SSC Case Study Competition: Solving a Puzzle with Multiple Solutions

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Jane Morrison
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1 SSC Case Study Competition: Solving a Puzzle with Multiple Solutions Biostatistics Seminar Oct 7, 2014 Shahriar Shams, Changchang Xu

2 SSC case study competition: FREE FOOD on top of knowledge transfer Venue for 2015: Nova Scotia

3 SSC conference 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

4 Case study competition 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)

5 Registration fees ($230 early bird) Cost of printing posters ($60-$80) Reimbursement- Dr. Wendy Lou

6 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

7 Background on the social mobile game 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

8 Objectives Identifying the predictors of revenue Determining the best timing for introducing certain free in-game features which would lead to higher revenue

9 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

10 Methods (cont d) 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

11 EDA: revenue distribution on other variables

12 EDA: revenue distribution on other variables Number of in-game purchases vs Revenue Number of distinct in-game purchases Revenue Mean

13 Results: Logistic Modelling Logit[P(revenue>0)] = β 0 + β 1 *games + β 2 *prize1 + β 3 *prize2+ β 4 *platform + β 5 *facebook Parameters OR (95% CI) p-value Games Played ( 1.017, 1.018) < Date of first prize: Within 2 days of installation vs No prize (10.892, ) < Date of first prize: More than 2 days after installation vs No prize ( 8.924, ) < Number of platform: 2 or more vs ( 1.603, 1.982) < Facebook connection: Connected vs Not connected ( 1.777, 2.009) < 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

14 Results: Inverse Gaussian Modelling Log(revenue)] = β 0 + β 1 *games + β 2 *purchase + β 3 *prize1+ β 4 *prize2 + β 5 *total Parameters Exp(Estimates) (95 % CI) p-value Intercept (0.240, 0.302) < Games (1.013, 1.017) < First in-game purchase on the installation day (1.006, 1.158) First prize received within the first 2 days of installation (1.045, 1.261) First prize received 2 days after installation (0.893, 1.093) Total numbers of items purchased (1.375, 1.471) < 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

15 Discussions 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.

16 Discussions (cont d) 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.

17 Future work 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.

18 Acknowledgement Dr. Wendy Lou Dr. Paul Corey Dr. Billy Chang And Changchang Xu for putting this presentation together

19 Experience from last year 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

21 Thank you!

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