  Sign up at http://bit.ly/EL-News
Introduction and rationale
What we do
Typical problems

How many new users complete a funnel?

How many users fail to complete an edit?

Is the conversion rate of a new experimental
feature significantly higher than a control?

What is the breakdown by user segment in
the adoption of a feature?
open source
      usable
     scalable
   transparent

 Web analytics

for rapid prototyping
   and A/B testing
High-quality, granular data to
measure how users interact with
features and the UI.

         client-side events:
                clicks
             impressions
          bucketing events
                errors

         server-side events:
             transactions
Third-party solutions
Legacy solutions
Legacy solutions
ClickTracking: issues
1. scalability
ClickTracking: issues
2. flexibility
ClickTracking: issues
              2. flexibility
event names
ext.articleFeedbackv5@4-option3-cta_learn_more-button_click-
overlay



additional data
e90RMgpyyOjKArMiyFUadnI1LgqkPXSU|http://en.wikipedia.
org/wiki/Main_Page|Foo
ClickTracking: issues
     3. data modeling


No mechanism for data validation
ClickTracking: issues
     3. data modeling

Limited support for data
documentation and provenance
ClickTracking: issues

4. data-centric collaboration

     Product managers
     Data analysts
     Engineers
     UX designers
EventLogging: design principles

Support getting desired metrics, not simply all available metrics

Provide data provenance/ enable long-term use of data

Support data-centric collaboration: using a wiki for data modeling

Support versioning/permit data sets to be compared

Support transparency/community scrutiny

Do not capture every action on site, but rich, well-documented data that fit the
question

Enforce lightweight client and server-side validation

Creates a contract between data analyst, product manager, engineer around
data definitions

Simplify access to data, QA and data analysis
Next
 • From an idea to a data model: Designing a schema (Maryana)

 • Instrumenting code, collecting and validating EventLogging data (Ori)

 • Accessing, QA'ing and analyzing EventLogging data (Dario)

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EventLogging Workshop

  • 1. EventLogging Workshop Dario Taraborelli Ori Livneh Maryana Pinchuk dario@wikimedia.org ori@wikimedia.org mpinchuk@wikimedia.org Wikimedia Foundation March 7, 2013 http://bit.ly/ELWorkshop
  • 2. Program 1.30-3.00. EventLogging tutorial • Intro and rationale (Dario) • From an idea to a data model: Designing a schema (Maryana) • Instrumenting code, collecting and validating EventLogging data (Ori) • Accessing, QA'ing and analyzing EventLogging data (Dario) 3.00-3.30 Coffee break • Coffee, cookies and snacks 3.30-5.00 Hacking session • Hands-on session to learn how to use EventLogging
  • 3. Service announcements IRC channel #wikimedia-e3 @ freenode.net Streaming/recording You're on the air (1.30-3pm) http://www.youtube.com/watch?v=ZBuTjl0lh9Y Mailing list Sign up at http://bit.ly/EL-News
  • 6. Typical problems How many new users complete a funnel? How many users fail to complete an edit? Is the conversion rate of a new experimental feature significantly higher than a control? What is the breakdown by user segment in the adoption of a feature?
  • 7. open source usable scalable transparent Web analytics for rapid prototyping and A/B testing
  • 8. High-quality, granular data to measure how users interact with features and the UI. client-side events: clicks impressions bucketing events errors server-side events: transactions
  • 14. ClickTracking: issues 2. flexibility event names ext.articleFeedbackv5@4-option3-cta_learn_more-button_click- overlay additional data e90RMgpyyOjKArMiyFUadnI1LgqkPXSU|http://en.wikipedia. org/wiki/Main_Page|Foo
  • 15. ClickTracking: issues 3. data modeling No mechanism for data validation
  • 16. ClickTracking: issues 3. data modeling Limited support for data documentation and provenance
  • 17. ClickTracking: issues 4. data-centric collaboration Product managers Data analysts Engineers UX designers
  • 18. EventLogging: design principles Support getting desired metrics, not simply all available metrics Provide data provenance/ enable long-term use of data Support data-centric collaboration: using a wiki for data modeling Support versioning/permit data sets to be compared Support transparency/community scrutiny Do not capture every action on site, but rich, well-documented data that fit the question Enforce lightweight client and server-side validation Creates a contract between data analyst, product manager, engineer around data definitions Simplify access to data, QA and data analysis
  • 19. Next • From an idea to a data model: Designing a schema (Maryana) • Instrumenting code, collecting and validating EventLogging data (Ori) • Accessing, QA'ing and analyzing EventLogging data (Dario)