How to create good BI for UA activities? How we did it at Pixel Federation? How we structure our systems and our teams? What we can say based on our experience? Presentation run by Michal Grno at 8th edition of GameCamp (www.gamecamp.io).

1. Data behind UA
Michal P. Grno, Head of Marketing Analytics
February 2020

2. LTV > CAC*

3. Doing
marketing
without any
data
Checking
reports within
GA or FB
Buying 3rd
party tool or
using MMP
Building your
own
Marketing
Analytics
Stack
www.pixelfederation.com
Data behind UA

4. Today’s Agenda
1. The basics
2. Our BI Stack & Team structure
3. Doing UA the smart way
www.pixelfederation.com
Data behind UA

5. The basics

7. Source: The only growth metric that matters (Mobile Dev Memo, 2019)

8. ● CTR (by channel / geo /
campaign / optimization)
● CPM
● IPM
● eCPI
● CPP (cost per payer)
● CPd1R (cost per d1
returned user)
● CTI (install rate)
● dN Retention / Retention
profile
● Tutorial completion
● Custom eng. metrics
(finished N quests, sent
N ships, etc.)
● Conversion rate
(to payer)
● Payers
● ARPU
● ARPPU
● Ad Revenue
● Score
● ROI / ROAS
App Install Engagement
Traffic
Source X
Acquisition Retention Monetization
Our take on the UA metrics
www.pixelfederation.com
Data behind UA
The basics

9. The basic setup of mobile marketing
App Network MMP Reporting
www.pixelfederation.com
Data behind UA
The basics

10. Our BI Stack

11. Data Discovery Workbench / Lab
Raw Data
Staging
Layer
Performance
Layer Custom Apps
Dashboards
Our Data Ecosystem
DWH API
API
API
www.pixelfederation.com
Data behind UA
Our BI Stack

12. Raw Data
Staging
Layer
Tracking
800,000,000
> 40%
actions tracked every single day
used for calculating reports
15+
3rd party APIs integrated
500
GBs of data processed daily
www.pixelfederation.com
Data behind UA
Our BI Stack

13. Processing
Data Discovery Workbench / Lab
Raw Data
Staging
Layer
Performance
Layer
~4,500
50
automated jobs daily
GBs of compressed data stored
9
analysts digging into data
www.pixelfederation.com
Data behind UA
Our BI Stack

14. Reporting
20,000,000
101
events sent to 3rd parties
reports refreshed
Performance
Layer Custom Apps
Dashboards
DWH API
API
www.pixelfederation.com
Data behind UA
Our BI Stack

15. Our BI Stack
Team

16. www.pixelfederation.com
Data behind UA
Our BI Team
Centralized
DATA
team
DA
TS
SP
EA
● Benefits
○ Knowledge sharing
○ Replaceability
● Drawbacks
○ Analytics only as a support
○ Not being part of product team
○ Communication

17. www.pixelfederation.com
Data behind UA
Our BI Team
Centralized
DATA
team
DA
TS
SP
EA
CDO
+
DWH
DA
SP MKT
TS2AC
*
*
*
*
*

18. www.pixelfederation.com
Data behind UA
Our BI Team
CDO
+
DWH
DA
SP MKT
TS2AC
*
*
*
*
*
● Benefits
○ Agility
○ Analytics as part of a product
○ Working on things that matter
○ Communication with PMs and GDs
● Drawbacks
○ Some tasks take too long
○ Doing things that have been
already done

19. www.pixelfederation.com
Data behind UA
Our BI Team
CPO
DASP
MKT TS2AC
*
*
*
**
CMO CTO
GA
Lead
MA
Lead
DWH
Team
HoT

20. Doing UA the smart way

21. LTV > CAC
Really easy
Not so much :(

22. Predicting the LTV
● Lifetime value of a player
○ “Lifetime” = predefined time period (3m, 6m, 1y, …)
○ Usually period to regain the costs
This is where we are now
● Methods
○ Historical revenue
○ Retention method (ARPDAU*Lifetime)
○ Continuous ARPU curve
○ Normalized ARPU curve
○ More advanced methods
■ Pareto/NBD
■ BG/NBD
■ Gamma-Gamma
www.pixelfederation.com
Data behind UA
Doing UA the smart way

23. Predicting the LTV
www.pixelfederation.com
Data behind UA
Doing UA the smart way

24. Predicting the LTV
● ARPU curves
○ calculated from historical data
○ for project + platform
○ function of days_in_game
○ normalization means that norm_ARPU(364) = 1
● How does the prediction works?
○ pred_ARPU(x) = actual_ARPU(x) / norm_ARPU(x)
www.pixelfederation.com
Data behind UA
Doing UA the smart way

25. Predicting the LTV
AFK Cats Android 21%
Diggy
Android 9%
Canvas 4%
Seaport
Android 19%
Canvas 12%
TrainStation Android 17%
D7 ROAS Benchmark
www.pixelfederation.com
Data behind UA
Doing UA the smart way

26. Predicting the LTV
AFK Cats Android 31%
Diggy
Android 23%
Canvas 13%
Seaport
Android 31%
Canvas 25%
TrainStation Android 40%
D30 ROAS Benchmark
www.pixelfederation.com
Data behind UA
Doing UA the smart way

27. Predicting the LTV
Project Platform D7 D30
AFK Cats Android 21% 31%
Diggy
Android 9% 23%
Canvas 4% 13%
Seaport
Android 19% 31%
Canvas 12% 25%
TrainStation Android 17% 40%
www.pixelfederation.com
Data behind UA
Doing UA the smart way
dN ROAS Benchmark
Project Platform D7 D30
AFK Cats Android 4.8x 3.2x
Diggy
Android 11.1x 4.3x
Canvas 25x 7.7x
Seaport
Android 5.3x 3.2x
Canvas 8.3x 4.0x
TrainStation Android 5.9x 2.5x
dN ROAS Multiplier

28. But there’s one huge problem with ROAS multipliers
*okay, maybe more than one

29. Predicting the LTV
www.pixelfederation.com
Data behind UA
Doing UA the smart way

30. Predicting the LTV
www.pixelfederation.com
Data behind UA
Doing UA the smart way

31. Predicting the LTV
● Easy to calculate
○ only payment data needed
● Easy to implement
○ not so big data
● Easy to interpret
○ everyone understands $$$
● It works!
● Unstable over time
○ game design changes
○ optimization/network
● Inaccurate with small samples
○ only payment data used
● Predictions made on cohort level
○ not on player level
● Easily “fooled” by whales
● Unreliable with early predictions
Pros Cons
www.pixelfederation.com
Data behind UA
Doing UA the smart way

32. How it actually looks?

34. Campaign naming structure

35. Basic metrics

36. Overall and recent performance

38. Custom engagement metrics

39. New way of looking at the data

44. To summarize
● LTV > CAC is not everything!
● Give UA managers the tools they need
to make the right decisions
● Measuring LTV is kind of difficult
● Garbage in = garbage out
www.pixelfederation.com
Data behind UA

45. Time for Q&A
Michal P. Grno
mgrno@pixelfederation.com
https://www.linkedin.com/in/michalprokopgrno/