Aeria Games and Data driven decision making: Avoiding pitfalls and building value

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In this presentation, COO of Aeria Games, Dr. Stefan Behrens took a look at how using metrics in making operational and corporate decisions has built significant value for Aeria Games. Careful interpretation of the data is needed for success and this presentation will show what Aeria Games has learned in the past, and how these lessons have helped to build a successful company based on data analysis.

Transcript

Data driven decision making: Avoiding pitfalls and building value08.04.2012, Quo Vadis, Dr. Stefan Behrens

1

Aeria Games and ProSiebenSat.1 Games have combined forces on April 1, 2014

Industry leading monetization

Unparalleled marketing power

Strong developer relationships

Best-in-class operations

Global communities & reach

Top 3 player in

Europe

&

2

Some facts on the combined business

Registered Users

Countries Serviced

Game Licenses Operated

Mobile Share of Revenues

35

77m

16%

39

&

3

Data driven decision-making effectively drives almost all aspects of our business at Aeria

Talent Product Acquisition Retention Monetization

Values

4

At Aeria, decision making starts with strong values

VALUES

5

Hiring the right talent is the basis for success

• Don’t hire (only) gamers

TALENT

• Put them through „boot camp“

• Let them crunch numbers

• Keep setting the bar higher & higher

6

Picking the right games is not an easy feat

PRODUCT

Target Group Fit

Committed Developer

Proven Success

Adequate Art Style

7

Lifetime Value

(365d ARPU)

Customer Acquisition Funnel

Buy & SpendAeria Points

(7d Conversion)

Return toPlay

(3d Retention)

GameLogin

(Reg-to-Play)

Landing PageRegistration

(Click-to-Reg)

Click onAd Banner

(CTR)

CTR: 0.23%

Clicks: 107,000

Click-to-Reg: 5.6%

Regs: 6,000

Reg-to-Play 33%

Players: 2,000

3d Retention: 45%

Retained: 900

7d Conversion: 8.5%

Spenders: 170 $14.09

Max CPC:

0.13 USD

Max CPL:

2.35 USD

Max CPP:

7.05 USD

Marketing: Maximum Customer Cost Calculation

Successful acquisition is about balancing a simple, but fundamental equation...

ACQUISITION

8

…but the equation can get mighty complex

$-

$5

$10

$15

$20

$25

Jan-

12

Feb-

12

Mar

-12

Apr

-12

May

-12

Jun-

12

Jul-1

2

Aug

-12

Sep

-12

Oct

-12

Nov

-12

Dec

-12

Jan-

13

Feb-

13

Mar

-13

Apr

-13

May

-13

30d LTV (a) 90d LTV (a)

1yr LTV (a) 1yr LTV (e)

Actual Predicted

Predictive LTV ModelingMulti-Channel Optimization

Search22%

Display/AdNetworks

38%

Branding1%

Facebook4%

PR5%

TV30%

ACQUISITION

9

Retention rates can tell you a lot…

0%

10%

20%

30%

40%

50%

60%

70%

29

.09

.0

2.1

0.

05

.10

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8.1

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11

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4.1

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17

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05

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2.

11

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4.1

2.

RR 3 days organic RR 3 days paid

Game Case: 3 Day Retention Rate (RR)

RETENTION

10

…if you know how to read them

0%

10%

20%

30%

40%

50%

60%

70%

29

.09

.0

2.1

0.

05

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11

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05

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2.

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4.1

2.

RR 3 days organic RR 3 days paid

Game Case: 3 Day Retention Rate (RR)

RETENTION

Technical issuewith a patch

Secondary accountsof existing users

11

However, sometimes, the answers areless than obvious

30%

35%

40%

45%

50%

55%

60%

6.1

.20

12

6.3

.20

12

6.5

.20

12

6.7

.201

2

6.9

.20

12

6.1

1.2

01

2

6.1

3.20

12

6.1

5.20

12

6.1

7.20

12

6.1

9.20

12

6.2

1.20

12

6.2

3.20

12

6.2

5.20

12

6.2

7.20

12

6.2

9.20

12

7.1

.20

12

7.3

.20

12

7.5

.20

12

7.7

.20

12

7.9

.20

12

7.1

1.20

12

7.1

3.20

12

7.1

5.20

12

Germany

UK

Game Case: 3 Day Retention Rate

RETENTION

12

Especially, when soccer is involved

30%

35%

40%

45%

50%

55%

60%

6.1

.20

12

6.3

.20

12

6.5

.20

12

6.7

.201

2

6.9

.20

12

6.1

1.2

01

2

6.1

3.20

12

6.1

5.20

12

6.1

7.20

12

6.1

9.20

12

6.2

1.20

12

6.2

3.20

12

6.2

5.20

12

6.2

7.20

12

6.2

9.20

12

7.1

.20

12

7.3

.20

12

7.5

.20

12

7.7

.20

12

7.9

.20

12

7.1

1.20

12

7.1

3.20

12

7.1

5.20

12

Germany

UKEuro Cup!

Game Case: 3 Day Retention Rate

RETENTION

13

Good sales strategy is fueled by analytics

Monthly Product Sales PlanContinuous KPI Analysis

1[0-5)

2[5-50)

3[50-100)4[100-200)5[200-400)6[400+)

0.00%

10.00%

20.00%

30.00%

40.00%

50.00%

60.00%

70.00%

0None

1[0-5)

2[5-50)

3[50-100)

4[100-200)

5[200-400)

6[400+)

1[0-5)

2[5-50)

3[50-100)

4[100-200)

5[200-400)

6[400+)

Item Saturation

TS Redemption

CCU

Spender Buckets

Retention

MONETIZATION

14

Season #1 Price: $12 per spin No. of Spins: 16 No. of Jackpots: 1,500

Season #2 Price: $25 per spin No. of Spins: 14 Number of Jackpots 1000 Optimized composition of weapons

Season #3 Price: $20 per spin No. of Spins: 20 Number of Jackpots 500+500 Optimized weapons and Jackpot

Shooter Game Case, Revenue 2013 Jun-Dec Event Optimization

EventSeason #1

EventSeason #2

EventSeason #3

Execute, analyze, optimize for best results

Jun Jul NovOctSep DecAug

MONETIZATION

15

Beware… It’s a trap! (at least sometimes)

Data consistency can be a b*tch

Correlation ≠ Causation

What get’s measured can be improved,but what about intangibles?

You can plan a pretty picnic,but you can’t predict the weather!

PITFALLS

Data driven decision making: Avoiding pitfalls and building value08.04.2014, Quo Vadis, Dr. Stefan Behrens

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