Data Scientists Need not apply
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Transcript of Data Scientists Need not apply
Data scientists need not apply:How anyone can do game analysis
Casual Connect, November 2014
@allisonbilas | [email protected]
Descriptive Exploratory Inferential Predictive Causal
Quantitatively describing data
Looking for previously unknown
relationships in data
Testing theories with a sample of
data
Analyzing current events to predict
future events
Measuring what happens to one
variable when you change another
Distribution; 5-number summary;
Before/after
Directionality; Visualizations
Regression models; Chi squared
Modeling, machine learning, data
miningA/B testing
OBSERVE EXPERIMENT
Descriptive Exploratory Inferential Predictive Causal
Quantitatively describing data
Looking for previously unknown
relationships in data
Testing theories with a sample of
data
Analyzing current events to predict
future events
Measuring what happens to one
variable when you change another
Distribution; 5-number summary;
Before/after
Directionality; Visualizations
Regression models; Chi squared
Modeling, machine learning, data
miningA/B testing
“EASY”
‣ Quantitatively describing data
‣ Distribution of data; 5-number summary; Outliers
Score Percentiles by Boost Used
0
28
56
84
112
140
10. 25. Median 75. 90. 99.
Valentine WinterPrism RubyBlaze BurstStone No Boost
Descriptive
‣ Looking for previously unknown relationships in data
‣ Visualizations are key component
ExploratoryMonetizers by Amount Spent per Customer
0
1,000
2,000
3,000
4,000
$0 - $5 $5 -$10 $10 - $25 $25 - $60 $60 - $280 $280 - $2975
Total Bookings by Amount Spent per Customer
$0
$25,000
$50,000
$75,000
$100,000
$0 - $5 $5 -$10 $10 - $25 $25 - $60 $60 - $280 $280 - $2975
4% 6%
16%
22%
35%
17%
34%
18%24%
15%
9%
1%
Three Big Questions
INSTALLS How many people have installed my
game?
DAU How many people are
playing my game?
REVENUE How much money is my game generating?
Acquisition Engagement Monetization
Revenue declined by $2K (-5%) week-over-week after the re-engagement campaign ended.
This was caused by a 3% decline in DAU, which in turn reduced the number of monetizers by 3%.
Those that did spend continued to spend at the same ARPPU of $7.29.
Reporting Analysis
DAU = 1.25M
Revenue = $41.4K
ARPPU = $7.29
Conversion = 0.45%
Revenue declined by $2K (-5%) week-over-week after the re-engagement campaign ended.
This was caused by a 3% decline in DAU, which in turn reduced the number of monetizers by 3%.
Those that did spend continued to spend at the same ARPPU of $7.29.
Reporting Analysis
DAU = 1.25M
Revenue = $41.4K
ARPPU = $7.29
Conversion = 0.45%
Revenue declined by $2K (-5%) week-over-week after the re-engagement campaign ended.
This was caused by a 3% decline in DAU, which in turn reduced the number of monetizers by 3%.
Those that did spend continued to spend at the same ARPPU of $7.29.
Reporting Analysis
DAU = 1.25M
Revenue = $41.4K
ARPPU = $7.29
Conversion = 0.45%
Revenue declined by $2K (-5%) week-over-week after the re-engagement campaign ended.
This was caused by a 3% decline in DAU, which in turn reduced the number of monetizers by 3%.
Those that did spend continued to spend at the same ARPPU of $7.29.
Reporting Analysis
DAU = 1.25M
Revenue = $41.4K
ARPPU = $7.29
Conversion = 0.45%
DAU = 1.25M
Revenue = $41.4K
ARPPU = $7.29
Conversion = 0.45%
Revenue declined by $2K (-5%) week-over-week after the re-engagement campaign ended.
This was caused by a 3% decline in DAU, which in turn reduced the number of monetizers by 3%.
Those that did spend continued to spend at the same ARPPU of $7.29.
Reporting Analysis
‣ Continue to use push notifications to re-engage users and support DAU
‣ Prioritize dev work for daily prizes to increase return rates
‣ Consider creating a high priced gem package to increase ARPPU
Recommendations
Allison Bilas, VP of Product
@allisonbilas
Additional Reading: https://bitly.com/bundles/sparklingallison/
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