Data is one of the most effective tools a product manager has. In fact, it should become an essential part of a PM’s working day. When used correctly, its potential can be maximized for making smart, informed decisions which push the product to the next level and leverage its success. The continuing evolution of AI systems allows processing large amounts of data, and their accuracy increases along with data volume. Therefore, the demand for data continues to grow. So, how can we embrace data and data-driven methodologies and turn numbers into solid products? In this ProductTank TLV meetup, on Tuesday, March 26 2019, Itamar Bartur counted the merits of a data-driven PM. Itamar Bartur is Principal Product Manager, Search & Recommendation, Alexa Shopping at Amazon. Previously he was VP Product at BondIT. Itamar has vast experience with ML-based data driven decision solution. He holds B.Sc in Computer Science, Mathematics & Cognitive Science from the Hebrew University, Jerusalem, as well as an MBA from Tel-Aviv University.

1. A MAN WITH OPINION
Data Driven Product Management

2. Itamar Bartur
Principal Product Manager
Search & Recommendations
Alexa Shopping Amazon

4. Being a data driven PM
Why? How to get
there?
Tips to use
and
pitfalls to avoid!

5. Being a data driven PM
Show me the value

6. What is your
FlyWheel?

7. What is your
FlyWheel?
Telemetry
Customer
Experience
Quality
Product
Priorities
Solution
Customer

8. Being a data driven PM
Show me the value

9. Show me the data
The 5 Whys The 5 Hows

10. An example that (never) happened
Increased customer usage,
how bad that can be?

11. WHY DATA?
What it really means to
be a data-driven PM?

12. WHY DATA?
Unbiased decision making

13. WHY DATA?
Strongly convicted,
Loosely coupled

14. Divide et Impera
Quality Usage Financials

15. The
Data-Driven
FlyWheel

16. Working
Backwards
❏ Start with a goal
❏ Work backwards from customers
❏ Work backwards from data customers
How?

17. Crawl, Walk, Run
Build your data capabilities in phases

18. Crawl, Walk, Run
One Door Decisions vs. Two Doors Decisions

19. Balancing KPIs
The importance of balancing your KPIs

20. Balancing KPIs
How to find the balancing KPI [be mean]

21. Apple to Apples
The denominator is the Usual Suspects

22. Apple to Apples
Example – women vs. men salary

23. Apple to Apples

24. SPEED MATTERS.

25. ❏ Data can be the enemy (Analysis paralysis)
❏ Offline tests
❏ One Door vs. Two Doors
❏ Significant results require time
❏ Judgment calls

26. THERE IS NO SUCH A THING AS
AUTOMATED PRODUCT
MANAGEMENT.

27. THANKS!
You can find me at
itamarb12@gmail.com