Most revenues of the MMORPG (massively multiplayer online role-playing game)
industry come from the sale of subscriptions and virtual items, especially
to loyal "hardcore" players who would stay in a game for more than a year.
Understanding the players' behavior and how long will they stay in the
game is hence vital to game operators. If a player's departure is
predictable, measures can be taken to prevent that from happening.
This paper strives to develop a practical scheme for predicting player
unsubscription. The players have various degrees of predictability, hence we
approach the task first by classifying them with support vector machine,
then use the same tool to model their playing pattern before and after a
given date. In the case of hardcore players, the scheme allows us to predict
two months prior with a compound accuracy of over 80%. We have also
conducted generalizability analysis to show that our scheme is generalizable
across different MMORPGs and can be also applied to avatar usage
Online gaming has become increasingly popular in recent years. In
, it is reported that over 55% of Internet users are now also
online gamers, of which 90% have experience with role-playing
games . The market size for online games has reached 6 billion
US dollars in 2007 , with the commonest business model being
the sale of virtual items or monthly subscriptions in which gamers must pay
for credits to continue their adventures in the virtual world. From the
perspective of game operators, predicting how many people will join a
game and how long they will stay in the game is crucial, since these
two factors dominate their revenues.
Predicting how many players will join a game before a game's launch is very
difficult, if not impossible, since it involves many non-game factors, such
as marketing strategies, the release date of the game (whether it is
launched during the summer vacation), the artistic design (whether it is
manga-like or realistic), and cultural references (whether it is Oriental or
Occidental). Predicting how long a player will stay once he joins a game is
more feasible, as it should correlate with the extent of his involvement in
the virtual world. Usually, this can be inferred from the player's external
behavior, such as how quickly his avatar advances to new levels and how much
time he spends in the game every day.
This study is an extension of our previous game hour analysis .
Our goal is to provide a practical scheme for predicting player
unsubscription that takes a player's game hours as input and determines
whether or not he will renew an expiring subscription. Predicting
unsubscription decisions are important to game operators because the
decisions affect their revenues directly. Our rationale is that, if we
can predict the departure of a player before he actually quits a game, the
game operator can take remedial measures to prevent it from happening and
improve the game along the way based on the feedback provided by such a
Predicting unsubscription decisions can provide the
Players usually quit a game because they are dissatisfied with the
game's design or content. Thus, to some degree, player unsubscriptions
should indicate low user satisfaction. In other words, if we can predict
which players will leave the game in the near future, we may have a chance
to stop them leaving, or at least understand their reasons and make future
improvements. To this end, operators could conduct surveys to determine the
causes of player dissatisfaction and improve the game accordingly. (It is
also likely that operators would not receive useful comments because
dissatisfied players who have been totally disappointed with a game
may reluctant to take surveys from game companies.)
Predicting gamer unsubscription facilitates forecasts on the number of
future players as well. Even though we may predict the number of players
directly by using time series modeling , unsubscription
prediction provides more information because we can predict which
players will leave the game rather than just how many players will
leave. With such information, game operators can optimize their network and
server allocation beforehand.
Our study is based on real-life traces collected from ShenZhou Online
, a mid-scale commercial MMORPG (massively multiplayer online
role-playing game) in Taiwan. The traces we acquired from the operator
contain the playing histories of 162,980 accounts over a span of four
years. We propose a classification method to first identify the involvement
pattern of the players in game along time with 90% accuracy, and then
devise a prediction model that detects whether gamers are leaving the game
in the near future. For "hardcore" players who subscribe to the game for
more than a year, the prediction reaches 85% accuracy. Furthermore, we
analyze the generalizability of our scheme by collecting 2,132
questionnaires from real-life gamers, and find that players of different
MMORPGs have similar playing patterns towards their unsubscription. We also
apply our methods to World of Warcraft  avatar traces, and find
that we can detect with 80% accuracy whether gamers are discarding their
avatars in the near future.
The remainder of this paper is organized as follows.
Section 2 reviews a selection of related studies. The origin and collection of our traces are described in
Section 3. In Section 4 we
observe how gamers play during their subscription and classify them into one of the two groups: fade-out and
sudden-out. We blaze a way of predicting gamer unsubscription in
Section 5, while the generalizability of our scheme is analyzed in Section 6. Finally, we conclude our findings and results in Section 7.
2 Related Work
Based on a set of World of Warcraft traces, Pittman et al. 
attempted to establish a realistic, empirical model for predicting
player behavior and server population fluctuation over time. The authors conjectured that at
least four types of information are required for such a
model: 1) the server's population variation over time; 2)
the arrival rate and session duration of players; 3) the spatial
distribution of avatars in the virtual world; and 4) the movements
of avatars over time (how many distinct regions the avatars visit
and how long they stay in a region).
Chambers et al.  presented a comprehensive analysis of player
behavior and game server workload of Counter-Strike, a well-known
first-person shooting game. They found that 1) gamers are extremely
difficult to satisfy and displays zero loyalty if a game server is not
properly set up and provisioned; 2) the popularity of a game follows a power
law, making it difficult to provision at launch time; 3) server workload
exhibits predictable patterns on daily and weekly scales but loses them in
longer terms; 4) a shared game hosting infrastructure posts significant
challenges; and 5) software updates are a great burden on game hosting and
must be planned for.
In our previous pilot study , we analyzed what time players enter
the game's virtual world and how long they stay in the game, investigated
whether a player's future game hours can be predicted with his observed
behavior, but fell short of embodying an unsubscription prediction model.
The study nevertheless paved the way for the selection of parameters in our
player classification method, and the goal it stated is very much
accomplished in this current paper.
3 Data Description
Developed and distributed by UserJoy Technology Co., Ltd., ShenZhou
Online  sustains at any moment thousands of online players, who
must purchase "game points" if they wish to continue their adventures in
the virtual world beyond the 30-day free trial period. A screenshot of the
game is given in Figure 1. As in typical MMORPGs,
ShenZhou Online players can fight with random creatures, trade in
marketplaces, take on quests, and train themselves to become masters of
Figure 1: A screenshot of ShenZhou Online
Out of courtesy of UserJoy, we were able to obtain the traces of 162,980
ShenZhou Online accounts from March 1, 2003 to February
15, 2007. A total of 102,233,240 sessions is logged.
Not all of the traces are suitable for our analysis, however, as about two
thirds of the accounts were never seen again after their respective trial
periods. Furthermore, some accounts obviously had sessions falling outside
the covered 1,447 days of the traces. To play on the safe side, we only
use traces whose first activity is six months or more later than March
1, 2003 and whose last activity (assuming that they quit
after) is six months or more earlier than February 15,
2007. The base of our study thus consists of the un-curtailed traces of
4 Classification of Online Gamers
4.1 Identifying the Groups
Figure 2: The playing history of six
Prediction of a gamer's unsubscription would be feasible if his playing
history prior to his departure exhibited one or more features not uncommon
to fellow leaving players. The most intuitive of those features may be an
ever-decreasing daily playtime. The two gamers on the left of
Figure 2, which juxtaposes the entire playing history
of six randomly chosen gamers, indeed conform with the intuition, while the
others display no obvious and exploitable trends. As a matter of fact, only
312 out of a random set of one thousand gamers fit into the "fade-out"
pattern, as determined by the human eye. The other 688 would be
categorized as "sudden-out," with no noticeable tendency in daily playtime
or login frequency. They could go from playing for more than 12 hours
every day this week to complete disappearance in the following week.
We figure that if we focus on the fade-out group of gamers, the prediction
of departure will attain a higher degree of success. Consequently, a
scientific method is needed to separate the "predictables" from the
4.2 Automating the Classfication
We base our automated classification method on gamers' average daily
playtime and playing density. First, we randomly choose 2,000
gamers from our traces and classify them with the human eye. Among all the
sample gamers, 613 are fade-out and 1,387 are sudden-out. Second, we
divide each gamer's history into k periods of equal length, and evaluate
the average daily playtime and playing density in each period. The playing
density is the occurrence of a gamer's playing days within all available
days. For example, if a gamer has at least logged in the game once for 15
days in June, his playing density in June will be 0.5.
In the case of k=3, suppose a gamer has a subscription length of 90 days,
then his average daily playtime and playing density, or his 3-period
features, for the first through the 30 day, the
31 through the 60 day, and the
61 through the 90 day are
respectively computed. In addition, we normalize the two statistics by
setting each player's maxima to be 1, so that the classification would not
be influenced by a few gamers' extra large or small playtime and density.
We use the support vector machine (SVM)  as the classifier. The
traces of the 2,000 sample gamers, along with their k-period features
and predetermined categories, serve as the training data set. Note that if
we divide the playing history too roughly (small k), the trend we extract
would be blurred, jeopardizing the overall classification accuracy. On the
other hand, if we use too large a k, the average daily playtime and
playing density on some periods might be dominated by a few big days. To
find the optimal value of k, we experiment within the range of [2,20],
and compare the resulting classification accuracy in
Figure 3. Each accuracy estimate is computed via ten-fold
cross-validation. We conclude that 10-period features contain the most
trend information and yield the best classification.
Now that a SVM model with k=10 is established, the traces of the remaining
accounts can be processed. Of the 18,514 remnants, 5,503 (29.7%) are
deemed fade-out while 13,011 (70.3%) are dubbed as sudden-out.
4.3 Predictive Classification
Figure 3: The classification accuracy of different values of
So far we have been classifying players with their complete traces. In
real-life prediction, however, only incomplete data is available, and
the approximate time of a gamer's final login is to be predicted. Therefore,
we need to check whether the gamers could be correctly classified by their
Figure 4: Predictivity of our classification method.
The traces of the 2,000 sample gamers, their last n days cut off
(n ∈ [3,60]), are fed into the SVM model as the training data set along
with their re-computed 10-period features. The predictivity of our
classification method, ten-fold cross-validated, is shown in
Figure 4, where lines are drawn for various subscription
lengths. It can be seen that gamers with longer subscription lengths tend to
be more resilient to the cutting, as 82% of the hardcore players
(subscribing for more than one year) are correctly classified even with
their last month truncated. This is good news because hardcore gamers
generate most of the operator's revenue and are most valued by the gaming
5 Predicting Gamer Unsubscription
5.1 Prediction Model
To predict whether a gamer is leaving in d days (n ∈ [3,60]), we first
construct a SVM for each d. Similar to our classification method, we use
the traces of the 2,000 sample gamers as the training data set. For each
sample gamer, we assign the prediction point at d days before his
quitting. Two random observation windows, counting from the gamer's first
login day, are derived for each gamer. One of the observation window, dubbed
as leaving, contains the prediction point, since it is implied that
after the last day of the window, the gamer will unsubscribe within d
days. The other window, tagged as staying, does not contain the
prediction point, so it is implied that the gamer will stay in the game for
at least d days after the last day of the window. The 10-period
features are extracted from each window and fed to the SVM along with their
corresponding window type.
Figure 5: Unsubscription prediction accuracy
We applied the above procedure on both fade-out and sudden-out groups to
give Figure 5. The accuracy estimates are again computed
via ten-fold cross-validation. Our method is especially useful for
predicting hardcore fade-out gamers, reaching an accuracy as high as 90%,
while only about 70% is attained for sudden-out gamers. The difficulty of
predicting sudden-out gamers lies in their irregular behavior, which may or
may not be due to their own social activities.
5.2 The Complete Scheme
The complete unsubscription prediction scheme is the combination of the
classification method (Section 4.2) and the prediction model
(Section 5.1) and takes a player's incomplete trace as
input. A gamer categorized as sudden-out is difficult to predict, so we opt
to leave him be, not applying the prediction model on him. As a result, the
complete scheme will give a three-way output:
The player is of sudden-out pattern and just unpredictable;
The player is staying in the game for the time being;
The player is leaving within a specific number of days.
Figure 6: Accuracy of our final prediction scheme.
The ten-fold cross-validated prediction accuracy of our scheme shown in
Figure 6, can be seen as a logical aggregation of
Figures 4 and 5. We can see that the
accuracy for predicting whether a gamer is going to leave in a month is
around 82% for gamers who subscribed for more than one year, and 85% for
gamers who subscribed for more than two years. The results indicate that,
for hardcore gamers, it is feasible to use our scheme to predict whether
they are quitting from the game in the near future.
Figure 7: False positives and false negatives of our prediction
The error rate of our scheme regarding the three classes of outputs is given
in Figure 7. The probability of wrongly identifying a leaving
hardcore player as staying is only about 10%. Put it the other way, we
can detect 90% leaving gamers correctly with our final prediction scheme,
if we do not count the false unpredictables in. The game operators, knowing
in advance which gamers are leaving from the game in advance, can target
their resources in preventing them from quitting. Nevertheless, a certain
amount of resource inefficiency is inevitable, as there is a 15% false
6 Generalizability Analysis
6.1 Generalizability across Games
There is a myriad of MMORPGs out in the market, and we need to make sure our scheme is applicable to at least most of them. In other words, we need to see whether players in different games are with similar playing trends, that is, if we classify players according to their playing behavior, e.g., decreasing or increasing playtime, the distribution of them in the categories is similar across the games. If the gamer population shows similar behavior in different games, our prediction methods should be applicable to other games.
We empirically classify gamers into five categories with respect to their
playtime trends: fluctuating ("depends"), decreasing,
changeless, increasing, and periodical. In order to see
the gamer distribution of different games, we conducted an online survey
resulting 2,132 effective questionnaires from gamers who had played an
MMORPG before but had already quit from it. In the survey, 74% gamers
were male and 93% gamers were students when they played the game.
Furthermore, 77% gamers turned to another game after they had left the
Figure 8: The distribution of playtime trends of different MMORPGs
We compare the playtime distribution of the three most popular MMORPGs in
the survey, World of Warcraft (WoW), Lineage , and Ragnarok Online
, with that of ShenZhou Online in Figure 8. We can
see that Lineage, Ragnarok Online, and ShenZhou Online feature similar
distributions, while WoW gamers tend to maintain their playing patterns
towards the end of their subscription, resulting in an exceptionally low
proportion of decreasing playtime. The discrepancy may be related to the
games' receptive artistry and sizes of population, since players are
attracted to well-designed games and games where they can find many
Figure 9: The distribution of reasons for unsubscribing different
We have also investigated what make gamers quit from a game
(Figure 9). We found that the top seven reasons are (in the
order of frequency):
They had more important things to do, such as obligatory military service or school entrance exams;
They become bored with it;
Their friends left;
They realized that it is waste of time playing MMORPG after all;
Their accounts were hacked;
They turned to other newer games;
They had no more money to spend on entertainment.
We find that no matter what the game is, gamers quit for similar reasons. In addition, WoW gamers tend to quit from the game citing "more important things" instead of boredom, a testimony of the game's attractiveness.
From our questionnaires, we find that gamers of different MMORPGs have
similar playing trends and unsubscription reasons. Therefore, since our
scheme can successfully predict the departure of ShenZhou Online gamers, it
should be applicable to another MMORPGs, too.
6.2 Prediction of Avatar Usage
Figure 10: prediction accuracy of our scheme for WoW avatar usage
If the game operator is able to predict whether a given avatar is going to
be discarded by its user, it will know in advance how popular a race or
career path will be in the near future. To verify the applicability of our
scheme on avatar usage, we apply it to the two-year traces of WoW avatars we
collected in . The verification serves two purposes, that is,
we use it to confirm whether our scheme applies to another MMORPG (WoW
rather than ShenZhou Online) and to avatars (rather than accounts). The
result, shown in Figure 10, is only slightly worse than
that of unsubscription prediction of ShenZhou Online gamers, achieving
80% accuracy at d ≥ 30 for hardcore players. The drop in accuracy may
be due to the facts that our WoW data is shorter in time and that complete
traces lasting longer than a year accumulate to only 6% of the data.
Furthermore, avatar usage is supposed to be more unpredictable, as gamers
might own multiple avatars at the same time, and might discard a avatar for
the sake of constant change.
We proposed in this paper a generalizable scheme for predicting online gamer
departures. The scheme is the combination of a player classification method
and a prediction model, and takes a player's to-date playing history as
input. It first identifies the player as fade-out or sudden-out according to
his playtime trend. If he falls into the fade-out group, given a specific
date in the near future the scheme predicts whether or not he has left the
game by that date. We devised and tested the scheme with four years' worth
of ShenZhou Online traces, and achieved a compound accuracy of over 80%
in predicting the decisions of hardcore players.
The ability to predict a gamer's departure is coveted by the MMORPG industry
as it allows the game operators to target their resources on keeping
subscribers motivated and to benefit from these loyal customers, not only
financially, but also in terms of the improvement of current games and the
design of future ones. To this end, we hope that our scheme will prove
helpful to operators, as well as gamers who may enjoy a better gaming
environment because of it.
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http://www.ewsoft.com.tw/index.php.Sheng-Wei Chen (also known as Kuan-Ta Chen) http://www.iis.sinica.edu.tw/~swc
Last Update September 19, 2017