-quent product updates and social interactions between users. The proposed approach builds on theoretical work on experiential products to define consumer utility as a function of intrinsic
preferences, social interactions, the match of content with user experience, and future benefits We empirically test our model using a novel individual data set from the online gaming industry
on daily content consumption, product innovation, and group membership. The results show that usage of simpler features is motivated primarily by intrinsic preferences, while group in
billion spent on video games, consoles, and accessories (Entertainment Software Association 2011 and $30 billion on crafts and hobbies in 2010 (Craft and Hobby Association, 2011;
Luo, Ratchford and Yang, 2013. In terms of digital and online consumption, the numbers are also noteworthy
Individuals can now access software, games, and social communities through smartphones, com -puters, and their gaming consoles (Williams, Yee,
and Caplan, 2008), and according to Nielsen, 81 billion minutes were spent on social networks and blogs in 2011 and 42%of tablet owners use them
daily while watching TV (Nielsen, 2011. Closely related to the empirical application in this paper the worldwide market for online games surpassed $15 billion in 2010 with additional sales of virtual
goods likely to exceed $1 billion (Playlogic Entertainment Inc, 2010. With online connectivity and the presence of the Internet, online or network games have been growing exponentially, with
a recent study showing that about 67%of teenagers regularly play some game online (Playlogic
Entertainment Inc, 2010 Although product usage and content consumption are ubiquitous consumer decisions and largely
explain repeat-purchase or product replacement (Huh and Kim, 2008), past marketing literature has focused on the adoption
such as playing games and sports, eating and drinking, watching TV, taking pictures, or recording movies (Luo, Ratchford,
behavior and product usage has been limited because of the lack of revealed preferences data on consumption; data collection mostly focuses on transactional information.
As an alternative, until recently, surveys or self-reported questionnaires have been used to study usage behavior, especially regarding technology products (Ram and Jung, 1990;
Shih and Venkatesh, 2004; Huh and Kim 2008 To completely understand the interaction of consumers and products, post-purchase behavior
we make use of a unique data set-from the popular online video game World of Warcraft-that tracks product usage, content consumption choices,
-preferences data on product usage Our research builds on two streams of literature. The first stream is based on psychological or
of the major developers of computer games. At the call, the discussion revolved around one of their main products
data that mimics usage patterns closely, Hartmann and Viard (2008) and Koppalle et al. 2012 propose dynamic methods that investigate the eï €ect of rewards on consumer activity in the golf
and Yang (2013) use leisure activities data to investigate the relation between consumption usage decisions and consumer lifestyles.
-sions of drinks using intra-day data, with consumers deciding between managing short run needs
but with primarily impact on a small segment of early users that are skilled also For other segments, postponement of new content launch is almost irrelevant.
Section 3 provides details about the novel data set on post-purchase decisions used in the paper
The estimation algorithm and a discussion about identification are presented in Section 4. The results and managerial implications are described in Section 5,
enjoying the product individually or with a community of other users. Every period t, consumer i
of a user group (stage d=1; second, she decides on content consumption (stage d=2). These
Once part of a group, the user obtains a number of benefits, including the opportunity to socialize with other individuals who have similar interests
of users, individuals choose from the available consumption alternatives, j=0,,...Jt, where j=0
For example, in video games, product updates are common and demand expertise from users to enjoy newly introduced content.
In television series, consumers decide to watch an episode partially because of instantaneous utility and partially because that decision allows them to enjoy
future episodes. Hence, we assume that consumers are forward-looking when making decisions and take into account future utility in their community membership and content consumption choices
Consumer communities, defined as networks of users or admirers of a brand, influence consumer perceptions of products (Algesheimer, Dholakia, and Herrmann, 2005;
with user interactions and social motivations explaining much of the variation in purchase choices and consumer behavior (e g.,
Examples of groups include users signing up to participate in a discussion forum about content, a Facebook group about a product, or a guild
or clan of players in a video game, as it is the case in our application At each time t, consumer i decides on their status regarding communities of users of the product
she can join a group, remain in a group if already part of one, leave that group,
or remain without any connections to user communities. 2 We assume that consumers can make this decision at any
2we opt to model the consumer decision to join any group, instead of a specific group, in part due to data
limitations and what can be identified in the empirical application. The model could be made to accommodate the
level of user experience at time t. Relatively more experienced consumers can help others, making
where lit denotes user i†s experience and lâ t stands for the average experience across all users at time
of a network of users and can be enjoyed separately from content consumption. The utility derived
the one-day state dependence combined with content aging to explain the data well 9
level that neighbors the level of the user to be more enjoyable (Pollak, 1970; Spinnewyn, 1981) and
users likely care about their status. We model this status eï €ect by comparing individual i†s level lit and the mean level of other consumers at time t, lt,
and below the mean expertise level of the user population can be interpreted as â€oesnob†and â€oebandwagon†eï €ects, based on the literature on prestige-seeking
For example, in computer games, a player†s level is defined usually as a function of most complex completed content;
in TV series, viewership of the latest episode shown is also a good representation of the
for the estimation algorithm 10 Finally, we assume that"ijt are unobserved shocks that are independently and identically dis
For examples, in our application, users of a video game can choose content that is hard to complete and have to repeat it in some cases multiple times before proceeding with the game
ability, the experience level of the user, the diï culty of the content, and guild membership status
data section The state variable pëoet denotes the index of the most recently introduced product update, pëoet 2
duration model and using historic data on update releases. The probability of an update occurrence
2 are estimated parameters. 5 We assume that users know the number of updates that a firm is going to launch, for example based on historic innovation patterns by the firm as in
being above or below the average experience level lâ t+1 of the user population.
their competitive position in the user population, lëoei, t+1=I (li, t+1 ï¿
, hired programmers) to support a fairly stable schedule of content introduction; according to announcements from the firm, new content is launched whenever
server maintenance is performed, which makes Pr (pëoet+1=pëoet+1 âoe§ept, Xt)= 0 except when XMONDAYT=1. For the
this assumption reasonable because we focus on experienced users and content had frequently been introduced for several years before the period analyzed in our study, providing enough information
Therefore, Equation 12 is a dynamic programming problem for consumer i. Let V ï ï
per week for video games. See Yao et al. 2011) for a list of other discount rates choices.
3 Industry and Data The proposed approach can be used to obtain insights about the relation between product usage
with the study of consumer demand in the online computer gaming industry 3. 1 An Online Game
We use data from the online game World of Warcraft developed by Blizzard Entertainment, a divi
According to the game†s website, World of Warcraft is a â€oemassively Mul -tiplayer Online Role-playing game (MMORPG), set in the high-fantasy universe centered around
dividuals explore the environment developed by programmers. The game was introduced originally in 2004 and became the best-selling PC game of 2005 and 2006 worldwide.
By 2011, Blizzard had launched three full-fledged expansions and dozens of patches that added new content.
Our data is related to the second expansion of the game, which sold more than 4 million copies in
The game environment and related data are particularly suitable to the study of product usage
First, users enjoy a storyline by repeatedly making consumption choices of content. Through these choices, they progress deeper into the storyline
Second, most user actions are visible to others since players interact in the shared environment and individuals have the opportunity to join online communities to share experiences
to provide a more complete experience to users and allow them to track their progress in the game
These data take the form of dates of first time completion of specific content consumption or a task
Several independent websites process this information into databases that allow cross-player comparisons and provide recommendations on how to progress in the game.
this paper, we use a publicly available data set on product usage collected from such a site called
Wowhead. 11 We complement these data with information about product updates, their content firm†s actions,
and other announcements from the oï cial game website Although purchase decisions are not the focus of the paper,
comes from additional fees paid by users to access the online game server and consumer content.
Our data set includes daily information about the game from November of 2008 to December of
player, we use the beginning periods in our data set to create a starting state for each player, which
the data includes only content related to the game main storyline. There are other unrelated tasks that we do not include in our
similar to the schedule observed in our data. Although there was not a predefined schedule, the time interval between updates varied by only a few weeks.
content was also similar in terms of user interaction and most additional content is introduced in a test server available to users, giving an almost perfect knowledge about the quality of content
before it goes live and allowing consumers to build expectations about the quality and timing of
The product usage data include actions of 206 users from one of the game servers, for whom the
These users were selected randomly from all experienced players who were able to access the content introduced on November 13th, 2008
Our data set does not include new players for two reasons. First, the website used as a source of the
data provides information about experienced users only. Second, the content introduced by the firm during our analysis was dedicated almost entirely to increasing participation of experienced players
The results of our application should be seen as an analysis of the behavior of these consumers and
On average, over the observed time periods, the users 13we obtained information about launch dates of expansions and patches from 2004 to 2008,
We use these data to create an empirical distribution used to define consumer expectations about the schedule of product updates
1 shows the daily participation of users measured by number of tasks completed, overall and with
t is defined as the highest level of content enjoyed by a user before that day. 14 The numbers indicate
the percentage of users at that level, while darker (lighter) lines between levels indicate that more
the first time a user performs a specific task in the game, each level l 2 {1, 2,,
and actually common to see users perform actions of the same level over time, and we observe repeated participation within a level
In the game, these communities are formal long-term groups of users who agree to cooperate and
14we note that our data is more detailed than the patterns presented in the figure,
a user do multiple tasks within the same day, we chose the higher level task to code participation on that day
The diï €erence is that to perform these tasks, users form temporary groups just before attempting a task using an option called â€oelooking for groupâ€
Based on information from user forums, the groups have one of two objectives: either be one of the top groups in the game
The data set contains the dates when an individual decides to join or leave a game community
of analysis. At the beginning of our study, the percentage of users in groups was low, about 6
To control for user diï €erences in the empirical application, we introduce both observed 17after reading multiple user forums discussions, the decision of changing a group seems not to be driven primarily
by prestige. Instead, it appears that switches are due to better matches between the user and the group characteristics
e g.,, time of day available for playing) or because of a personal connection to the group. Unfortunately, we do not
In our application, the firm published ability measures for each user before the product launch, called badges (a term also used in gamification:
how many times an individual successfully completed previous content above a certain complexity. The distribution includes individuals with
zero badges all the way up to 2500 badges, with a clear break in the data, with a group of users
because it might be easier for a group of users to provide insights about advanced content
users may fail to complete tasks and remain at the same level of expertise. We use data from the website World of Logs18 about the
success rates for diï €erent content. This website provides aggregate statistics about the number of
times that users attempted and successfully completed tasks in the game. For individual and small
group tasks, included in the lower two levels of each product update-levels 5-6, 9-10 and so on
If users are part of a community, which allows for additional in-game coordination, these rates increase by about 10%while skilled players have an additional 10%chance
If user expertise is above or below the content level by at least four levels, these success rates are increased to 100%or decreased to 0%respectively
Before we describe the estimation algorithm, we discuss the data patterns that identify the pa
-rameters in our model. Starting with the content utility function, its intercept is identified by the
value between task complexity and user experience are identified from the observed choices of con -tent levels, given the experience levels of consumers at each period t. For example, a consumer of
observed diï €erences in actions of consumers above or below the mean level of the user population
Moreover, since the average level of all users is drifting over time, the identity of consumers above
For the community membership parameters, the individual data on decisions of joining, re -maining, or leaving a group identify the overall costs of joining
and using the product, through the observed frequency in the data of remaining in groups
In the data, we observe consumers attempting higher level tasks more frequently when a product update is about to be released,
to join a group is influenced by the expectation that users will gain from the group by obtaining
Our data show that users play more and at higher levels just before and once they are part
of a community, providing support to the forward-looking assumption that users anticipate those benefits when making the decision to join the group
4. 2 Estimation Algorithm The structure of the consumer choice process within one time period involves making two consecutive
rate that is available as data. We note that for the community membership decision, the probability
We employ the iterative Expectation-Maximization (EM) algorithm procedure (Arcidiacono and Jones, 2003; Chung et al.
We combine the EM algorithm with the use of the constrained optimization approach (Su and Judd, 2012;
programming problem. Here, instead of using the nested fixed point algorithm (NFXP) on the Bell -man equation to solve for the value function,
we maximize the log-likelihood of the model subject to the constraint defined by the Bellman equation.
the data Y is LL (Y â ¥, âoe, VÂ =NX i=1 0@GX
The estimation algorithm proceeds as follows Step 1. Make an initial guess for the probability that each individual
model explains the data well. Second, we analyze parameter estimates and discuss the implied importance of diverse motivations of product use.
measure changes in user participation with diï €erent innovation patterns and social interactions and discuss managerial implications
given the parameter values and data for each time period. Figure 3 shows actual and estimated
pattern is observed if we disaggregate content consumption by product update in our data For our model, the hit-rate across all consumer choices
expertise users, as shown by the positive coeï cients when users are above the user population level
but especially so for high-skill users (coeï cient of 1. 9 vs. 1. 6). There are two reasons that explain
this result: first, more experienced users are likely to know where to find information and how to
navigate the process of joining a community, reducing the cost of joining a group; second, consumers
gets significant benefits, especially the low skilled users (0. 16 vs. 0. 13. In addition, when users
gain more expertise, the benefit of being part of a community becomes less important for low skilled
users(-0. 014) but increases for high skilled users (0. 018. This finding suggests that low skilled
users benefit from groups when they are starting to interact with content, but as their experience
while high skilled users benefit more and more from group engagement, explained possibly by a higher status in the group or the enjoyment
Parameter Low-Skill Users High-Skill Users Joining Cost Intercept 1-12.216 (0. 106)- 12.337 (0. 162
composed of skilled users and with an estimated size of 16%of the overall population, gets the highest satisfaction from content
as the distance between content and user levels increases, enjoyment of content decreases significantly. This is especially true for tasks with complexity above the consumer†s expe
watching a new episode in a television series after skipping the previous one. The diï €erent consumer
game, we found that content at the middle of each expansion is usually the most valuable to users,
when users have more time to play the game 31 Low-Skill Users High-Skill Users
Parameter Segment 1 Segment 2 Segment 3 Segment 4 Base Intercepts Nonmembers â 00-5. 482 (0. 488)- 10.0585 (0. 341)- 5. 1381 (0. 309)- 7. 077 (0. 331
value, to those of consumers who decided not to join a group of other users.
that collaboration across users within groups is prominent, with more experienced users frequently helping novice users to engage with more demanding content
Finally, the competitive eï €ect is significant for all segments, but especially so for the two skilled
are commonly below the average expertise level of other users 5. 2. 3 State Dependence and Aging of Content
potentially, as uncommon users of the product, perceive almost no aging of the product over time
) In television series, the same pattern is also common with subsequent seasons of a show attracting increasingly fewer audiences.
In the four panels of Figure 4, we show the evolution of the experience levels of each segment
average experience level of the user population in our data set We observe that the evolution of experience is significantly diï €erent across segments,
pattern but with a main diï €erence that these users progress much faster, in part justified by their
more serious users do not have new challenging content to improve experience and stop using the
Evolution of the user level, the mean level of the user community, and mean of the content
The panels in Figure 5 show the results by segment and content choices j=1,..20.
of match between user expertise and content complexity, denoted as the cost of completing a task
users, product usage of simpler content is motivated primarily by intrinsic preferences, while group interactions and future benefits of gaining experience from using the product are relatively more
and, even though users in our sample are experienced and familiar with the product, there is a clear distinction between innovators and laggards, in terms of timing of usage and of response to
as users become more experienced and enjoy more demanding content, they join a group of users to collab
-orate and share the enjoyment of content. Segment 3 is the fastest to form groups, while the other
product life, more than 70%of users enjoy being part of a product community, reflecting reduced heterogeneity in the consumer population.
-plexity by simulating a change in the â€oedistance†between the expertise of a user and higher level
of engaging in social interactions between users. Social components in product usage are becoming very important for most categories,
as managers attempt to lead users to social media platforms to generate content and connect with other users with similar preferences
5. 4. 1 An Alternative Innovation Schedule: When Should Managers Launch New Con -tent Our approach can be used to provide insights about alternative scheduling of product updates.
our data, we observe most updates concentrated in the first half of the product lifecycle,
participation from a large community of users or pace the introduction of new content to keep
by the share of active users. 25 The postponing of the last update leads to an initial drop in
1 and 3 of more highly skilled users In net terms, postponing the product update has a negative impact with overall participation
demand that the firm cannot satisfy, for example due to server capacity 5. 4. 2 Change in Product Diï culty:
between a user expertise level li and the level of content complexity lj required by a task of higher
user and content. 26 This is a relevant set up as, on the one hand, more complexity leads to longer
users, the impact of increasing or decreasing the perceived diï culty of the game on their behavior is
The darker line displays participation in the lower complexity setting, the lighter line reflects higher complexity,
negative eï €ects on later stages of the game because users consume content too fast.
In many product categories, companies facilitate social interactions between users, for example through the creation of online forums, public relations events,
or websites in social platforms. In the case of our application, the firm has over the years implemented a number of tools that allowed easier
members and where individual users can advertise their interests for groups To measure the eï €ect of an incentive to social interactions within our model,
any tool that facilitates in-game social interaction for all users impacts k1. Alternatively, our model
can quantify firm actions that specifically aï €ect more experienced users-such as making it easier
The results are presented in the two panels of Figure 9. On the top panel, we show the evolution
of the average level of users, while the lower panel reveals the percentage of participating users, for
both the actual and counterfactual cases. Overall, the decrease of 25%in k1 increases membership in
as the bottom panel shows, consumers are more active with the content. This engagement is especially important in later stages of the game
%increase in relative terms, indicating that users become more experienced with the game, are able to
making content accessible to users, either by lowering its diï culty or increasing social connections, has strong benefits to participation because,
the game has significant amounts of content that users never attempt, complemented with frequent updates.
a product are formed by frequent usage, such as video games, TV SHOWS, mobile applications, book series, and, in more general terms, any durable good where practice is essential to develop the
Using data from the popular online game â€oeworld of Warcraftâ€, we find that motivations for product
, TV series) or when pricing options such as a time-subscription (online games) oï €er a direct link between
and the EM Algorithm, †Econometrica, Econometric Society, Vol. 71 (3), pp. 933-946 4. Arndt, Johan (1967), â€oerole of Product-Related Conversations in the Diï €usion of a New Prod
-wideâ€, available at http://us. blizzard. com/en-us/company/press/pressreleases. html? id=2847816
Entertainment Software Association (2011), Top 10 industry facts, October 10th 12. Golder, P. N. and G. J. Tellis (2004), â€oegrowing, Growing, Gone:
The Case of Japanese Video games, †Working paper 21. Kopalle, Praveen K.,Scott A. Neslin, Baohong Sun, Yacheng Sun, and Vanitha Swaminathan
stereotypical gamer profile, †Journal of Computer-Mediated Communication, 13, pp. 993-1018 44. Yao, S.,C. F. Mela, J. Chiang,
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