On Prophesying Online Gamer Departure
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On Prophesying Online Gamer Departure

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On Prophesying Online Gamer Departure

  1. 1. On Prophesying Online Gamer Departure Pin-Yun Tarng1, Kuan-Ta Chen2 and Polly Huang1 1 Department of Electrical Engineering, National Taiwan University 2 Institute of Information Science, Academia Sinica 3. Evaluate the average daily playtime and playing density in Figure 5: Unsubscription prediction accuracy Introduction and Motivation each period 4. SVM as classifier, treat k -period features and predetermined categories as the training data set. Business model of online game companies usually depends on: Complete Scheme • Sale of virtual items 100 • Monthly subscriptions in which gamers must pay for credits Playtime features Density features to continue their adventures in the virtual world. Both features The combination of our classification method and prediction 90 Being able to predict how long people will stay in the game will model: Accuracy (%) directly affects game companies’ revenue. 80 Fade-out prediction model This study provides a practical scheme for predicting player un- 70 Staying subscription: Classification model Leaving? • Input– a player’s game hours 60 Fade-out Leaving • Output– whether or not he will renew an expiring subscrip- 5 10 15 20 Gameplay k value Trend? tion. Sudden-out • Our rationale– if we can predict the departure of a player Figure 3: The classification accuracy of different values before he actually quits a game, the game operator can of k. take remedial measures to prevent it from happening and improve the game along the way based on the feedback Figure 6: The complete unsubscription prediction scheme provided by such a player. To find the optimal k value, we experiment within the range of [2, 20], ten-fold cross-validating for each value. From Fig.3, we Our traces are from ShenZhou Online, a mid-scale commercial find that the optimal value is 10. Input – a player’s incomplete trace MMORPG in Taiwan sustaining at any moment thousands of Only incomplete data is available in real-life prediction – Output – three way output: players online. gamers’ traces, with last n days cut off (n in [3, 60]), are fed • Sudden-out pattern (or just unpredictable) into the SVM model. Fig.4 shows the result. • Staying for the time being ShenZhou Online Traces Summary 2003-03-01 • Leaving within a specific number of days. 2007-02-15 The accuracy of our complete prediction scheme is shown in Longer than 2 years Fig.7, and Fig.8 shows the three types of errors. 100 1,447 days Longer than 1 year 102,233,240 Longer than 0.5 year Shorter than 0.5 year 90 Accuracy (%) 162,980 20,514 80 Longer than 2 years 100 Longer than 1 year 70 Longer than 0.5 year Shorter than 0.5 year Figure 1: Summary of ShenZhou traces 90 Accuracy (%) 60 0 10 20 30 40 50 60 80 Before unsubscription(days) 70 Figure 4: Predictivity of our classification method. Classification of Online Gamers 60 0 10 20 30 40 50 60 d value (days) From observations on gamers’ playing history (as in Fig.2), an intuitive categorization of unsubscribing players: Model for Predicting Unsubscription Figure 7: Accuracy of our complete prediction scheme • Fade-out, with ever-decreasing daily playtime and login frequency How to predict whether a gamer is leaving in d days? (d in [3, • Sudden-out, no noticeable tendency in daily playtime or False leaving False staying False sudden−out 60]) 50 50 50 login frequency Longer than 2 years Longer than 1 year Longer than 2 years Longer than 1 year Longer than 2 years Longer than 1 year Similar to our classification method, for each gamer: Longer than 0.5 year Shorter than 0.5 year Longer than 0.5 year Shorter than 0.5 year Longer than 0.5 year Shorter than 0.5 year 40 40 40 1. Assign prediction point at d days before his quitting 30 30 30 Error rate (%) Error rate (%) Error rate (%) 20 20 20 20 20 20 2. Derive two random observation windows, counting from the Daily playtime (hrs) Daily playtime (hrs) Daily playtime (hrs) 10 10 10 15 15 15 gamer’s first login day 10 10 10 0 0 0 5 5 5 3. Leaving window – contains the prediction point, unsubscribe 0 10 20 30 d value (days) 40 50 60 0 10 20 30 d value (days) 40 50 60 0 10 20 30 d value (days) 40 50 60 0 0 0 within d days after window; staying window – not contain 0 200 400 600 800 1000 0 200 400 600 800 0 200 400 600 800 Day Day Day the prediction point, still stay at least d days after window 20 20 20 4. Extract 10-period features from each window Figure 8: False positives and false negatives of our pre- Daily playtime (hrs) Daily playtime (hrs) Daily playtime (hrs) 15 15 15 5. Fed to the SVM along with corresponding window type diction scheme 10 10 10 5 5 5 The prediction accuracy for each d value is shown in Fig.5 0 0 0 0 200 400 600 800 0 200 400 600 800 1000 0 200 400 600 800 1000 Day Day Day Fade−out Sudden−out Longer than 2 years Longer than 2 years Conclusion 100 100 Longer than 1 year Longer than 1 year Longer than 0.5 year Longer than 0.5 year Shorter than 0.5 year Shorter than 0.5 year Figure 2: The playing history of six sample gamers 90 90 Accuracy (%) Accuracy (%) The ability to predict a gamer’s departure is coveted by the 80 80 MMORPG industry as it allows the game operators to target How to perform automated classification? 70 70 their resources on keeping subscribers motivated and to bene- 1. Randomly choose 2,000 gamers, classify them with the human fit from these loyal customers. To this end, we hope that our 60 60 0 10 20 30 40 50 60 0 10 20 30 40 50 60 eye d value (days) d value (days) scheme will prove helpful to operators, as well as gamers who 2. Divide each gamer’s history into k periods of equal length may enjoy a better gaming environment because of it.

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