A 80/20 approach to getting maximal insight in the shortest time and for the least fuss from your data (and a tour of corresponding pitfalls).

1. Statistics for hackers
@sbelak!
simon@hekovnik.si

2. Data science = storytelling

3. What to measure?

4. Metrics to Help You Make Wise Decisions
About Your Start-Up
• Customer Acquisition Cost (CAC)
• Activation
• Retention - Churn (attrition)
• Life Time Value (LTV)
• Conversions
• Revenue MRR
• Referral

6. Good metrics
Are Actionable ] optimize
Can be Audted
Are Accessible ]! understand!
An actionable metric is one that ties!
specific and repeatable actions !
to observed results.!
—Ash Maurya!

7. Metrics are data for
humans

8. Metrics have to hurt
(and be actionable)

9. Why should we measure?
!
Understand
&
optimize

10. Capture time dynamics

11. Capture every event
(not just touch points)

12. Usage cohort

13. User cohort

15. Time cohort
weeks
% active users
cohorts

16. Target
customer
Typical
customer →
dynamic

18. market analytics product analytics
Target Typical

19. Metrics vs. KPIs
Composite
↳ ratios
↳ comparable
!
!
Linked to product & story
↳ explainable
↳ indirect
What you actually measure

23. Focus on one KPI at
a time

24. The ambulance
response time problem

27. segmentation
clustering

28. segmentation
clustering

30. Statistics 101

32. 32

33. 33
Anscombe's
quartet!
!
• same mean!
• same variance!
• same correlation!
• same linear regression line!

34. 2
1
3
<3 Visualizations

35. Bar chart/histogram
Scatter plot/
line plot

36. Working with series
1st derivate: change!
2nd derivate: change of change

37. The dead salmon
conundrum

38. n!
Correlation!
!
Causati cause!
symptom!
symptom!
symptom!
cause!

39. Your
metrics
capture
external
relations!

40. Your
metrics
capture
external
relations!
NORMALIZE!!

41. Sample size
• Significance!
• http://www.surveysystem.com/sscalc.htm!
• http://getdatadriven.com/ab-significance-test!
• 10 x 100 > 1000

42. More data > better model

43. Hustling data:
proxy touch points

44. :) :(
t
happiness
bounce
bounce

45. Algebra of data
Sort!
Normalize!
Compose!
Decompose (segment)!
"

46. Your tools
shape your thoughts

47. Denormalize!
co-occurrence begets serendipity !
Aggregate data in
one place

48. (De)compose data

50. =
+

52. =
+

53. Data science = storytelling

54. Takeouts
• More data > better model.
• You can get away with very little statistics if you are diligent
with your visualizations.
• Metrics have to hurt (and be actionable).
• Time dynamics greatly enrich your data's explanatory power.
• Beware: humans are pattern matching machines and
sometimes we see patterns where there are none.
• Beware: your metrics capture external relations.