In this presentation, Chris More from Mozilla, talks about how to build a growth model to predict growth.

1. Growth Models—How to Build One
to Predict Growth
Chris More, Mozilla

2. predictive growth models
5 steps to acquiring precognition superpowers

3. // quick intro
Chris More
Head of Firefox Growth at Mozilla
Background:
★ Leadership
★ Digital marketing
★ Data and analytics
★ Full-stack developer
★ Race car driver (zoom zoom)

4. // question this talk will help you answer
Paid Media
Launch a 3-month paid
media campaign with
goal of 1 million new
installs.
Onboarding
Launch a new user
onboarding experience that
may improve D30 retention
by 3%.
which project should you invest in first?

5. // what makes a growth model “predictive”?
Inputs:
➔ Acquisition metrics
➔ Retention rates
➔ Weekly/seasonal trends
Outputs:
★ Active users base
★ Growth ratios
★ Future growth predictions

6. modeling 101
(no brain melting necessary)

7. step 1: inputs
building a predictive growth model

8. // inputs: active users
Active Users

9. // inputs: leaky bucket analogy
Active Users
Funnel
Acquisition

10. // inputs: leaky bucket analogy
Active Users
Funnel
Acquisition
Churned Users

11. // inputs: leaky bucket question
Active Users
What are the
characteristics
of these
users?
When did we
acquire these
users?

12. // inputs: acquisition timeline

13. // inputs: acquisition timeline
D0 D1 D2 D3 D4

14. // inputs: acquisition timeline
D0 D1 D2 D3 D4

15. // inputs: acquisition timeline
100
200
300
400
D0 D1 D2 D3 D4
D0: Day of product launch
Dn: n days after launch

16. // inputs: acquisition timeline
100
200
300
400
D0 D1 D2 D3 D4

17. // inputs: acquisition timeline
100
200
300
400
D0 D1 D2 D3 D4

18. // inputs: acquisition timeline
100
200
300
400
D0 D1 D2 D3 D4

19. // inputs: acquisition timeline
100
200
300
400
D0 D1 D2 D3 D4

20. // inputs: acquisition timeline
100
200
300
400
D0 D1 D2 D3 D4

21. // inputs: user retention
25%
50%
75%
100%
D0 D1 D2 D3 D4

22. // inputs: user retention
25%
50%
75%
100% D0: Day of acquisition
Dn: n days after acquisition
D0 D1 D2 D3 D4

23. step 2: cohorts
building a predictive growth model

24. // cohorts: consistent acquisition (for simplicity)
100
200
300
400
D0 D1 D2 D3 D4

25. // cohorts: consistent retention
25%
50%
75%
100%
D0 D1 D2 D3 D4

26. // cohorts: apply retention to cohorts
100
200
300
400
D0 D1 D2 D3 D4

27. // cohorts: apply retention to cohorts (colored)
100
200
300
400
D0 D1 D2 D3 D4

28. // cohorts: add up cohorts vertically
100
200
300
400
400
D0 D1 D2 D3 D4

29. // cohorts: add up cohorts vertically
100
200
300
400
400 620
D0 D1 D2 D3 D4

30. // cohorts: add up cohorts vertically
100
200
300
400
400 620 795
D0 D1 D2 D3 D4

31. // cohorts: add up cohorts vertically
100
200
300
400
400 620 795 955
D0 D1 D2 D3 D4

32. // cohorts: add up cohorts vertically
100
200
300
400
400 620 795 955 1105
D0 D1 D2 D3 D4

33. // cohorts: populating spreadsheet model
180
days
180 days

34. // cohorts: spreadsheet model details
time (days)
cohorts
Daily
cohorts
colored as
seen
previously
new users

35. step 3: outputs
building a predictive growth model

36. // outputs: review our cohorts
100
200
300
400
400 620 795 955 1105
D0 D1 D2 D3 D4

37. // outputs: graph active users
300
600
900
1200
D0 D1 D2 D3 D4
400
620
795
955
1105

38. we’ve got growth, right?
not forever.

39. // outputs: growth prediction
Uh oh.
Growth stalls
6 months
later...

40. now what? did we fail?
not yet.

41. // outputs: leaky bucket equilibrium
Active Users
Funnel
Acquisition
Churned Users
Inputs
=
Outputs
What can
we do about
this growth
decline?

42. step 4: scenarios
building a predictive growth model

43. // scenarios
What if the following happened X days after product launch?
1. Optimized onboarding improves D7+ retention by 5%?
2. We increased acquisition by 10% between D30 and D90?
3. Acquisition rates decline 1% per week after D120?

44. // scenarios: retention improvement scenario
Outcome:
4% additional
DAU at 6 months.
What if optimized
onboarding
improves D7+
retention by 5%?

45. // scenarios: paid media acquisition example
Outcome:
1% additional
DAU at 6 months.
What if we
increased
acquisition by
10% between
D30 and D90?

46. // scenarios: acquisition decline example
Outcome:
5% loss of
DAU at 6 months.
What if
acquisition started
to decline 1% per
week at D120?

47. // scenarios: impact trends
1. Short-lived acquisition campaigns decline over time.
2. Retention improvements compound over time.
3. To continue with growth, you must improve growth metrics.

48. step 5: ratios
building a predictive growth model

49. // scenarios
For every...
➔ X new users, we see Y active users in Z days.
➔ For every X% better retention, we see Y DAU in Z days.
➔ For every $X cost in acquisition, we see $Z in lifetime value.

50. // scenarios: benefits
Why growth ratios?
➔ ROI & Lifetime value
➔ Prioritization of growth tactics
➔ Goal setting and planning

51. modeling re-cap
predictive growth models

52. // re-cap
★ At a minimum, a model needs 2 inputs: acquisition+retention.
★ The model can predict future active users.
★ The power of model is understanding “what if scenarios”.
★ Growth ratios are perfect for planning (rightsize tactics) and ROI.
★ You can do basic predictive growth modeling in a spreadsheet.
★ Keep the model as simple as possible.

53. // next steps
I will be open-sourcing this predictive growth model.
Follow me on twitter (@chrismore). I will be posting the
growth model in June.
Questions?
➔ Say hello after the event
➔ Tweet questions

54. // next steps
I will be open-sourcing this predictive growth model.
Follow me on twitter (@chrismore). I will be posting the
growth model in June.
Questions?
➔ Say hello after the event
➔ Tweet questions
Thank you!

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