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A subscribers' behaviour simulation tool to support mobile game business decisions

CSM/KSS'2005 : 19th JISR-IIASA Workshop on Methodologies and Tools for Complex System Modeling and Integrated Policy Assessment jointly with the 6th International Symposium on Knowledge and Systems Sciences (KSS), Aug. 29-31, 2005, IIASA, Laxenburg, Austria.

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A subscribers' behaviour simulation tool to support mobile game business decisions

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  1. CSM/KSS'2005 : 19th JISR-IIASA Workshop on Methodologies and Tools for Complex System Modeling and Integrated Policy Assessment jointly with the 6th International Symposium on Knowledge and Systems Sciences (KSS), Aug. 29-31, 2005, IIASA, Laxenburg, Austria A subscribers' behaviour simulation tool to support mobile game business decisions Norihisa Komoda, Yuji Shono, Department of Multimedia Engineering Graduate School of Information Science and Technology Osaka University, Japan Ayako Hiramatsu, Hiroaki Oiso Osaka Sangyo University, Japan, Codetoys, Japan

  2. Contents 1. Objectives 2. Overview of the mobile game “Who Wants to be a Millionaire?” 3. Subscriber Number Forecasting 4. Detail of Simulation Process 5. Experiments and evaluations 6. Conclusions I want to play more. Fee is too expensive.

  3. by incentive by incentive unsubscribers new subscribers Current subscribers 1. Objectives • Target; Mobile quiz game content (monthly subscribe-type business) • The content provider needs to increase the subscribers. • To increase the new subscribers and to decrease the unsubscribers, the content provider considers several incentive plans; bonus prize, point system, up-stage, and so on. In this presentation, by forecasting the subscriber number by using the subscribers’ behavior model, the selection support method for the incentive plans is proposed.

  4. 2. Overview of the Game • “Who Wants to be a Millionaire?” • broadcasted on TV more than 56 countries. • From 2001, Codetoys provides the mobile game in the world-wide under the license from Celador, UK . • In Japan, a web base service started from 2002 through the 3 domestic carriers. Established in 1998 in Finland 2002 GSM Association Award for the Best Consumer Wireless Application or Service from 2002.4 from 2002.10

  5. 2. Overview of the Game “Who Wants to be a Millionaire?” in mobile Example of question in English According to the Bible, how many days and nights did it rain during the Noachian worldwide flood? 1. 40 2. 100 3. 365 4. 7 Question 聖書によると、ノアの大洪水は何日続いた? 40 4 choices of an answer 100 365 Rules of mobile game 7 • Max 15 questions/game. • Answer a question wrong, the game is finished. • Answer 15 questions correctly, • a subscriber gets 10 M points. • A subscriber can play games up to 150 times in one month.

  6. 2. Overview of the Game • Ranking is decided by 2 factors. • Primary: Point (Decided by the number of correct answers) • Secondary: Game time for a play • (easy to cheat in game) • A stage system for a prize (an incentive system) • Top 25% subscribers at the end of a month move up to • a higher stage in the next month. • Top 5 subscribers of the stage 4 get a prize. • (10,000 Yen(74 Euro)) • At least, 4 months are needed to get a prize. Stage 1 Stage 2 Stage 3 Stage 4 Prize Given to top 5 subscriber of stage 4 TOP 25% TOP 25% TOP 25%

  7. unsubscribes Unsubscription rate(%) stage 3. Subscriber Number Forecasting Basic idea for forecasting the subscribers number number of subscribers(k+1 month) = number of subscribers(k month) + number of new subscribers– number of unsubsrcibers subscribers(k month) New subscribers Approximately constant About 1000 sub./month subscribers(k+1 month) The unsubscription rates differ in subscriber segments decided by subscribers’ attributes. And, the effect of incentive plans also differs. So, segment based forecasting is necessary.

  8. 3. Subscriber Number Forecasting The important fact for realizing segment based forecasting is that the subscribers move belonging segments every month. • The stage of the subscriber is ranked up. • A game ranking of the subscriber is changed by distribution. • subscription period • stage • monthly game count • game ranking Therefore, • All subscribers’ attributes should be updated and subscribers must be divided into segments every month. • For that operation, all subscribers should be managed as individuals, not as the groups. • Subsrciber based forecasting is necessary.

  9. For segment s. original unsubsrciption rate ： 30% change of rate by incentive j： -20% 30%×(1 - 0.2) = 24% 3. Subscriber Number Forecasting The outline of subscriber based subscriber number forecasting after incentive plans (1) Steady-state unsubscripion rates are estimated by the log data. The rate after incentive plan execution is changed by the provider’s intuition. (2) Forecasting the number of new subscribers and unsubscribers of each month. Approximately constant. Since this research does not treat the incentives for increasing the new subscribers, we assume the number of new subscribers is constant; 1000 sub./month. (1)Subscribers are divided into segments based on subscribers’ attributes. (2)The changes of the unsubsrciption rate by incentive plan are set segment by segment. (3) Based on modified unsubsrciption rates, subscribers’ behavior is simulated.

  10. 4. Detail of Simulation Process Unsubsrciption rate(UnSub_rate j) of segment j <segment 1> Period:more than 3, stage 4, game count >100, ranking top 33% subscribers are divided into 192 segments by four attributes All subscribers ･･･ UnSub_rate 1 = 10% ･･･ ･･･ <segment 2> • subscription period：1, 2, 3, >3 • stage： 1, 2, 3, 4 • monthly game count： 0, 1-100, 100-150 • game ranking： • 0, 0-33%, 33-66%, 66-100％ • 4 x 4 x 3 x 4 =192 segments UnSub_rate 2 = 30% <segment 3> : Continue ．．． : Unsubscribe

  11. 4. Detail of Simulation Process The provider estimates the change rate of the unsubscription rates of all segments applied an incentive plan by intuition. Below 2nd stage:-20% Above 3rd stage:-50% <Segment 2> 2nd stage, > 100 games, > 4 months, < 33% ranking <Segment 1> 4th stage, > 100 games, > 4 months, < 33% ranking Unsubscription rate =15% 7.5% Unsubscription rate =25% 20% <Segment 3> 2nd stage, > 100 games, 3 months, < 33% ranking <Segment 4> 3rd stage, < 50 games, > 4 months, < 33% ranking Unsubscription rate =27% 21.6% Unsubscription rate =20% 10% ．．．

  12. : Continue 4. Detail of Simulation Process • (1)Unsubscription • Each subscriber stochastically unsubsribes following the assigned modified unsubscription rate of the segment at random. Segment ID Unsubscribe rate k month 40% 1 : 40% 2 : 20% 40% 3 : 15% 15% : Unsubscribe ・・・

  13. : Continue 4. Detail of Simulation Process • (2) Relocation of subscribers to adequate segment • Based on the subscriber attribute values (subscription period, stage, monthly game count, and game ranking), the segments that subscribers belong with may change every month. Segment ID Unsubscribe rate k month k+1 month 40% Segment ID 1 : 40% 1 : 40% Unsubscribe rate Ranking down 2 : 20% 2 : 20% 40% 3 : 15% 3 : 15% Stage up 15% : Unsubscribe ・・・ ・・・

  14. : Continue 4. Detail of Simulation Process • (3) Addition of new subscribers • New subscribers in k+1 month are added in suitable segments. Segment ID Unsubscribe rate k month k+1 month 40% 1 : 40% 1 : 40% Ranking down 2 : 20% 2 : 20% New subscriber 40% 3 : 15% 3 : 15% Stage up 15% : Unsubscribe ・・・ ・・・

  15. 5. Experiments and evaluation • Verification of the forecasting method: • Forecasting in the period in which there was no incentive. • subscriber based forecasting (proposed method) • vs. • segment based forecasting • Comparison of potential incentive plans: • Forecasting • Comparison of forecasting and real data applied selected incentive plan.

  16. 5.2 Experiments(1) Verification of forecasting method Three month forecasting results by using the real subscriber attribute data of August, 2004 are compared with the real subscriber number of November, 2004. No incentive plan carried on in this period. subscriber based forecasting (proposed method) vs. segment based forecasting Segment based Total error: 1.1% Average: 7.7% % Subscriber based Total error; 4.6% Average: 3.9% The subscriber based forecasting is twice as precise as the segment based method.

  17. The provider decided the condition and change rate of Unsub_rate by intuition. Forecasting results Plan A is the best and the provider applied the plan A in January 2005. Comparison of three incentive plans 5.3 Experiments(2) A new lottery incentive that gives gifts to 10 subscribers who satisfy some condition.

  18. Comparison of forecasted and real number of subscribers 5.3 Experiments(2) A lottery incentive plan that gives 3,000 yen (22 Euro) to 10 subscribers randomly selected from all subscribers who gave 15 correct answers in January, 2005 was executed only one month. % Subscriber based Total error rate: -0.1% (5822 sub. No.) stage * The number of new subscribers was adjusted as 1000. Segment based Total error rate: -0.5% (5,800 sub. No.) Total No. By using the tool, total subscriber number can be forecasted with an accuracy of -0.1% error. And, the errors of subscriber number by the stages are from -5.0% to 1.9%.

  19. 6. Conclusions • The subscriber number forecasting method for decision making of content incentive plans has been developed. • Forecasting is possible under the various assumption. • After the application to three incentive plans decision making, the provider said, “The tool can quantify the effect of an incentive plan. It is useful for decision making.” • Further researches; (1) Since the precision of the forecasting is depend on the DM’s inspired guess, the support of the DM’s input effort is needed. (2) Development of a new subscriber model.

  20. Thank you for your kind attention. 2002.7 near Laxembrug

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