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Product Management from Facebook to GitHub: Using data to understand users & build products Identify Define Creating Opportunities Success Experiences Who am I? Cristina Fernndez Counsyl (genetics) GitHub Facebook Identify Define


  1. Product Management from Facebook to GitHub: Using data to understand users & build products

  2. Identify Define Creating Opportunities Success Experiences

  3. Who am I?

  4. Cristina Fernández Counsyl (genetics) GitHub Facebook

  5. Identify Define Creating Opportunities Success Experiences

  6. Identify Define Creating Opportunities Success Experiences

  7. Initial Assumptions (@FB) ● Blocked users are spammers ● People should only be friending people they know

  8. But when we dug into the data the profiles didn’t look like spammers ● Established accounts ● With 100+ of friends ● Who posted regularly

  9. So why are the users being blocked?

  10. Supplement data patterns with user interviews ● Wanted to explore the world - meet new people! ● Didn’t understand the “rule” about only friending people you know (cultural expectation) ● Most people liked receiving friend requests from new people

  11. What did we do? 1. Trained algorithm on localized data 2. Changed communication around WHY people were being blocked Results: ● Less people actually getting blocked (happy users) ● More friend requests sent & accepted (FB growth)

  12. Data served a starting point for discovering an opportunity

  13. Identify Define Creating Opportunities Success Experiences

  14. #vanitymetrics

  15. New Feature: Require 2fa ● User requests ● Manual workaround ● Improve security

  16. How many Organizations can we expect to enable this?

  17. Focus on Organizations that are already requiring 2fa

  18. Estimation parameters ● Organizations that are actively developing code ● >10 Users ● >80% of users have 2fa enabled

  19. Define a goal that reflects the user needs being addressed

  20. Identify Define Creating Opportunities Success Experiences

  21. Artificial Intelligence

  22. Starting from scratch meant we needed to build all the building blocks Create a production Build in feedback Make Topic Develop ML and ready database mechanism to suggestions NLP algorithms with Topic improve the accessible for users suggestions algorithm

  23. We needed lots of different teams and skillsets ● Data Scientists (Machine Learning) ● Data Engineering (Data Infrastructure) ● Application/Front-End Engineers (user facing features) ● Design (UX)

  24. Cross functional work allows you to bring all the pieces together

  25. Identify Define Creating Opportunities Success Experiences

  26. Q & A

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