1. The document discusses A/B testing approaches for game design, noting key areas that can be tested like onboarding experiences, monetization strategies, and retention mechanics. 2. It introduces Bayesian approaches to A/B testing, noting that observing results allows updating beliefs about hypotheses rather than relying on passing a threshold for significance. 3. Key challenges with frequentist approaches are discussed like multiple comparisons inflating false positive rates, and "peeking" at intermediate results invalidating conclusions. Bayesian methods account for uncertainty and can incorporate prior information and new evidence iteratively.

1. Steve Collins
CTO / Swrve
A/B Testing for Game Design
Iteration: A Bayesian Approach

2. Biography

3. Push Notifications In-App MessagingA/B TestingTargeting & Analytics

4. Introduction to
A/B Testing

5. Game Service
Test
Dev
Update
Test
Dev
Update
Test
Dev
Update
Test
Dev
Update
Development
Beta
“Soft”
Launch
LAUNCH
Test
Dev
Launch

6. On-board
1st experience
Tutorial Flow
Incentives / Promo
Monetization
IAPs, virtual items
Promotions / offers / Ads
Economy / Game Balance
Retention
Messaging / Push Notify
Core loops, events
Community / Social
“Engagement”
Fun factor!
Content refresh / evolution
Level balance, progress
LTV (+$)
Acquisition
Ads (CPC, CPI)
Cross Promo
Discovery, PR
UAC (-$)

7. ROI = LTV - UAC

8. ROI UACLTV =
nX
x=1
ARPUx COSTx
(1 + WACC)x
CAC
ARPULTV =
nX
x=1
ARPUx COSTx
(1 + WACC)x
CACLTV =
nX
x=1
ARPUx COSTx
(1 + WACC)x
CAC
d
d
lifetime?
LTV
{

9. 1" 2" 3" 4" 5" 6" 7" 8" 9" 10" 11" 12" 13" 14" 15" 16" 17" 18" 19" 20" 21" 22" 23" 24" 25" 26" 27" 28" 29" 30" 31" 32"
High Mean Low
Install-day-cohort 30-day revenue

10. Test%Hypotheses%
Data$driven$
Take%ac'on%
Iterate,'fail+fast'
Understand)
Metrics()>(Analy0cs()>(Insight(

11. 1. Split population 2. Show variations 4. Choose winner 4. Deploy winner3. Measure response

12. State
Sync State
Sync
Game
State
DB
Data
Warehouse
Analyse
Events
(tagged
with
A
or
B)
Game
Server
Content
Management
System
A
or
B
variant
A/B
Test
Server

13. What to test?

14. vs
Message layouts / content

15. choke point
Tutorial Flow

16. vs
25 25
-20%-40%
Promotion Discounts

17. Elasticity testing: exchange rate
$4.99 $4.99
100 coins
A B
200 coins
vs

18. Price set A
Price set B
Price set C
Store Inventory

19. (f) Zoomed in version of the previous scatterplot. Note that jitter and transparency
Value of first purchase
Totalspend
$70$30$15
A/B Test
VIPs
Repeat
Purchases
$5$2

20. Timing
Wait Sessions
(3)
Wait Event
(movie_closed)
In-app Msg
"Request Facebook
Registration"
FB Reg
when where

21. 0 10000 20000 30000 40000 50000
0.00.20.40.60.81.0
number of user trials
Probabilityofwinning

23. Canacandycrushbalt*
Day 1 retention = 30%
* Apologies to King and Adam Saltsman

24. Beta Test
Original New Version

25. Expecting 30% day-1 retention
After 50 users, we see 0%
Is this bad?

26. Null Hypothesis Testing
(NHT) View

27. The Null Hypothesis
) =
1
p
2⇡ 2
e
(x µ)2)
2 2
H0 : x ! µ
H0 : x ! µ
Accept
Reject
H0 : x = µ
30%

28. ) =
1
p
2⇡ 2
e
(x µ)2)
2 2Reject
0%
30%

29. Conclusion: 30% is unlikely to be the
retention rate

30. Issue #1
p-value

31. p-Value
95%
2.5% 2.5%
=
1
p
2
e
(x µ)2)
2 2
(n − r)!r!
µ = np σ = np(1 − p)
z =
¯x − µ
σ
z =
¯x − µ
ˆσ√
n
SE(¯x) =
ˆσ
√
n
n =
(zα + zβ)2
σ2
δ2
µ + 1.96σ
µ − 1.96σ
z =
¯x − µ
σ
z =
¯x − µ
ˆσ√
n
SE(¯x) =
ˆσ
√
n
n =
(zα + zβ)2
σ2
δ2
µ + 1.96σ
µ − 1.96σ

32. p-Value
The probability of observing as extreme a result
assuming the null hypothesis is true
The probability of the data given the model
OR

33. Null Hypothesis: H0
H H
Accept H Type-II Error
Reject H Type-I Error
Truth
Observation
False Positive

34. All we can ever say is either
not enough evidence that retention rates are the same
the retention rates are different, 95% of the time
p < 0.05
p-Value

35. actually...
The evidence supports a rejection of the null hypothesis, i.e.
the probability of seeing a result as extreme as this, assuming
the retention rate is actually 30%, is less than 5%.
p < 0.05

36. Issue #2
“Peeking”

37. Required change
Standard deviation
Number of
participants
per group n ≈
16σ 2
δ2

38. Peeks 5% Equivalent
1 2.9%
2 2.2%
3 1.8%
5 1.4%
10 1%
To get 5% false positive rate you need...
i.e. 5 times
http://www.evanmiller.org/how-­‐not-­‐to-­‐run-­‐an-­‐ab-­‐test.html

39. Issue #3
Family-wise Error

40. Family-wise Error
p = P( Type-I Error) = 0.05
5% of the time we will get a false positive - for one treatment
P( no Type-I Error) = 0.95
P( at least 1 Type-I Error for 2 treatments) = (1 - 0.9025) = 0.0975
P( no Type-I Error for 2 treatments) = (0.95)(0.95) = 0.9025

41. Bayesian View

42. 0.0 0.2 0.4 0.6 0.8 1.0
0246810
Retention
Density
Expected retention rate
~30%
“Belief”
uncertainty

43. 0.0 0.2 0.4 0.6 0.8 1.0
0246810
Retention
Density
~15%
New “belief” after 0 retained users
Stronger belief after
observing evidence

44. The probability of the model given the data

45. p(heads) = p(tails) = 0.5

46. Tossing the Coin
0 10 20 30 40 50
0.00.20.40.60.81.0
Number of flips
ProportionofHeads
Heads
Tails
THTHTTTHHTHH…

47. 1 5 10 50 500
0.00.20.40.60.81.0
Number of flips
ProportionofHeads Tossing the Coin
Long run average = 0.5

48. p(y|x) =
p(y, x)
p(x)
p(x|y) =
p(x, y)
p(y)
p(y, x) = p(x, y) = p(y|x)p(x) = p(x|y)p(y)
p(y|x) =
p(x|y)p(y)
p(x)
p(y|x) =
p(x|y)p(y)
P
Probability of x
p(y|x) =
p(y, x)
p(x)
p(x|y) =
p(x, y)
p(y)
p(y, x) = p(x, y) = p(y|x)p(x) = p(x|y)p(y)
p(y|x) =
p(x|y)p(y)
p(x)
Probability of x and y
(conjoint)
p(y|x) =
p(y, x)
p(x)
p(x|y) =
p(x, y)
p(y)
p(y, x) = p(x, y) = p(y|x)p(x) = p(x|y)p
p(x|y)p(y)
Probability of x given y
(conditional)
Terminology

49. The Bernoulli distribution
For a “fair” coin, θ = 0.5
Head (H) = 1, Tails (T) = 0
p(x|θ) = θx(1 − θ)(1−x)
A single toss:
p(x|θ) = θx(1 − θ)(1−x)
p(heads = 1|0.5) = 0.51(1−0.5)(1−1) = 0.5
0 (1−0)
p(x|θ) = θx(1 − θ)(1−x)
p(heads = 1|0.5) = 0.51(1−0.5)(1−1) = 0.5
p(tails = 0|0.5) = 0.50(1−0.5)(1−0) = 0.5

50. The Binomial
p(x|θ) = θx(1 − θ)(1−x)
Probability of heads in a single throw:
Probability of x heads in n throws:
p(heads = 1|0.5) = 0.51
(1 − 0.5)(1−1)
= 0.5
p(tails = 0|0.5) = 0.50
(1 − 0.5)(1−0)
= 0.5
p(h|n, θ) =
n
h
θh
(1 − θ)(n−h)
p(x|θ, n) =
n
x
θx
(1 − θ)(n−x)

51. 20 tosses of a fair coin
=
1
p
2⇡ 2
e
(x µ)2)
2 2
1 − θ)(n−x)p(θ)dθ
pc − pv
1−pc)+pv(1−pv)
n
p(x|0.5, 20)
p(x|θ)p(θ)
x
(1 − θ)(1−x)
0.51
(1 − 0.5)(1−1)
= 0.5

52. 20 tosses of an “un-fair” coin
1
p
2⇡ 2
e
(x µ)2)
2 2
θx(1 − θ)(n−x)p(θ)dθ
ztest =
pc − pv
pc(1−pc)+pv(1−pv)
n
p(x|0.2, 20) p(x|0.5, 20)
p(¯θ) = p(x|θ)p(θ)
p(x|θ) = θx
(1 − θ)(1−x)
s = 1|0.5) = 0.51
(1 − 0.5)(1−1)
= 0.5

53. Likelihood of θ given observation i of x heads in n throws:
“Binomial Likelihood”
h
p(x|θ, n) =
n
x
θx
(1 − θ)(n−x)
1
p(h|n, θ) =
n
h
θh
(1 − θ)(n−h)
p(x|θ, n) =
n
x
θx
(1 − θ)(n−x)
L(θ|xi, ni) =
ni
xi
θxi
(1 − θ)(ni−xi)

54. The Likelihood
Increasing likelihood of θ with more observations…
0.0 0.2 0.4 0.6 0.8 1.0
0.00.20.4
1 heads in 2 throws.
θ
Likelihood
0.0 0.2 0.4 0.6 0.8 1.0
0.000.100.20
5 heads in 10 throws.
θ
Likelihood
0.0 0.2 0.4 0.6 0.8 1.0
0.0000.0100.020
500 heads in 1000 throws.
θ
Likelihood

55. p(¯θ) = p(x|θ)p(θ)
p(x|θ) = θx
(1 − θ)(1−x)
p(heads = 1|0.5) = 0.51
(1 − 0.5)(1−1)
= 0.5
p(tails = 0|0.5) = 0.50
(1 − 0.5)(1−0)
= 0.5
The observations (#heads)
p(x|0.2, 20) p(x|0.5, 20)
p(¯θ) = p(x|θ)p(θ)
p(x|θ) = θx
(1 − θ)(1−x)
p(heads = 1|0.5) = 0.51
(1 − 0.5)(1−1)
= 0.5
p(tails = 0|0.5) = 0.50
(1 − 0.5)(1−0)
= 0.5
The model parameter (e.g. fair coin)
p(x|θ, n) = θx
(1 − θ)(n−x)
B(a, b) =
Γ(a)Γ(b)
Γ(a + b)
=
(a − 1)!(b − 1)!
(a + b − 1)!
beta(θ|a, b) = θ(a−1)
(1 − θ)(b−1)
/ B(a, b)
p(θ|x) = θx
(1 − θ)(n−x)
p(θ) / p(x)We want to know this
ztest =
c v
pc(1−pc)+pv(1−pv)
n
p(x|0.2, 20) p(x|0.5, 20)
p(¯θ) = p(x|θ)p(θ)
p(x|θ) = θx
(1 − θ)(1−x)
p(heads = 1|0.5) = 0.51
(1 − 0.5)(1−1)
= 0.5
Probability of data given model
A recap…

56. Note that
p(x, y) = p(x|y)p(y) = p(y|x)p(x)
p(x|θ) ̸= p(θ|x)
p(cloudy|raining) ̸= p(raining|cloudy)
p(θ|x, n) = θx
(1 − θ)(n−x)
θ(a−1)
(1 − θ)(b−1)
/ B(a, b) p(
p(θ|x, n) = θx+a−1
(1 − θ)(n−x+b−1)
/ B(x + a, n − x + b

57. p(y|x) = p(y, x)p(x)
p(x|y) = p(x, y)p(y)
p(x, y) =
p(x|y)
p(y)
=
p(y|x)
p(x)
p(x|y)p(y) = p(y|x)p(x)
p(y|x) =
p(x|y)p(y)
p(x)
p(x, y) = p(x|y)p(y) = p(y|x)p(x)
p(x|y) ̸= p(y|x)
p(cloudy|raining) ̸= p(raining|cloudy)
(θ|x, n) = θx
(1 − θ)(n−x)
θ(a−1)
(1 − θ)(b−1)
/ B(a, b) p(x
x+a−1 (n−x+b−1)
Bayes’ Rule
p(x, y) = p(x|y)p(y) = p(y|x)p(x
p(x) =
y
p(x|y)p(y)
p(x|θ) ̸= p(θ|x)

58. p(y|x) =
p(x|y)p(y)
P
y p(x|y)p(y)
discrete form
p(x|y) =
p(y)
p(y, x) = p(x, y) = p(y|x)p(x
p(y|x) =
p(x|y)p(y)
p(x)
p(y|x) =
p(x|y)p(y)
R
p(x|y)p(y)dy
continuous form

59. p(x, n|θ) = θx(1 − θ)(n−x)
ztest =
pc − pv
pc(1−pc)+pv(1−pv)
n
p(h, 20|0.2)
p(x)
p(✓|x)
| {z }
= p(x|✓)
| {z }
p(✓)
|{z}
/ p(x)
|{z}
p(x) =
Z
p(x|✓)p(✓)d✓
prob of model
prob #heads given model
p(✓|x)
| {z }
= p(x|✓)
| {z }
p(✓)
|{z}
/ p(x)
|{z}
p(x) =
Z
p(x|✓)p(✓)d✓
p(✓|x)
| {z }
= p(x|✓)
| {z }
p(✓)
|{z}
/ p(x)
|{z}
p(x) =
Z
p(x|✓)p(✓)d✓
prob model given #heads
p(x)
p(x|y) =
p(x, y)
p(y)
p(y, x) = p(x, y) = p(y|x)p(x) = p(x|y)p(y)
p(y|x) =
p(x|y)p(y)
p(x)
p(y|x) =
p(x|y)p(y)
R
p(x|y)p(y)dy

60. p(✓|x)
| {z }
= p(x|✓)
| {z }
p(✓)
|{z}
/ p(x)
|{z}
p(x) =
Z
p(x|✓)p(✓)d✓
posterior likelihood prior normalizing
factor
p(✓|x)
| {z }
= p(x|✓)
| {z }
p(✓)
|{z}
/ p(x)
|{z}
p(x) =
Z
p(x|✓)p(✓)d✓normalizing factor

61. p(✓|x)
| {z }
= p(x|✓)
| {z }
p(✓)
|{z}
/ p(x)
|{z}
p(x) =
Z
p(x|✓)p(✓)d✓
The prior
Captures our “belief” in the model
based on prior experience, observations or knowledge

62. Best estimate so far
Iterations with more data…
p(θ|x) = p(x|θ) p(θ) / p(x)
p(θ|x) =
θx
(1 − θ)(n−x)
p(θ)
θx(1 − θ)(n−x)p(θ)dθ
ztest =
pc − pv
pc(1−pc)+pv(1−pv)
n
p(h, 20|0.2)
p(x, n|θ) = θ (1 − θ)
p(θ|x) = θx
(1 − θ)(n−x)
p(θ) / p(x)
ˆp0(θ|x0) = p(x0|θ) p(θ) / p(x0)
ˆp1(θ|x1) = p(x1|θ) ˆp0(θ) / ˆp0(x1)
ˆpn(θ|xn) = p(xn|θ) ˆpn−1(θ) / ˆpn−1(xn)
x (n−x)
p(θ|x) = θx
(1 − θ)(n−x)
p(θ) / p(x)
ˆp0(θ|x0) = p(x0|θ) p(θ) / p(x0)
ˆp1(θ|x1) = p(x1|θ) ˆp0(θ) / ˆp0(x1)
ˆpn(θ|xn) = p(xn|θ) ˆpn−1(θ) / ˆpn−1(xn)
p(θ|x) =
θx
(1 − θ)(n−x)
p(θ)
p(θ|x) = θx
(1 − θ)(n−x)
p(θ) / p(x)
ˆp0(θ|x0) = p(x0|θ) p(θ) / p(x0)
ˆp1(θ|x1) = p(x1|θ) ˆp0(θ) / ˆp0(x1)
ˆpn(θ|xn) = p(xn|θ) ˆpn−1(θ) / ˆpn−1(xn)
p(θ|x) =
θx
(1 − θ)(n−x)
p(θ)
x (n−x)

63. Selecting a prior
We’d like the product of prior and likelihood
to be “like” the likelihood
We’d like the integral to be easily evaluated
p(x, n|θ) = θx
(1 − θ)(n−x)
p(θ|x) = θx
(1 − θ)(n−x)
p(θ) / p(x)
p(θ|x) =
θx
(1 − θ)(n−x)
p(θ)
θx(1 − θ)(n−x)p(θ)dθ
ztest =
pc − pv
pc(1−pc)+pv(1−pv)
n
p(h|n, θ) =
n
h
θh
(1 − θ)(n−h)
p(x|θ, n) =
n
x
θx
(1 − θ)(n−x)
1

64. “Conjugate prior”
ztest =
pc(1−pc)+pv(1−pv)
n
p(h, 20|0.2)
p(¯θ) = p(x|θ)p(θ)
p(x|θ) = θx
(1 − θ)(1−x)

65. Beta distributionp(x, n|θ) = θx
(1 − θ)(n−x)
beta(θ|a, b) = θ(a−1)
(1 − θ)(b−1)
/ B(a, b)
p(θ|x) = θx
(1 − θ)(n−x)
p(θ) / p(x)
ˆp0(θ|x0) = p(x0|θ) p(θ) / p(x0)
ˆp1(θ|x1) = p(x1|θ) ˆp0(θ) / ˆp0(x1)
number of heads + 1 number of tails + 1
p(θ|x, n) = θx
(1 − θ)(n−x)
θ(a−1)
(1 − θ)(b−1)
/ B(a, b)
p(θ|x, n) = θx+a−1
(1 − θ)(n−x+b−1)
/ B(x + a, n − x
p(x, n|θ) = θx
(1 − θ)(n−x)
B(a, b) =
Γ(a)Γ(b)
Γ(a + b)
=
(a − 1)!(b − 1)!
(a + b − 1)!

66. Beta distribution

67. beta priorbinomial likelihood
Putting it together…
p(θ|x, n) = θx
(1 − θ)(n−x)
θ(a−1)
(1 − θ)(b−1)
/ B(a, b) p(x, n)
p(θ|x, n) = θx+a−1
(1 − θ)(n−x+b−1)
/ B(x + a, n − x + b)
p(x, n|θ) = θx
(1 − θ)(n−x)
number of heads x number of tails (n-x)
p(x|y) ̸= p(y|x)
p(cloudy|raining) ̸= p(raining|cloudy)
p(θ|x, n) = θx
(1 − θ)(n−x)
θ(a−1)
(1 − θ)(b−1)
/ B(a, b) p(x)
p(θ|x, n) = θx+a−1
(1 − θ)(n−x+b−1)
/ B(x + a, n − x + b)
p(θa > θb) =
θa>θb
p(θa|xa)p(θb|xb) dθadθb

68. 1. Decide on a prior, which captures your belief
2. Run experiment and observe data for heads, tails
3. Determine your posterior based on the data
4. Use posterior as your new belief and re-run
experiment
5. Rinse, repeat until you hit an actionable certainty
Putting it together…

70. Uniform prior
“Fair” coin
Pretty sure coin is fair

71. “Coin is fair” prior
“Fair” coin
Very sure coin is fair

72. Uniform prior
“Biased” coin
Pretty sure coin is unfair

73. “Coin is fair” prior
“biased” coin
Not sure of anything yet!

74. When to reject?

75. The Credible Interval
Uniform prior
“Fair” coin
95% credible interval

76. The Credible Interval
Uniform prior
“Biased” coin
Outside credible interval

77. Captures our prior belief, expertise, opinion
Strong prior belief means:
‣ we need lots of evidence to contradict
‣ results converge more quickly (if prior is “relevant”)
Provides inertia
With enough samples, prior’s impact diminishes,
rapidly
The Prior

78. Running a test…
A B

79. With 1 or more variants we have a multi-dimensional
problem
Need to evaluate volumes under the posterior
In general requires numerical quadrature = Markov
Chain Monte-Carlo (MCMC)
Multiple variant tests

80. p(θ|x, n) = θx
(1 − θ)(n−x)
θ(a−1)
p(θ|x, n) = θx+a−1
(1 − θ)(n−x+
p(θa > θb) =
θa>θb
p(θ
p(θ|x, n) = θx
(1 − θ)(n−x)
θ(a−1)
(1 − θ)
p(θ|x, n) = θx+a−1
(1 − θ)(n−x+b−1)
/
p(θa > θb) =
θ >θ
p(θa|xa)p(

81. Probability of Winning
|x, n) = θx
(1 − θ)(n−x)
θ(a−1)
(1 − θ)(b−1)
/ B(a, b) B(x
p(θ|x, n) = θx+a−1
(1 − θ)(n−x+b−1)
/ B(x + a, n − x + b
p(θa > θb) =
θa>θb
p(θa|xa)p(θb|xb) dθadθb
p(x|θ, n) = θx
(1 − θ)(n−x)
B(a, b) =
Γ(a)Γ(b)
Γ(a + b)
=
(a − 1)!(b − 1)!
(a + b − 1)!

82. p(θ|x, n) = θx
(1 − θ)(n−x)
θ(a−1)
(1 − θ)
p(θ|x, n) = θx+a−1
(1 − θ)(n−x+b−1)
/
p(θa > θb) =
θa>θb
p(θa|xa)p(θ

83. What’s the prior?

84. Fit a beta
a=42, b=94

86. Some examples...

87. A successful test

88. Probability of beating all

89. Observed conversion rates (with CI bounds)

90. A not so successful test...

91. Probability of beating all

92. Observed conversion rate (posterior)

93. Assumptions
Users are independent
User’s convert quickly (immediately)
Probability of conversion is independent of time

94. Un-converged conversion rate

95. Benefits / Features
Continuously observable
No need to fix population size in advance
Incorporate prior knowledge / expertise
Result is a “true” probability
A measure of the difference magnitude is given
Consistent framework for lots of different scenarios

96. Useful Links
• h#ps://github.com/CamDavidsonPilon/Probabilistic-­‐‑Programming-­‐‑
and-­‐‑Bayesian-­‐‑Methods-­‐‑for-­‐‑Hackers
• “Doing Bayesian Data Analysis: A Tutorial with R and Bugs”, John K.
Kruschke
• h#p://www.evanmiller.org/how-­‐‑not-­‐‑to-­‐‑run-­‐‑an-­‐‑ab-­‐‑test.html
• h#p://www.kaushik.net -­‐‑ Occam’s Razor Blog
• h#p://exp-­‐‑platform.com -­‐‑ Ron Kovahi et al.

97. Thanks

98. Multi-arm bandits
5% 6% 10% 4%
Pull 1
Pull 2
Pull 3

99. Multi-arm bandits
Draw sample from
each "arm" prior
distribution
Evaluate Success
Update posterior for
chosen "arm"
Pick largest & deploy
Thompson Sampling
Thompson, William R. "On the likelihood that one unknown probability exceeds another in view of the evidence of two samples". Biometrika, 25(3–4):285–294

100. https://github.com/CamDavidsonPilon/Probabilistic-­‐Programming-­‐and-­‐Bayesian-­‐Methods-­‐for-­‐Hackers

101. Difficulty tuning
a slight silly example…
Canacandycrushbalt

102. Winner