Customers not coming from paid marketing are key for marketability of apps in light of the struggle to keep customer lifetime value above customer acquisition cost. Unfortunately, data on these 'organic' users is sparse. It is mostly unknown why they download an app. Did they hear about it through family and friends (word-of-mouth)? Did they discover it on the App/Play Store? Did they watch a TV ad? Do they know your brand and searched for it? This talk investigates organic traffic in mobile games using data of the Berlin-based game developer Wooga. It introduces word-of-mouth as a key driver of adoption in the app ecosystem by presenting unique in-app survey results. It extends these with quantitative analysis and finally provides a synopsis by sketching a framework of the drivers of word-of-mouth for mobile apps.

1. People play – people talk? July ‘16

2. Founded in 2009
Based in Berlin, more
than 40 nations
Five hits in six
years
2

3. 3

4. 4
Organic Adoption and Word-
of-Mouth on Mobile

5. Inferring User Base
Dynamics from Sparse Data

6. Customer value
>
Customer acquisition cost

7. Cost per installLifetime value

8. 8
User
acquisition

9. Organics
9
User
acquisition
Discovery
Word-of-
mouth
Social
media
Search
Brand
Press
 Featuring
 ASO
 PR
 TV
 Offline
 Fun product

10. A lot of traffic cannot
be (easily) attributed
to a source

11. INSTALLS
Digital
marketing
Digital
marketing
CPI
X $
11

12. INSTALLS
Organics
Digital
marketing
CPI
X $
eCPI
=
30%
of CPI
12

13. Digital
marketing
CPI
Y $
INSTALLS
Organics
Digital
marketing
CPI
X $
eCPI
=
30%
of CPI
13

14. CPI
Y $
eCPI
=
70%
of CPI
CPI
X $
eCPI
=
30%
of CPI
INSTALLS
Digital
marketing
Organics
Digital
marketing
Organics
14

15. Effective CPILifetime value

16. Organics contribute
majorly to making app
marketing profitable

17. Better understand Organics
Predict organics for marketing budgeting
Model organics for LTV predictions
Identify opportunities for snowball effects

18. Better understand Organics
Predict organics for marketing budgeting
Model organics for LTV predictions
Identify opportunities for snowball effects

19. Better understand Organics
Predict organics for marketing budgeting
Model organics for LTV predictions
Identify opportunities for snowball effects

20. Better understand Organics
Predict organics for marketing budgeting
Model organics for LTV predictions
Identify opportunities for snowball effects

21. 21

22. • Using simple predictors like DAU, Marketing metrics,
Featuring
• Works pretty well
• Doesn’t enable conceptual understanding
• Out-of-sample performance ???
22
Predicting Organics

23. 23
Modelling Organics
Game 1
Game 2
Game 3
Geo 1 Geo 2 Geo 3

24. 24
„Uplift response functions“
Game 1
Game 2
Game 3
Geo 1 Geo 2 Geo 3 Geo 4 Geo 5 Geo 6 Geo 7

25. 25
Global log-log model
Game 1 Game 2 Game 3

26. 26
Spurious?

27. Organics
27
Digital
marketing
Discovery
Word-of-
mouth
Social
media
Search
Brand
Press

28. WOM
28
Digital
marketing
Discovery
Word-of-
mouth
Social
media
Search
Brand
Press

29. 29

30. 30
“Word-of-mouth is the
world’s most effective,
yet least understood
marketing strategy.”
- Ivan Misner

31. • 75% face-to-face, 15% phone, 10% online
• Drivers of WOM: customer satisfaction, trust and brand
commitment
• Nielsen: Most trusted source of consumer information,
most likely to be acted upon
• WOM customers add more value, create more WOM in
turn
31
WOM facts

32. 32
On
mobile?

33. Survey
Quantitative analysis

34. 34
Digital
marketing
Discovery
Word-of-
mouth
Social
media
Search
Brand
Press
Survey

35. Survey based Survey and tracking based
How did you find us? (Ad traffic excluded)

36. Survey based Survey and tracking based
How did you find us? (Ad traffic excluded)

37. Why was the game recommended to you?

38. Response position bias

39. Survey results
WOM >> WOM
People recommend casual
games mainly for fun

40. Mobile Ads
App Store
Charts
Word of Mouth
Day 7 Retention 100 95 120
Rounds until day 7 100 100 110
WOM users over-index on
retention and engagement

41. Source: Wikipedia
Granger causality test41
Quantitative analysis

42. Causality tests
CAUSING ORGANICS NOT CAUSING ORGANICS
• DRU
• Engagement
• New users
• Marketing volume
42

43. People play – people
talk?
43
CAUSING ORGANICS NOT CAUSING ORGANICS
•Marketing volume
•DRU in some large countries with
limited marketing activity
•Engagement
Causality tests

44. Rounds
per 2.5x x
player
44

45. • Substantially lower WOM
• Not consistently caused by a loyal
customer base and its engagement
• Even though engagement is very high
• Marketing activity partly causal
45
Pearl’s
Peril
vs.
Jelly
Splash

46. 46
?

47. 47
Game/app design
Product marketing

48. 48
Market conditions
Timing/
saturation Innovativeness
Competitors

49. Organics
49
Paid marketing WOM
Appeal of gameplay
Product marketing
Market conditions
Loyal,
satisfied
customer
base

50. •KPIs: Organics per Paid  Organics per kDRU
•Consider WOM in game/app design (e.g. giving players
things to talk about broadly, social features)
•Think about WOM in product marketing (game name –
Best Fiends, IP – Pokemon Go)
50
Business impact so far

51. 51

52. 52
People play – People talk !
(Give them an audience.)

53. 53
Very different for
different apps.

54. 54
Broad
audience
Niche
audience
eCPI << CPI LTV > CPI

55. - Survey & time series analysis

56. julian.runge@wooga.com