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.

Logiciels

Building an Experiment
Pipeline for GitHub’s New
Free Team Offering
Philip Bremer
Director, Software Engineering
GITHUB

Building an experiment
pipeline for GitHub’s
Free for Teams plan
GitHub
Presented by Philip Bremer

● 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

● Individuals and teams

● Price should not be a barrier

● Free private repos

● Upgrade path for advanced
features
Free for Teams

New orgs and teams
Global Nav Repo CTAsOrg Creation

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

● 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?

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

● 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

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?

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

1. Building an Experiment Pipeline for GitHub’s New Free Team Offering Philip Bremer Director, Software Engineering GITHUB

2. Building an experiment pipeline for GitHub’s Free for Teams plan GitHub Presented by Philip Bremer

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. GitHub’s growing product footprint

5. ● Individuals and teams ● Price should not be a barrier ● Free private repos ● Upgrade path for advanced features Free for Teams

6. Time to build an experiment pipeline!

7. New orgs and teams Global Nav Repo CTAsOrg Creation

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. ● 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. 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. ● 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. 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. Thank you!

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

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