Product Management Event at #ProductCon NY on Data Analytics for better Product Decision Making by Vlado Hruda Product Manager at Mixpanel

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Data Analytics for better Product
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5. 5
Vlado Hruda - PM @ Mixpanel
Sep 10, 2019
Data Analytics for better
Product Decision Making

6. 6
I. Why should you care?
II. Data analytics framework
III. Best practices: 3 Case Studies
Outline

7. 7
Success
How people think
it looks like ...

8. 8
Success
How people think
it looks like ...
… what it really looks like
Success

9. Focusing on intuition ...
9

10. Focusing on intuition might not be always enough to succeed
10

11. Blockbuster went bust
11
VS

12. Anomo: social network for introverts
12

13. Juicero went down the drain
13

14. Google Glasses did not help PMs see the future
14

15. 15
What do these stories
have in common?

16. Product leaders had limited visibility into
product usage & market trends
16

17. Instead, by combining intuition & with being data informed can
dramatically increase your chance of success
17
User analyticsIntuition

18. 18
Success
How people think
it looks like ...
… what it really looks like
Success
Insight A
Insight B
Insight C

19. 19
Data analytics framework
How can we use Data
Analytics for better
product decisions?

20. Collect accurate data
Provide access to real-time
behavioral data anyone can
use
Engage
Execute your ideas through product
experiments, messaging, and
personalization
Set goals & create hypotheses
Know what targets help you achieve
your goals and develop ideas for how to
hit them
Understand the why
Go deeper to identify the
cause of each trend
Identify trends
Define your metrics and see
the patterns that matter
Implement Data
Analytics with
Innovation Loop

21. Innovation Loop example:
Retail eshop
Imagine
Owner of an online store
o Goals
○ Maximize sales
○ Increase sales
velocity

22. Innovation loop
1) Collect accurate data: track your purchases
22
Purchases tracking

23. Innovation loop
1) Collect accurate data: track your purchase in a funnel
23
Home page Product Page Purchase

24. Innovation loop
2) Identify the trends: Slice & dice the data
24
Home page Product Page Purchase

25. 25
Home page Product Page Purchase
Innovation loop
3) Understand the why: Create cohort of behavior you want to understand
New User cohort

26. Innovation loop
3) Understand the why:
Reach out to user from your
cohort for qualitative research

27. Innovation loop
4) Set goals & create hypotheses
- Goal: New users should perform almost
as good as returning users
- Hypothesis: Recent mobile & web
optimization negatively impacted new
user behavior
Signed in
Browsed product page
Express Checkout
Quit

28. 28
Innovation loop
4) Set goals & create hypotheses: Validate your hypotheses at scale

29. 29
Innovation loop
4) Set goals & create hypotheses: Validate your hypotheses at scale

30. Innovation loop
5. Engage: Re-engage with
Katie & other customers &
help her finish purchase
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31. 31
I. Reaching product/market fit
II. Product launch in 6 months
III. Interface redesign w/ 0 churns
3 Case
studies

32. 1st Case study:
Reaching product/market fit

33. I. Challenge
● Top company bet on AI
analysis tool
● We had only 3 users in a
closed beta
● Retention approaching 0%
1st Case study: Product/market fit

34. 34
II. Solution
- Validated value
proposition in calls
- Funnel analysis to
discover UX challenges
- Used AI to provide even
more value that users
asked for
1st Case study: Product/market fit

35. III. Results
● Top AI adopted product
with 300x+ usage
growth
● Fixed retention from 2%
to ~15% range
● Started to scale solution
to another analysis
product
1st Case study: Product/market fit
Mixpanel AI tool adoption

36. 2nd Case study:
Product launch

37. I. Challenge
● Top product gap
● Limited resources
● Need to deliver quick
solution
2nd Case study: Product Launch

38. 38
II. Solution
1. Prioritize rigorously with
analytics
2. Create a nimble, speedy
team
3. Put a stake in the ground
4. Provide customers with
value right out the gate
5. Go beyond the status quo
and launch state-of-the-art
features
TRENDS
All base analytics
packages
2nd Case study: Product Launch

39. III. Results
● Turned the top reason
why we failed to win
deals to a reason we
won them
● Became one of the top
used Mixpanel report
● Other market players
tried to copy some of
the key features
2nd Case study: Product Launch

40. Case study III:
Redesign

41. I. Challenge
● Funnels: product with
10 years of traction
● Current UI did not meet
new users expectations
● Existing users had
ingrained bad habits
3rd Case study: Redesign

42. 42
II. Solution
- Tracking set at each
control level (e.g.
button)
- 20+ customer calls to
discover & validate
friction points
- Walkthrough
onboarding
- Staged rollout
TRENDS
All base analytics
packages
3rd Case study: Redesign

43. III. Results
● 0 churned customers
● No twitter ***-storm
● Consistent growth of
users
3rd Case study: Redesign

44. Collect accurate data
Provide access to real-time
behavioral data anyone can
use
Engage
Execute your ideas through product
experiments, messaging, and
personalization
Set goals & create hypotheses
Know what targets help you achieve
your goals and develop ideas for how to
hit them
Understand the why
Go deeper to identify the
cause of each trend
Identify trends
Define your metrics and see
the patterns that matter
Summary:
Data analytics can be the
driver to help you win the
market with your product

45. Thank you!

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