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Title: Case Study Competition 8mm Analysis of a Uken Games mobile app -5mm
Author: Nathan Esau, Steve Kane

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Nathan Esau, Steve Kane
September 2015


Case Study Competition
Analysis of a Uken Games mobile app

Figure 3: Stages played, engagement, revenue, player skill (bonuses completed)

Figure 4: Heatmap for distribution of players from around the world

1. Come up with a good way to visualize the
data that helps to provide explanatory
2. Use the demographic and event features to
predict revenue, engagement and player
3. Suggest control-treatment experiments for
follow up analysis



• We

used the random forest model to predict revenue and engagement since this
type of model works well with skewed data (Huang, 2005).
• The gradient boosted model was used to predict retention. This model performed
better than linear models we tried.

Size training set:
250,000 players
Size validation set: 50,000 players
Types of variables: Country, gender, dates
of in-game events, ingame purchases, prizes
won; about 40 variables

Table 1: Comparison of prediction mean and training mean


Revenue measures the amount of money spent,
while Engagement measures the amount of time
spent in game. Retention indicates whether a
player plays the game 30 days after installation.
• Revenue

and engagement are heavily skewed
• 97.8% of players don’t spend money
• Given that players pay, the average is $3.33 with
only 5% paying more than $13.20
• The average time spent playing is 32.75, but the
median time spent playing is only 7.
• Retention: 9% of players returned 30 days later

Engagement Return Player

Validation Mean 3.31 | Revenue > 0 33.33


3.33 | Revenue > 0 32.75


Training Mean

Note that the random forest model predicts 0 revenue for most of the new players,
which is what we would expect.

Random Forest Model

Gradient Boosted Model

Figure 5: Simple decision tree

Figure 7: Error-complexity tradeoff

A/B tests to improve key metrics
• Figure

3 shows that completing bonus objectives is
likely to increase revenue. We expect that creating
additional stages or adjusting the difficulty of the
bonus objective could increase revenue.
• It would be interesting to analyze the impact of
providing unlockable features when a player the
game connects to Facebook or another device.

Figure 1: Important features for predicting revenue

Relationship of key metrics

Figure 6: A random forest
Figure 2: Important features for predicting engagement

• Stage

Count is highly significant to both
We update our model, F (x) = 0.5 log
engagement and revenue
by continually adding a new basis function to
• The types of games a player tries out is highly
minimize the loss function,
l(yi, yˆi) = yi ln(1 + e ) + (1 − yi) ln(1 + e )
• The type of purchase a player made is very
The gradient boosted model uses a greedy algorithm. significant to revenue


We create an ensemble of many unstable models to
form a stable model.

Analysis was done using R. The ggplot2, randomForest, data.table, xgboost, readr,
qdapTools, and Matrix packages were used. For the models see Methods to Extract Rare Events by Weihua Huang. 2005.

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