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

Norihisa Komoda, Yuji Shono, Department of Multimedia Engineering

Graduate School of Information Science and TechnologyOsaka University, Japan

Ayako Hiramatsu, Hiroaki OisoOsaka Sangyo University, Japan, 　 Codetoys, Japan

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.

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 un

subscribers, the content provider considers several incentive plans;

bonus prize, point system, up-stage, and so on.

In this presentation, by forecasting the subscriber numberby using the subscribers’ behavior model,

the selection support method for the incentive plans is proposed.

unsubscribers

Current subscribersnew subscribers

by incentiveby incentive

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.

from 2002.4 from 2002.10

Established in 1998in Finland

2002 GSM Association Award for the Best Consumer Wireless

Application or Service

2. Overview of the Game

4 choices of an answer

Question

•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.

Rules of mobile game

“Who Wants to be a Millionaire?” in mobile

401003657

聖書によると、ノアの大洪水は何日続いた ?

Example of question in English

According to the Bible, how many days and nights did it rain during the Noachian worldwide flood?

1. 402. 1003. 3654. 7

2. Overview of the Game

Stage 1 Stage 2 Stage 3 Stage 4

TOP 25%

Given to top 5 subscriber of stage 4

Prize

TOP 25% TOP 25%

•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 toa 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.

3. Subscriber Number Forecasting

New subscribers

Approximately constantAbout 1000 sub./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.

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

unsubscribes

subscribers(k month)

subscribers(k+1 month)

0

10

20

30

40

1 2 3 4 stage

Unsubscription rate(%)

3. Subscriber Number Forecasting

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.Subsrciber based forecasting is necessary.

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 periodstagemonthly game countgame ranking

3. Subscriber Number ForecastingThe 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.For segment s.

original unsubsrciption rate ： 30% change of rate by incentive j ： -20%

30%×(1 - 0.2) = 24%

4. Detail of Simulation ProcessUnsubsrciption rate(UnSub_rate j) of segment j

All subscribers

･･･

<segment 1>Period:more than 3, stage 4, game count >100, ranking top 33%

subscribers are divided into 192 segments by four attributes

．．．

<segment 2>

UnSub_rate 1 = 10%

subscription period ： 1, 2, 3, >3stage ： 1, 2, 3, 4monthly game count ：

0, 1-100, 100-150game ranking ： 0, 0-33%, 33-66%, 66-100 ％

4 x 4 x 3 x 4 =192 segments : Continue

: Unsubscribe

･･･

･･･

UnSub_rate 2 = 30%

<segment 3>

4. Detail of Simulation Process

<Segment 1> 4th stage,> 100 games, > 4 months, < 33% ranking

．．．

<Segment 3> 2nd stage, > 100 games,3 months, < 33% ranking

<Segment 2> 2nd stage, > 100 games,> 4 months, < 33% ranking

Below 2nd stage:-20% Above 3rd stage:-50%

Unsubscription rate=25% 20%

Unsubscription rate=15% 7.5%

Unsubscription rate=27% 21.6%

<Segment 4> 3rd stage, < 50 games,> 4 months, < 33% ranking

Unsubscription rate=20% 10%

The provider estimates the change rate of the unsubscription rates of all segments applied an incentive plan by intuition.

4. Detail of Simulation Process

Unsubscribe ratek month

・・・

(1)Unsubscription•Each subscriber stochastically unsubsribes following the assigned modified unsubscription rate of the segment at random.

Segment ID

1 : 40%

2 : 20%

3 : 15%

40%

15%

: Continue: Unsubscribe

40%

Unsubscribe rate

4. Detail of Simulation Process

: Continue: Unsubscribe

Unsubscribe rate

k+1 month

1 : 40%

2 : 20%

3 : 15%

・・・

Stage up

1 : 40%

2 : 20%

3 : 15%

・・・

40%

40%

(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

Ranking down

Segment IDk month

15%

Unsubscribe rate

4. Detail of Simulation Process

: Continue: Unsubscribe

New subscriber

k+1 month

1 : 40%

2 : 20%

3 : 15%

・・・

Stage up

1 : 40%

2 : 20%

3 : 15%

・・・

40%

40%

(3) Addition of new subscribers•New subscribers in k+1 month are added in suitable segments.

Ranking down

Segment IDk month

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.

5.2 Experiments(1) Verification of forecasting method

1.32.8

13.4

7.1

4.8

6.68.3

2.1

0

5

10

15

1 2 3 4stage

error rate

Segment based Total error: 1.1% Average: 7.7%

Subscriber based Total error; 4.6% Average: 3.9%

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

The subscriber based forecasting is twice as precise as the segment based method.

%

5.3 Experiments(2) Comparison of three incentive plans

Candidate condition of an incentive plan

Unsubscribing rate change Increase rate (%)

Change condition Rate 1 mon.

2 mon.

3 mon.

(A) All subscribers who gave 15 correct answers

All-30%

0.1 1.5 3.5

(B)Subscribers who have subscribed for less than 3 months

Less than 3 months -50%-0.8 0.3 1.4

More than 4 months 0%

(c)Subscribers whose stages are higher than 2nd.

Lower than stage 2 -20%-0.4 0.7 1.6

Higher than stage 3 -50%

A new lottery incentive that gives gifts to 10 subscribers who satisfy some condition.

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.

5.3 Experiments(2)

-1.4

-5

1.81.9

-2.2

-6.5

-1

3.7

-10

-5

0

5

1 2 3 4

error rate

Segment basedTotal error rate: -0.5% (5,800 sub. No.)

Subscriber basedTotal error rate: -0.1% (5822 sub. No.)

%

Comparison of forecasted and real number of subscribers

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.

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%.

real sub. No.* 2305 1439 806 1279 5829

Forecasted No 2346 1466 795 1215 5822

* The number of new subscribers was adjusted as 1000.

Total No.

stage

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.

Thank you for your kind attention.

2002.7 near Laxembrug