This document summarizes a talk given by Matthew Botta, Data Manager at Jet.com. The talk outlines six key takeaways for using data for product success: 1) Define goals upfront by asking questions; 2) Don't let perfect data prevent starting with good data; 3) Iterate frequently on data collection and analysis; 4) Too much data can be worse than no data; 5) Build a long-term, data-informed culture; 6) Collaborate closely with data analysts. ProductSchool offers part-time courses in product management, coding, data, digital marketing and blockchain in various cities worldwide and online.

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8. Matthew Botta
TONIGHT’S SPEAKER

9. • Current – Manager, Analytics & Insights at Jet.com
• Deputy Analytics Director, Hillary Clinton campaign
in Virginia (2016)
• Assorted roles at Dataminr
Matthew Botta

10. 1. Defining goals upfront by asking questions
2. Don’t let the perfect be the enemy of the good
3. Iterate, iterate, iterate!
4. When too much data is worse than no data
5. Building a data-informed culture for the long-term
6. Becoming friends with your analyst
Key Takeaways

11. 1.) Defining goals upfront by asking questions
▪ Importance:
Questions before metrics!
▪ Bad Example:
Starting with “I want to track x,y,z…”
▪ Good Example:
Defining rationale for action and how to
measure success
▪ Implementation:
Always ask – “Why are we doing this?”

12. 2.) Don’t let the perfect be the enemy of the good
▪ Importance:
▪ Start tracking at (or before) launch
▪ Bad Example:
▪ “Maybe we can try a different font?”
▪ Good Example:
▪ High-level metrics day 1, details after
▪ Implementation:
▪ What do we need to know first?

13. 3.) Iterate, iterate, iterate!
▪ Importance:
Learning what does and does not work
▪ Bad Example:
Publishing analysis and never revisiting
▪ Good Example:
Regular cadence of updates
▪ Implementation:
Collecting feedback, re-assessing success metrics

14. 4.) When too much data is worse than no data

15. 4.) When too much data is worse than no data
▪ Importance:
Too much data can lead to paralysis
▪ Bad Example:
5-page “Summary” report
▪ Good Example:
Topline Summary
▪ Implementation:
What does the audience need to know?

16. 5.) Building a data-informed culture for the long-term
▪ Importance:
Your supply of data analysts is a finite resource
▪ Bad Example:
“I don’t know, is this good?”
▪ Good Example:
“I tried pulling some metrics…”
▪ Implementation:
Training and knowledge sharing!

17. 6.) Becoming friends with your analyst
▪ Importance:
Do you want timely, helpful analysis?
▪ Bad Example:
5 PM emails about 9 AM meetings
▪ Good Example:
“Can we sit down and game this out?”
▪ Implementation:
Collaboration over deliverables

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