The story of the Product Growth team at Atlassian
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The document summarizes the work of the Product Growth team at Atlassian, an Australian software company. It discusses how the team uses behavioral data and experimentation to improve products and increase active users. Some key challenges included building infrastructure to integrate data sources, improving data quality, and driving cultural change to encourage data-driven decision making. The team analyzes user event data to identify areas for testing and works with product managers to modify the user experience based on experimental results.
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The story of the Product Growth team at Atlassian
- 1. The story of the Product Growth team at Atlassian Ilias Flaounas Sr. Data Scientist ! ! ! ! Web Analytics Meetup, Sydney 14 October 2015
- 2. Atlassian - Trivia • Atlassian is an Australian enterprise software company specialised in products that support project management and collaboration. • ~13 years old • >1.5K employees • Headquarters in Sydney • Best place to work in Australia (2014 & 2015) (The Australian Financial Review)
- 6. Key Requirements:! • Support from upper management! • Some minimum critical mass of customers! • Serious investment in infrastructure, people, time
- 7. Product Growth Team • 18 members (from initially 6 members on 2013) • Main mission: Increase Active Users! • How? * Discover value in behavioural data * Experiment * Suggest improvements! • Champion data-driven decision making
- 14. Where do we fit in the org? A kind of stretched analogy… Patient User Illness Bad User Experience Medical doctor Product manager Diagnosis Interviews, surveys… Treatment UX modification Medical researchers Growth team Observations Data mining Medical trials A/B tests Ethics Privacy Policy Evidence-based medicine Data-driven development
- 15. The Challenges • Build infrastructure! - break data silos - replace existing “solutions” - improve data quality - add instrumentation
- 16. We analyse the behavioural data Timestamp Instance User Event Attributes 1356958862 abcd2134abcd AB12FF89 Login 1356968863 abcd2134abcd DA99A11 Login 1356978862 abcd2134abcd AB12FF89 dashboard view 1356988862 abcd2134abcd DA99A11 dashboard view 1357884442 abcd2134abcd FF12FF89 view issue {issueID = 1234} 1357956862 abcd2134abcd AB12FF89 create issue {issueID = 1000} 1357957862 abcd2134abcd DA99A11 view issue {issueID = 1000} 1357958862 abcd2134abcd DA99A11 comment issue {issueID = 1000} 1357959862 abcd2134abcd AB12FF89 create issue {issueID = 1001} … … … … …
- 17. Analytics Infrastructure (v.2) On 2014:! ~3-4TB overall (highly compressed)! 800GB per day produced (uncompressed)
- 21. • Build infrastructure! - break data silos - replace existing “solutions” - add instrumentation - improve data quality • Cultural change & Education! - help people ask the correct questions! - challenge gut feelings! - “analytics is counting” The Challenges
- 22. Help people ask the correct questions…
- 23. Help people ask the correct questions… Report 1 Report 2 Report 3 Report 4 Report 5 Report 6 Report 7 Report 8 Report 9
- 25. No usage
- 26. No usage Low usage
- 27. No usage Low usage Medium usage High usage
- 28. Deal with gut feelings & common sense… How Common Sense Fails Duncan Watts, 2011 κοινὴ αἲσθησις Aristotle, 384–322 BCE
- 30. • Source: P. Chopra, The ultimate guide to A/B testing, http://www.smashingmagazine.com, 2010
- 31. Voting time: Raise your Left or Right hand for the version that worked best; No hand if you believe there’s no difference
- 32. Subject: %%first_name%%, assemble the bitbucketeers! Subject: Grab the Bitbucketeers...4, 3, 2, 1 LAUNCH! Voting time: Raise your Left or Right hand for the version that worked best; No hand if you believe there’s no difference
- 33. Voting time: Raise your Right hand for success; Left hand for failure; No hand if you believe there’s no difference
- 34. Voting time: Raise your Right for success; Left hand for failure; No hand if you believe there’s no difference
- 35. Voting time: Raise your Right hand for success; Left hand for failure; No hand if you believe there’s no difference
- 36. Deal with “Analytics is counting”…
- 37. X. Fu & H. Asorey, “Data driven product Innovation”, Tutorials, KDD 2015 We are completing a transition… • Phase A: Data scientists as Owners • Phase B: Data scientists as a Service • Phase C: Data scientists as Partners
- 38. Take away message • Becoming data-driven is hard • Cultural change is the key challenge - More difficult than the engineering part • The transition is a marathon not a sprint • There’s no common sense, thus potential is high
- 39. Thank you!