ssc case study competition: solving a puzzle with · pdf filessc case study competition:...
TRANSCRIPT
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Biostatistics Seminar Oct 7, 2014
Shahriar Shams, Changchang Xu
SSC Case Study Competition: Solving a Puzzle with Multiple Solutions
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SSC case study competition: FREE FOOD on top of knowledge transfer
Venue for 2015: Nova Scotia
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∗ Annual meeting of Statistical Society of Canada (SSC) ∗ Student conference and the main conference ∗ Student conference
∗ Keynote speaker, career panel, individual research poster presentation (eg. Your practicum work)
∗ Main conference ∗ Presentations by students and professionals (academia/industry) ∗ Case study competition
SSC conference
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∗ Two case studies ∗ Data will be uploaded on the SSC conference website ∗ Some research questions(3/4) will be given with each
dataset to keep uniform focus ∗ Final output is a poster (this year the max dim was 6’
wide and 3’ height) ∗ No restriction on group size ∗ Allowed to have faculty mentor (or Senior PhD
students as mentor)
Case study competition
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∗ http://www.ssc.ca/en/meetings/2014/case-studies
∗ Registration fees ($230 early bird)
∗ Cost of printing posters – ($60-$80)
∗ Reimbursement- Dr. Wendy Lou
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Identification of Factors Associated with Revenue from a
Social Mobile Game
Gabriel Lau, Lei Miao, Shahriah Shams, Kwong-Him To, Changchang
Xu, Ruoyong Xu Biostatistics Division, Dalla Lana School of Public Health,
University of Toronto
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∗ Social mobile game users: 300,000, behavioural characteristics ∗ Data cover Platform: which the players used for game playing Dates for reaching a stage & first purchase & first prize awarded & first git received: a comprehensive representation of the user’s progress in the game Revenue: amount which they paid for in-game purchases.
∗ Observation period: User behaviors were recorded for a
predefined period after game installation
Background on the social mobile game
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• Identifying the predictors of revenue
• Determining the best timing for introducing certain free in-game features which would lead to higher revenue
Objectives
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Methods
Data description Revenue: The revenue obtained from user scaled by an unknown constant Prize: The day of which the first prize was awarded:
I. Within 2 days of installation II. More than 2 days after installation III. No prize awarded
Purchase: The day of which the first in-game free purchase was made: I. On the day of installation II. After the day of installation
Games: The number of games played (1 unit =10 games) Platform: The use of hardware to access the game
I. One platform II. Two or more platform
Total number of items purchased during the observation period Facebook: Connected to Facebook
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Statistical Modelling Logistic regression model: predict whether a user would pay any amount Inverse Gaussian model: finding covariates associated with the increase in mean revenue
Methods (cont’d)
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EDA: revenue distribution on other variables
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EDA: revenue distribution on other variables
Num
ber o
f dis
tinc
t in-
gam
e pu
rcha
ses
Revenue Mean
Number of in-game purchases vs Revenue
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Results: Logistic Modelling
Parameters OR (95% CI) p-value
Games Played 1.018 ( 1.017 , 1.018) <0.0001 Date of first prize: Within 2 days of installation vs No prize 11.840 (10.892 , 12.871) <0.0001 Date of first prize: More than 2 days after installation vs No prize 9.775 ( 8.924 , 10.706) <0.0001 Number of platform: 2 or more vs 1 1.783 ( 1.603 , 1.982) <0.0001 Facebook connection: Connected vs Not connected 1.889 ( 1.777 , 2.009) <0.0001
Logit[P(revenue>0)] = β0 + β1*games + β2*prize1 + β3*prize2+ β4*platform + β5*facebook
• Number of games played, date of first prize, number of platforms and facebook connection all seem to play a significant role in predicting whether a game user would contribute to the revenue
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Results: Inverse Gaussian Modelling
Parameters Exp(Estimates) (95 % CI) p-value
Intercept 0.269 (0.240 , 0.302) <0.0001
Games 1.015 (1.013 , 1.017) <0.0001
First in-game purchase on the installation day 1.080 (1.006 , 1.158) 0.0328
First prize received within the first 2 days of installation 1.148 (1.045 , 1.261) 0.0039
First prize received 2 days after installation 0.988 (0.893 , 1.093) 0.8133
Total numbers of items purchased 1.422 (1.375 , 1.471) <0.0001
Log(revenue)] = β0 + β1*games + β2*purchase + β3*prize1+ β4*prize2 + β5*total
• Number of games played, date of first in-game purchase, date of first prize and total number of items purchased all seem to impact significantly on the increase of final revenue
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People who did not receive a prize were very unlikely to pay hence it was recommended that the game can give out more prizes in the beginning stages.
Facebook connection is associated to a great chance of paying.
People who installed the game on multiple platforms were more likely to pay, therefore this should be encouraged.
Generally, the earlier the user made free in-game purchases and received prizes, the higher the predicted revenue was received.
Discussions
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More demographic information was needed to really investigate the predictors leading to higher revenue.
It would be interesting to analyze age information.
The return player variable provided was not a good representation of retention because it was dictated by whether a user returned on one single day.
Discussions (cont’d)
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Further work could be done on the investigation of retention by creating a better representable and quantifiable variable.
Instead of modeling the aggregate revenue, the spending patterns of users over the observation period should be explored.
Future work
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∗ Dr. Wendy Lou ∗ Dr. Paul Corey ∗ Dr. Billy Chang
∗ And Changchang Xu for putting this presentation
together
Acknowledgement
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∗ Start a bit early (end of April is crazy for Masters students)
∗ Try to form a balanced group ∗ No need to focus on all the questions unless you
really want to ∗ Learn from your peers as much as you can
Experience from last year
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Thank you!