This document discusses the importance of product managers understanding statistics. It argues that using statistics and data analysis helps product managers better understand their users and make more informed decisions. Some key points made include: - Statistical analysis tools like probability, cohort analysis, and Pareto analysis can provide insights into who uses a product, when, and why. - Averages can hide important distributions in data and obscure outliers. Things like variance are more revealing. - Understanding data through a statistical lens prevents making assumptions without evidence and being misled by superficial metrics. - Courses in statistics, computer science, marketing, and data science can equip product managers to analyze user data effectively.

1. Product Management &
Statistics?
MARJUKKA NIINIOJA OSAANGO
CONNECT: @MNIINIOJA & LINKEDIN

2. My example data comes from…
Introduction to API Economy
Free 1 ECTS Master’s level course with Tampere
University
www.apieconomy.info & www.osaango.academy

3. On any given day, a project
manager should be thinking….
 Who uses my product?
 Who doesn’t use it?
 Why do they use/don’t use?
 When did they start/stop using?
 Why?
 What to put next in the product roadmap?
 New features
 Fixes

4. Solution?
https://www.datasciencesociety.net/mooc-courses-for-
getting-started-with-machine-learning/
Our focus today

5. Assuming you have a mass of
data…
 If you know the what the “correct answer” is but you are not sure how to
get there…
 use Machine Learning (which is based in its core on probabilities, which is a
statistical method)
 If you don’t know the what the outcome should look like…
 use raw Statistical Analysis and season with a Data science approach

6. Must have courses for PMs
 Finance & Computer Science
 Marty Cagan, Inspired: How to build
tech products users love, 51.
 All of the above + Statistical
analysis
 https://www.philosophicalhacker.com
/why-pms-should-study-statistics/
 All of the above + Marketing &
Introduction to API Economy
 Marjukka Niinioja in Product Tank
Helsinki Apr 23, 2020

7. Why Statistics & PM?

8. Because assumption is the mother
of all f***-ups and pretty pictures
tell lies
Wow, most of our
users are men and we
have users in all age
groups!

9. But the truth needs some digging…
Oh, but most of our
returning users are
under 54 and almost
half of them are
female!

10. Probability
 The most important thing when handling anything with machine learning
& AI
 Estimation tool
 “Based on your latest tweets you are a (with a 0.75 probability) a 25 yrs
old man”
https://www.youtube.com/watch?v=OgO1gpXSUzU

11. Caveat average – long live
distribution
https://hbr.org/2002/11/the-flaw-of-averages
https://medium.com/@joseperezaguera/some-basic-statistical-techniques-for-product-managers-250a02586453
How confident are
you that we are on
track with the release
for the big marketing
launch?
How much traffic will we have on
average, I need to estimate the
infrastructure capacity and costs
volatility = std dev (velocity week) /
average (velocity week)
(std dev of velocity * # of remaining
iterations) / average velocity

12. Example of
NPS and
average
What’s the average of
our NPS score?
https://www.checkmarket.com/
blog/net-promoter-score/

13. Variance shows anomalies
Sum
Variance

14. Cohort analysis
 Behavioural
 1 or more common
charasteristics in a group
(=“cohort”) with in a time-span
 Built into many tools, like
Google Analytics
https://medium.com/analytics-for-humans/a-beginners-
guide-to-cohort-analysis-the-most-actionable-and-
underrated-report-on-google-c0797d826bf4

15. Example with raw “week cohorts”

16. Example cohort with raw “week +
experience” cohorts

17. Pareto analysis – find the issues to
fix with the 80/20 Pareto principle
https://en.wikipedia.org/wiki/Pareto_analysis

18. Learn & partner
WWW.OSAANGO.ACADEMY