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Building an Experiment Pipeline for GitHub’s New Free Team Offering

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Building an Experiment Pipeline for GitHub’s New Free Team Offering

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In April 2020, GitHub announced a new Free for Teams plan. Behind the scenes, the engineering team was also setting up an experiment pipeline and an integration with Optimizely. In this session, we will take a peek at the process of setting up the integration, learning about the behavior of this new Free for Teams customer segment, and the next steps for this experiment pipeline.

In April 2020, GitHub announced a new Free for Teams plan. Behind the scenes, the engineering team was also setting up an experiment pipeline and an integration with Optimizely. In this session, we will take a peek at the process of setting up the integration, learning about the behavior of this new Free for Teams customer segment, and the next steps for this experiment pipeline.

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Building an Experiment Pipeline for GitHub’s New Free Team Offering

  1. 1. Building an Experiment Pipeline for GitHub’s New Free Team Offering Philip Bremer Director, Software Engineering GITHUB
  2. 2. Building an experiment pipeline for GitHub’s Free for Teams plan GitHub Presented by Philip Bremer
  3. 3. ● Director of Software Engineering
 UI Platform 
 ● Korean adoptee immigrant ● Soccer, yoga, skiing ● Current book: The New Jim Crow ● Ambivert: 75% introvert, 25% extrovert ● Pronouns: he/him About me @philip_bremer
  4. 4. GitHub’s growing product footprint
  5. 5. ● Individuals and teams ● Price should not be a barrier ● Free private repos ● Upgrade path for advanced features Free for Teams
  6. 6. Time to build an experiment pipeline!
  7. 7. New orgs and teams Global Nav Repo CTAsOrg Creation
  8. 8. Global nav experiment Problem: Users can’t easily find their organization(s) after creating or joining
 Hypothesis: Adding a link to “Your organizations” in user’s global dropdown menu will improve discovery Results: ● Success! ● +134.46% improvement ● Oops! We spent a ton of impressions ● ~10% of total impression budget
  9. 9. ● Misunderstood the difference between bucketing vs activation vs tracking
 ● High traffic for global nav
 ● The experiment baked too long How did we spend so many impressions?
  10. 10. Track Log notable events
 to calculate impact 
 of variation Bucketing, activation, and tracking Bucketing Assign user 
 to variation Activation Log impression 
 when user sees
 the experiment
  11. 11. ● Anonymous sessions are 
 read-heavy and repetitive 
 ● Cache anonymous sessions to reduce load on our application
 ● Goal: serve page for a subset of anonymous sessions without disrupting the benefits of our cache 
 ● Goal: serve page with no negative impact to our page load budget Logged out experiments
  12. 12. Performance & DX ● Robust datafile management ● More internal API guardrails for activate vs get_variation ● Improved logging and testing across environments Experimentation culture ● Empower feature teams to experiment with evidence- based delivery Up Next?
  13. 13. Thank you!

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