Purchasing Behavior in Free to Play Games: Concepts and Empirical Validation
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2015 48th Hawaii International Conference on System Sciences
Purchasing Behavior in Free to Play Games: Concepts and Empirical
Validation
Nicolai Hanner Ruediger Zarnekow
Technical University of Berlin Technical University of Berlin
nicolai.hanner@tu-berlin.de ruediger.zarnekow@tu-berlin.de
Abstract best choice for mobile games at the moment [1].
Hence, the success of those games keeps calling for
Free to play is supposed to be the future of monetiz- competitors and has led to an enormous amount of free
ing games. On one side user can access the game for to play games in the market. This decreased the poten-
free, thus it is distributed quickly. On the other side the tial user base a single game can attract, reduces the
game generates turnover by user spending real money chance of retention and increased the cost for publish-
on virtual items. Even though only a small number of ers to gain new users.
players buy in-game items, it seems to be a very suc- This challenge forces game developers and publish-
cessful revenue model for games. Yet there is little ers to look for measurements to increase the games
understanding of the purchasing behavior of paying profitability. Therefore, it is important to them to iden-
customers. Due to the importance of understanding the tify their most profitable users. One common concept
small group of paying users, the customer lifetime to predict the potential value of customers is the cus-
value becomes a major issue. This paper contributes to tomer lifetime value (CLV). This concept has been
the understanding of purchasing behavior in free to established in marketing research and is based on the
play games. With real data of paying users, we shed expenses a company incurs to attract, sell and service
light on the purchasing behavior for conversion, reten- the customers, and the revenue generated by them [15].
tion and monetization of customers in free to play On important aspect of the CLV is to model the future
games. purchases of a customer [15, 5, 28]. By now these
models have mainly been built on business cases in
1. Introduction medical offices, attorneys and insurance related firms,
customer database, high-tech B2B, retail, and e-
A fast growing part of the digital economy is the commerce [28]. Especially due to the context that users
videogames industry. It is a dynamic, innovative and can always use the game for free and that game mech-
technology driven business [30, 18, 25, 2]. One can see anism influence the user behavior [1, 14], the question
video games as “the fastest growing and most exiting arises if purchases in free to play games follow the
category of mass media on the coming decade” [18]. same pattern as common retail or service scenarios.
One recent radical change in the videogames industry Yet, the concept of free to play games is new to the
is a new way of monetizing the game content, called industry as well as it is to scholars. Past research
free to play. Free to play games mean that publishers evokes from virtual world and focuses primarily on the
give videogames away for free and appeal the gamers users’ motivation to participate in virtual world (which
later whilst playing to buy virtual items [10]. Yet the must not be obligatory games) [23]. Though scholars
user can still keep using the game without paying. have added new insights to an economical view of
Virtual items can for example increase the virtual char- virtual worlds and games, there is only little research
acter’s ability or simply individualize the character that explains and analyses the purchasing behavior of
with decorative items [10, 23]. The easy and free entry gamers in free to play games. Regarding this lack of
into the game courts a much larger base of customers. knowledge, this paper aims to make a contribution for
Therefore, the number of games with a free to play researchers as well as for practitioners to understand
revenue model, the user base, and generated revenue how users purchase in free to play games.
increase disproportionately worldwide. Mainly, these The paper focuses on two goals - one is related to
are free to play online games, social games and mobile the purchasing behavior and will be targeted in section
games. Especially the mobile gaming market experi- 2 and 3 by formulating hypotheses and analyzing the
ences a huge shift towards free to play games [16, 23]. purchasing behavior of users in free to play games. The
As game professionals state that free to play seems the second goal seeks for the potential application in man-
1530-1605/15 $31.00 © 2015 IEEE 3326
DOI 10.1109/HICSS.2015.401agement of games and usage of CLV models and is Though in the beginning virtual items have been
mainly answered within the fourth section. used in the context of virtual worlds many genres of
This paper is structured as follows. The second games in different domains started to use this revenue
chapter theoretical background and hypothesis will model. Especially casual games1 (e.g. candy crush
explain common concepts in research regarding virtual saga, clash of clans) have adopted this revenue model
items/free to play games and secondly the concept of successfully. Furthermore free to play is a common
CLV. Further there will be information about the me- revenue model in social games [1]. Yet, also core
chanics of free to play games. From this background games start using this revenue model. One of them,
we draw our hypotheses, which are based on the analy- called Cross-Fire, is amongst the bestselling games
sis of free to play games and previous research on vir- worldwide [29]. By now, successful free to play games
tual items and CLV. The third chapter contains infor- can be found within different domains [1]. This shows
mation about the data and the games and includes the that free to play games can hardly be characterized by
analysis of our hypotheses using statistical methods. In the genre or game type. Free to play games have in
the fourth chapter we discuss the results from our anal- common that they are accessible and playable for free
ysis and give managerial advice. The paper ends with a until the gamer wants a certain virtual item. Therefore,
conclusion. some virtual items can be only or at least faster ac-
cessed through real money [9].
2. Background and Hypotheses Furthermore, scholars have explored the importance
of game mechanics. For example Lin and Sun explore
2.1 Virtual items and free to play games the impact of virtual items on the fairness in games.
I.e., what happens if gamers can buy an advantage in
Virtual items as a revenue model - trading real the game for real money [16]. Hamari and Lehdonvirta
money for virtual items – was established back in the analyze how game design can foster the user’s inten-
early 2000’ [14]. Since then, virtual items have been tion to buy virtual items [14]. Also the perspective of
subject of different academic contributions. As men- game mechanic, and customer relationship has been
tioned, the motivation for analyzing and explaining this analyzed by Hamari and Järvinen [13].
concept predominantly evokes from virtual/digital Predominantly those virtual items have either a
worlds. They focus on the user’s motivation [23] or competency value or a visual authority, in some cases
their extended self in online worlds [4]. Further re- they might fit to both values. One widespread mechan-
search also includes an economic perspective on the ics, especially in casual games, is the reduction of wait-
topic. Gou and Barnes investigated purchasing behav- ing time. This can be seen as a functional attribute that
ior in virtual worlds. As a result they can partly explain allows the user to skip time for a desirable “next step”
the users’ intention to make an in-game purchase [10]; in the game.
a likewise approach can be found in the research of Moreover, the demand for virtual items can be trig-
Mäntymäki and Salo [17]. More research was conduct- gered through marketing activities. Especially in mo-
ed on the intention to purchase virtual products by bile games it is common to send push messages to the
Animesh et al. [3]. Wu et al. focused on explaining the user’s mobile device reminding him of the game and
business logic of online games. They showed similari- potentially interesting offers in the in-game stores. Yet,
ties to two-sided markets and how network effects resent research shows that an inflationary spread of
influences the games profitability [31]. virtual items decreases their value [31]. Therefore a
Besides these an important topic consists in the val- publisher cannot simply “spam” the users with offers
ue users receive from virtual items. Park and Lee for virtual items, since this would subvert the whole
summarized these values from previous research [23]. revenue model of free to play games. In most free to
One is the enjoyment value. This value is simply relat- play games the process of purchasing a virtual item is
ed to the perceived fun in the game if a user buys a separated into two parts. Mostly the user cannot buy a
virtual item [23]. The second value is the character virtual item directly with real money. It can only be
competency value. It aggregates all values that come obtained through an in-game currency. Therefore free
from increasing the characters ability and/or functional to play games have two types of currency. One that can
attributes [23]. Visual authority value is the third value. be earned within the game (e.g. resources like stones,
It explains the value from rare items or the increased iron etc.). The second type of currency “premium-
social status through virtual items [23]. The fourth currency” (e.g. diamonds) can sometimes be earned in
value is the monetary value where users purchase vir-
tual items due to their cost-effectiveness and reasona- 1
Casual games refer to games that can be learned easily and are
bly pricing [23]. played occasionally whereas core games have a more complex game
mechanic and need full user attention if played.
3327the game but in most cases it has to be purchased with
Firm value
real money. This type of in-game currency is sold in
packets. E.g. a player can buy 10 diamonds for $ 1,99
or 100 for $ 18,99. Usually these packets are priced
between $ 1 up to $ 100. After the purchase, the player CLV & CE
can use this currency to buy the wanted virtual items.
2.2 Customer lifetime value
Customer Customer Customer
Acquisition Retention Expansion
The CLV is a widely discussed topic in the academ-
ic literature [11]. Due to closer customer relationships
and progresses in technology the topic became interest-
ing for researches as well as practitioners [5]. Closer
customer relationship refers to a direct contact between Marketing Programs
customers and firms due to new channels like the in-
ternet [15]. Progress in information technology means NOTE: CLV = customer lifetime value; CE = customer equity
the feasibility to track the customer purchasing behav- Figure 1: Conceptual Framework for Modeling
ior [11, 5] making the transaction data available [7]. Customer Lifetime Value [11]
The CLV can be seen as a metric to assess the return of
marketing investments [11]. Further, it allows a firm to Regarding the CLV calculation and prediction, it
identify the most profitable users on an empirical mod- should be differentiated between contractual settings
el. Therefore, the firm can foster an efficient allocation where a defection of a customer is observable and non-
of marketing resources [15]. Besides a wide range of contractual settings where a customer defection cannot
topics regarding the CLV, Jain and Singh summarized be detected [28].
three areas of research [15]. Two of them focus on
modeling and analyzing the customer base and their 2.3. Purchasing behavior
purchasing behavior. The calculation of the CLV takes
all expenses into account whereas the customer base We now discuss three hypotheses that we will de-
analysis analyzes the existing customer base to predict duce from theoretical background and further assump-
future transactions. The third area focuses on the ef- tions. In order to achieve our research goal we use the
fects of loyalty programs on the CLV and the firm’s framework shown in figure 1 as the basis for our hy-
profitability [15]. potheses.
Moreover, researchers summarized further aspects As stated we are interested in the purchasing behav-
of the CLV in a conceptual framework, which has been ior of customers, which can be summarized in custom-
used in different academic contribution regarding the er acquisition, customer retention and customer expan-
CLV (see figure 1) [11]. The framework shows that sion [11]. The acquisition and retention is of high im-
marketing activities can influence purchasing behavior, portance, as the acquiring and maintaining the en-
which is customer acquisition, customer retention and gagement of large user bases is a core activity of free
customer expansion. These components are interrelated to play games [13]. For free to play games the defini-
and influence the CLV. As such the CLV determines tion of the acquisition is of importance, as it depends
the firm value [11]. on the service, whereas it should be set to the install,
Some researchers state that the retention phase can the usage of the game or the first purchase [13]. Since
be seen as the most critical for the CLV [26], others we focus on paying users we defined the first purchase
have not found a strong correlation between duration as the end of the acquisition. Consequently once the
of the customer relationship and profitability [27]. As user makes a purchase he or she bypasses to the reten-
mentioned in the introduction, there are different cases tion.
in which CLV models have been tested. Yet, research- The retention can be seen independent regarding the
es argue that even simply management heuristics can purchasing of virtual items, since users can continuous-
lead to similar results in several use cases [28]. Hence, ly use the game without paying [13]. Yet, as we focus
some authors suggest more mathematical than numeri- on the CLV and are strongly interested in paying users,
cal models for the CLV calculation [24]. we link the retention towards in-game purchases. Fur-
thermore we differentiate between the retention and the
actual amount spent by the user. As Gupta et al. state –
the customer expansion is the margin generated by a
customer in each time period. This margin can be de-
3328fined as the cross- and up-selling of products or addi- expectation, or they might feel that the ease of use
tional services [11]. In free to play games, these addi- (effort expectation) is low - they are not likely to con-
tional services are virtual items [13]. Thus, purchasing vert at a later stage, even though they start playing the
virtual items is also a part of the customer expansion. game. Thus, we propose that users who take longer
Though Hamari and Järvinen state that monetization time before they start playing will less likely ever con-
seems to be the more suitable term in the context of vert to paying users.
free to play games [13].
Hence, our hypotheses will address: the conversion H1: The probability of a user to convert to a paying
(customer acquisition), retention (customer retention), customer (t) will decrease with the time () passed
and monetization (customer expansion). All of them before the user starts playing.
might be widely used in the videogames industry how-
ever, are not well understood. To ease the understand- (t1) > (t2) … . > (tn)
ing we will draw a conceptual model in figure 2 of the
user’s lifetime in a free to play game. Retention: Being in the state of a paying customer,
nth
currency the question arises, how long will the user keep pur-
second chasing. In the context of free to play games there is
purchase
purchase
sunk cost after the first purchase, because virtual items
monetization
playing first
purchase can only be used in the specific game. As the user
V(x)
installation
starts to invest in the game he or she makes a progress
in the game which he cannot transfer to any other game
i p x1 x2 xn time (or back into real money) [1]. Hamari applied the idea
of sunk costs to social games [12]. The author argues
ct rx that users have a loss aversion. Hence, even without
conversion retention intrinsic motivation the user might return to the ser-
Figure 2: Purchasing behavior in free to play games vices due to the perceived dissatisfaction by leaving it,
because he or she loses the previously gained progress
The user’s lifetime starts with the installation, the and invested money [12]. Therefore, if the users made
next step will be that the user plays the game. This a certain progress in the game by buying items – thus,
phase is the conversion phase t, where the user either increasing their potential loss by abandoning the game
becomes a paying customer, or not. Followed up by the – the user will make further purchases. We propose
users purchases that he or she does to certain extend that these users are now in a close relationship to the
game and willing to do more than one purchase.
and what is defined as the retention x. The amount of
Hence, the probability to retain in the game is increas-
money spend on every purchase is defined as the value
ing.
x of monetization.
Conversion: As argued the conversion phase ends
H2: The probability of a users’ retention (x) will
with the first purchase a user makes. The user will
purchases an item if the user perceives an item to be increase with every made purchase ().
valuable [10]. But how can the user know his or her
valuation of this virtual item? He or she has to start to (x1) < (x2) … . < (xn)
play the game and explore its possibilities. Evidently,
this explorative phase will take the player a certain Monetization: By doing a purchase in the game the
time and can be describes as a starting immersion into user must choose the amount of money he or she wants
the game – this immersion can increase the probability to spend on virtual items. Yet the user cannot have
that a user starts to pay for virtual items [21]. If the much experience with the premium currency. Because
user doesn’t feel attracted by the game and waits a the revenue model of free to play games is based on the
longer period before he or she starts playing it, we absent of this premium currency, the user will not have
argue that this will decrease the probability that this had the chance to earn much of it without paying [1].
user will eventually convert to a paying user, even if he For example in social games Paavilainen et al. show
or she starts playing the game at some point. This can that users can have a negative attitude towards buying
further be explained by expected performance and virtual items, because they do not see the value of vir-
effort. As researches argue, effort and performance tual items [22]. This lack of be experience can lead to a
expectations can be related to the purchase intention mistrust in the sold virtual items. As researchers argue
[10]. Hence, we propose that if users do not feel affect- that trust can influence the willingness to buy virtual
ed by the game they do not have a high performance items [8]. We propose that with increasing experience
3329with virtual items, the willingness to pay will increase. information about the purchases is tracked by the back-
Furthermore the character competency influences the end of the publishers’ infrastructure. The users come
willingness to pay for virtual items [10, 9]. Yet, these from mostly European countries and the USA. The
character competencies must either be gained through following figure shows the structure of the data of a
intensive playing or using virtual items to reach an generic paying user (see figure 3).
equal level. If the users gains character competencies second third
installation tutorial first
by buying virtual items this will increase his willing- of the game completed purchase purchase
purchase ($1,99)
ness to pay. Moreover the immersion into the game can ($1,99) ($4,99)
be related towards the willingness to pay [21]. Re- ID:1 ID:1 ID:1 ID:1
ID:1
search shows that users who buy items show a higher t
immersion [21]. This leads to the idea that the user
i tc x1 x2 x3
might not know his or her valuation of virtual items in
the beginning but with an increasing immersion into beginning of the ending of the observation
the game their willingness to pay for virtual items will observation period period
increase [14, 13]. Hence we argue that the users’ will- Figure 3: Data structure
ingness to pay will increase with repeated use of virtual
items. According to that we formulate our third hy- The data tracking starts with the installation process
pothesis as follows: of the game. During this process, every user is tracked
with its own ID that can be related to any purchase this
H3: The amount of money users are willing to single user does during his or her customer lifetime.
spend on virtual items x will increase with every pur- The information about those purchasing events in-
chase made (). cludes the amount spent (a fixed price for a packet) and
the timestamp (day/month/year/hour/minute/second).
x1 < x2 … . < xn The data of the RMG and SMG includes only play-
ers that have at least made one purchase in the game.
3. Data Analysis From the card trading game we receive the same data,
however, there is further information about the in-
game process of the user. It is tracked whether or not
3.1 Data Structure the user has passed a tutorial (which shows basic me-
chanics in the game). We use the tutorial completion as
The data is provided by a marketing firm in the vid-
an indicator that the player has already started playing
eogames industry that chose to remain anonymous.
the game. Hence, we can track users that started play-
The data comes from three different free to play mobile
ing the game, but never converted to paying customers.
games, which are all released by different publishers.
For the following analysis we use a cohort of users.
Both, games and publishers, have to remain anony-
The cohort is defined by a timescale where they in-
mous. As characterized in section 2.1, all of the ob-
stalled the game (see figure 4).
served games sell their premium currency in packets.
observed purchases
Once purchased, the user can spend the premium cur-
rency on different virtual items in the game.
The first game can be seen as a resource manage- observed installations
ment game (RMG) this means the user has to build and
manage a virtual environment. The bigger this envi- 08/15/2013 11/07/2013 03/07/2014 t
ronment gets the more resources can be produced. This
increases the user’s level and global power. The second defines the cohort of players
game is a sports management game (SMG). The user beginning of the ending of the observation
simply has the role of a sports club manager and is observation period period
responsible for the sporting and economic future of the Figure 4: Cohort building
club. The third game is a card trading game (CTG). In
this game the user trades fantasy characters cards in To ensure valid results we use a cohort of users
order to battle against other users. where a period of at least four month of customer life-
These games have been chosen, firstly to dispose of time can be observed after the user installed the game2.
different genres for observation, secondly, because -
2
according to the marketing firm - they performed dif- It should be noted that the used data only shows a very small num-
ferently in terms of profitability. The observed time ber of users (This does not affect users whose duration is longer, For following days it fluctuates around 1%. By
since they still are included in our data analysis, though looking at the length of the time passed until the users
we might not see all of their late purchases. Yet this is started playing we see that this happens very shortly
not of importance for our analysis since our results are after the installation, 75% finished it within 3 hours
mainly based on the first ten purchases. Regarding the after the installation (see figure 6). Hence, for later
CTG data all observed users completed the tutorial at days we have a reduced amount of observable cases.
some point. The following table summarizes the num- This can explain anomalies.
ber of users for each game that are taken as the basis
for the analysis (see table 1).
Table 1: Sample size per game 0,001 0,1 10 time (days)
RMG SMG CTG Figure 6: Distribution of the conversion time install to
tutorial (CTG)3
number of
2428 1416 349
paying users
The following table summarizes this result of the
number of users
started playing
n/a n/a 5797 correlation between the conversion probability and the
passed time before playing.
The data has not been adjusted in terms of outliers,
Table 2: Correlation between conversion and passed time
since we cannot assume a normal distribution of the before the user started playing (CTG)
data. For correlation analysis we use the spearman’s
rank correlation coefficient (ρ), as suggested by Myers data source CTG
et al. [20]. probability of con-
ρ version
3.2 Results passed time
-0,669*
before playing
Our first hypothesis focuses on the conversion * = p < 0.05 (two-tailed test), ** = p < 0.01 (two-tailed test), *** = p < 0.001
phase of users. We proposed that users who do not (two-tailed test)
start playing the game early enough will also have a
The results show a significant negative correlation
decreasing probability of converting to paying users.
between the conversion probability and the passed
We use the CTG data because we can observe the tuto-
time. Therefore, this is an acceptable support for our
rial completion. As argued in section 3.1 we use the
hypothesis.
tutorial completion as an indicator that the user started
The second hypothesis supposed to show an in-
playing the game. The following figure (see figure 5)
creasing rate of retention with every repeated purchase.
shows the distribution of the probability to convert to a
To verify this hypothesis we calculate the retention rate
paying user for every day passed after the installation.
for every group of users as shown in figure (7 and 8)
10%
by looking at the probability of a group, e.g. the group
of users with three purchases, to do one more purchase.
8% The following figures show the distribution of proba-
6%
bility to do repeat purchase.
4% 100%
2% 90%
0% time (days) 80%
1 2 3 4 5 6 7 8 9 ≥ 10
70%
Figure 5: Probability of conversion per passed time before
the user started playing (CTG) 60%
As shown the conversion rate is already very low on 50% no. of
the first day. While it decreases on the second day the 1 2 3 4 5 6 9 ≥ 10 purchases
7 8
third one shows a further drop. With the fourth day Figure 7: Probability of retention per purchase (SMG)
passed after the install without completing the tutorial
the probability to convert drops significantly. 3
The scale is shown logarithmic for a better depiction, as recom-
mended by McGill et al. [19].
3331100% 130%
120%
90%
110%
80% 100%
90%
70% 80%
60% 70%
60%
50% no. of 50% no. of
1 2 3 4 5 6 7 8 9 ≥ 10 purchases 1 2 3 4 5 6 7 8 9 ≥ 10 purchases
Figure 8: Probability of retention per purchase (RMG) Figure 10: Mean purchasing volume per purchase (RMG)
The retention rate of both games shows an increas- In both games one can see an increase of the aver-
ing probability of retention with every following re- age amount spent by every following purchase. The
peated purchase. This observation is more supported mean is reaching a more stable value during the 5th or
by the RMG data. Yet, for both games the retention 6th purchases. Following table summarizes of the corre-
rate converges towards a stable value. The following lation between the average value per purchase and the
table summarizes of the correlation between the proba- number of purchases.
bility of retention and the number of purchases.
Table 4: Correlation between monetization and purchases
Table 3: Correlation between retention and purchases
data source SMG RMG
data source SMG RMG average value per
ρ purchase
ρ probability of retention
number of
number of 0,648* 0,891***
0,891*** 0,954*** purchases
purchases
* = p < 0.05 (two-tailed test), ** = p < 0.01 (two-tailed test), *** = p < 0.001
* = p < 0.05 (two-tailed test), ** = p < 0.01 (two-tailed test), *** = p < 0.001 (two-tailed test)
(two-tailed test)
For the third hypothesis we see strong support from
Both games show a high significant positive corre- the RMG data, while the SMG data also shows positive
lation between the retention probability and the number correlation, yet, without a very strong level of signifi-
of purchases. Thus, we see our hypothesis is strongly cance.
supported. This observation can be supported by looking at the
For the third hypothesis we group the average value sizes of the packets the users purchased in the game
per purchase depending on the number of purchase. (see table 5 and 6). For the SMG the first purchase
The following figures shows the (normalized4) mean shows that the majority of users (around 65%) buy an
spending for the purchases of a group of users (see extra small packet or small one. In the following nine
figure 9 and 10). purchases there is a shift towards lager and thus, more
expensive packets.
130%
120% Table 5: Mean packet size by purchase (SMG)
110%
100% following
first
90% packet Size nine
purchase
80% purchases
70% XS 39,0% 27,8%
60% S 25,8% 25,3%
50% no. of
1 2 3 4 5 6 7 8 9 ≥ 10 purchases M 19,9% 24,4%
Figure 9: Mean purchasing volume per purchase (SMG) L 11,5% 15,3%
XL 1,8% 3,4%
XXL 1,9% 2,5%
4
Due to the confidentially of the data we are not allowed to show
the average in currency. Therefore, we normalized the mean spend-
ing of all analyzed purchases to 1 (100%).
3332Table 6: Mean packet size by purchase (RMG) sumed that the retention rate will increase with repeat-
ed purchases. The data of the RMG and SMG show the
following highest rate of churn for users without a repeat pur-
first
packet Size nine
purchase
purchases chase. Yet with following purchases the churn rate
decreases, thus the retention rate increases. We con-
XS 8,5% 7,1%
clude that users in free to play games tend to an in-
S 38,0% 31,6% creasing retention rate once they started purchasing.
M 37,5% 30,1% The increasing retention rate indicates that users spend
L 11,3% 20,0% more often once they started spending. Regarding the
XL 3,6% 7,9%
CLV calculation this should be considered if modeling
the CLV. Thus, it is important to find models that can
XXL 1,2% 3,2%
reproduce these purchasing behaviors, e.g., they should
support a dynamic retention rate. Moreover, marketing
The RMG data shows similar results, though the activities as well as the game design should support the
smallest packet is purchased only by 8.5% of the users. willingness of users to do repeated purchases once they
Again the next bigger packets Small and Medium get start spending real money in the game. We consider
less attractive for users during their following purchas- game mechanics that do not push the frustration level
es whereas larger packets are purchased more often. too soon. Especially where users compete - an ease in
competition can increase enjoyment and a stronger
4. Discussion competition can increase the effort a user takes [6].
Thus, in the beginning an increased enjoyment can
In this section we will discuss our findings and re- keep the user retaining, whilst eventually an increased
late CLV and marketing measurements towards those degree of difficulty will increase his or her effort in the
results. game. Consequently, matching users the right way
Regarding the first hypothesis, we see evidence that could be essential for the retention.
user who do not start playing the game within days The third hypothesis is also supported by the data
after the installation they eventually never convert to a analysis. In the SMG as well as in the RMG we see a
paying customer. Yet, it seems that the critical phase of strong correlation between the average spending and
conversion is – at least for most of the users – by- the numbers of purchases a player makes. Therefore,
passed very soon after the installation. This leads us to we conclude that with every made purchase the user
the conclusion that users who do not feel attracted by advance and immerse in the game their willingness to
the game - because of a lack of performance or ex- spend money on virtual items is increasing. Further, we
pected effort - and are not willing to immerse and ex- see evidence for this hypothesis by looking at the size
plore the games environment are less likely to ever feel of the sold packets. The results show a shift from
better about it and then become paying customers, even smaller packets during the first purchase towards big-
if they start playing the game at some point. The first ger ones in the following purchases. This adds up with
hypothesis should be considered with respect comes to the mentioned results and can be explained with the
the question whether a playing user will ever purchase risk players take if they buy a big packet of virtual
or not. This decision can be made any time after the currency. They accept an inferior exchange rate in the
installation process. Therefore, marketing activities beginning for their first purchase. This can be seen as a
based on the CLV should be planned carefully. Gener- risk aversion of the users that decreases by the growing
ally it seems appropriate to look at the time a user experience with the premium currency where the user
needs before he or she starts playing. If this time ex- can gain trust in the game. Regarding the third hypoth-
ceeds a certain maximum (this time could be specific esis, the used CLV model must consider a dynamic
of the game) the user should be considered a non- average spending on purchases. Furthermore publish-
paying user. Thus, the CLV can be set to zero. This ers should manage to reduce the risk the user takes for
means for games management, that publishers should the first purchase to improve the monetization. As
only try to incite users who start playing the game soon most users do only spend once, the increase of the
after the installation. This seems important if incen- average spending would contribute significantly to the
tives are generated by free premium currency or virtual games profitability. As mentioned by other authors this
items, since an efficient allocation of virtual items on can be realized by giving small amounts of premium
users can reduce potential deflation of their value, as currency to users for free. For users that do not value
stated by Wu et al. [31]. the game or those who decided not to spend this is nice
The second hypothesis is also strongly supported by to have and if it is a small packet it will not imbalance
the results. As argued within the hypothesis, we as- the games mechanics and cause deflation. For those
3333users who are interested in purchasing virtual items it Further we addressed the applicability of these results
is a helpful indicator of what they can achieve by pur- on the management in free to play games. In the dis-
chasing premium currency. Yet the results of hypothe- cussion section we argued what game publishers can
sis one should be considered, to prevent a deflation of do regarding their game design, CLV calculation and
virtual items. Moreover publishers should not try to marketing activities. All hypotheses give relevant in-
monetize the user to soon, as the immersion into the side on the purchasing behavior and lead to potential
game might take a long time of game play. Hence, measures for publishers. Another observation is the
expensive items might not be presented to early or significant differences between games. Hence, we
should not be too important for further progress in the argue that game design has a strong influence on the
beginning. Furthermore a dynamic presentation of results.
virtual items depending on the stage of every user The results of this paper are limited by the used da-
could improve the monetization. For the analyses we ta. As stated the data shows differences between
did on two games we could observe significant differ- games. Apparently, others games might lead to other
ences. Evidently, the design might have a strong influ- results. Yet we ensured to use data from different game
ence on the purchasing behavior. This idea is supported genre to valid our results.
by Hamari and Lehdonvirta [14]. For future research we consider several research di-
rections. Especially virtual items have been mainly
5. Conclusion and further research studied in virtual worlds. Yet the most revenue gener-
ated by virtual items is in videogames. Therefore, re-
The way of monetizing game content like free to search can extend on game mechanics and virtual items
play games do is new to industry and scholars. There is in the context of all types of free to play games. Fur-
only little research on how those games work and there thermore an interesting topic seems the usage of big
are no concepts that can empirically explain the cus- data in the videogames industry. As games generate a
tomer behavior in free to play games. Based on the massive amount of data the fast analysis of this data
important issue of identifying profitable users using will become possible by using big data techniques.
CLV models, the main goal of this paper was to show Since videogames are solely digital products there is
pattern in purchasing behavior in free to play games. also the chance to use marketing activates or game
Therefore, we characterized the purchasing behavior in mechanics dynamically on the results of CLV models.
free to play games. We deduced three hypotheses from
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