1. 3 COMMON MISTAKES WHEN
LOOKING AT FREEMIUM
METRICS
Sunday, 3 February 13

2. INTRODUCTION
• Product Lead at NaturalMotion
• Previously worked on:
stan.beremski@naturalmotion.com
Sunday, 3 February 13

3. OVERVIEW OF FREEMIUM
GAMES
Sunday, 3 February 13

4. THE FREEMIUM MODEL
App Store
Engage
Retain
Monetize + Invite
Sunday, 3 February 13

5. Good metrics give you insight into conversion at each stage of
the model
App Store
Engage
Retain
Monetise + Invite
Good metrics have explanatory powers
Sunday, 3 February 13

6. MISTAKE #1
NOT FOCUSING ON
EXPLANATORY METRICS
Sunday, 3 February 13

7. Vanity Explanatory
• Users • Total Revenue / Total Users • Cohort segmentation
• Page Views • Session per player / day • Retention
• Daily Revenue • Invites sent / player • LTV
• Total Mins of Play • Invites accepted / Invite • Sessions pp / day
• Total Sessions • ARPDAU
• DAU/MAU • Behavioural segmentation
• Whales vs Free
• Single player vs Multi
• Funnels
• Tutorial / Quest
• Virality funnel
Counts Ratios Segmented
Ratios
Sunday, 3 February 13

8. MISTAKE #2
NOT CONTROLLING FOR
VARIABLES
Sunday, 3 February 13

9. You have just released an update to your game and you take a look at the
metrics to gauge success...
• Did this update improve monetisation?
• How long did it take to us get a conclusive answer?
(Data is fictional)
Sunday, 3 February 13

10. There is a problem if you only look at New Users, Revenue and ARPDAU to
gauge the success of an update...
(Data is fictional)
Problem: Metrics can be affected by uncontrolled variables
• In our fictional game players spend 90% of their LTV within 7 days of first playing the app
• An large influx of new players will cause a revenue spike even if the app remains unchanged
Solution: Look at metrics which isolate what you are interested in and control
for other variables (in this case the player’s lifecycle within the app)
Sunday, 3 February 13

11. The solution is to look at player LTV at specific points in the player’s lifecycle
Total Revenue from cohort
Player LTV N days into
LTV = Day N LTV =
the lifetime of a cohort
Total # Player of a cohort
(Data is fictional)
Sunday, 3 February 13

12. MISTAKE #3
NOT LOOKING AT THE
DISTRIBUTION OF
UNDERLYING DATA
Sunday, 3 February 13

13. We often look at averaged data.
{1,2,3,3,3,4,4,5,10}
Mean = 3.89
Median = 3
Mode = 3
Sunday, 3 February 13

14. Power-law distribution are common in freemium games
Represents $65,000 revenue from110,000 players
Max: $624
Min: $0 (Data is fictional)
Mean: $0.57
Median: $0
Mode: $0
Std Dev: 7.12
% Payers: 5%
Sunday, 3 February 13

15. But statistics can be misleading...
Property Value
Mean
of
x
in
each
case 9
(exact)
Variance
of
x
in
each
case 11
(exact)
Mean
of
y
in
each
case 7.50
(to
2
decimal
places)
Variance
of
y
in
each
case 4.122
or
4.127
(to
3
decimal
places)
CorrelaAon
between
x
and
y
in
each
case 0.816
(to
3
decimal
places)
Linear
regression
line
in
each
case y
=
3.00
+
0.500x
(to
2
and
3
decimal
places,
respecAvely)
Sunday, 3 February 13