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The Impact of New Content and User Community Membership on Usage of Online Games


We investigate the motivations behind product usage in categories characterized by frequent 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 an 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 primarily motivated by intrinsic preferences, while group interactions and future benefits of learning about the product are relatively more important to explain consumption of more complex content. We find that an early innovation schedule and lowering content complexity can motivate engagement in initial stages of the product lifecycle, while providing incentives to social interactions is useful to increase content consumption in later stages. Our approach can be used to optimize the schedule and content of new product updates.

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  1. An anecdotal example of how important the relation between the purchase (subscription) decision and product usage is to managers and consumers alike was provided in May of 2011 at the earnings call of Activision Blizzard, one of the major developers of computer games. At the call, the discussion revolved around one of their main products, the popular online game World of Warcraft, which contributed a large percentage of the firm’s profits. Subscriptions declined from 12 million paying customers at the end of 2010 to 11.4 million at the end of March of 2011 and continued to rapidly drop until May. In response to questions about this decline, the company’s CEO, Mike Morhaime, said that “subscriber base does not change linearly. It fluctuates based on content consumption, which players seem to be doing a whole lot of - at a more rapid pace,” and continued by promising “faster release of new content” to respond to the demand decline.

  2. We 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 consumer decision of choosing a specific group by changing the choice set at this decision stage and adding elements to the utility such as group characteristics.

  3. It is possible to also include a more permanent satiation directly dependent on product usage, but we found that the one-day state dependence combined with content aging to explain the data well.

  4. Although it is possible to have a more complex function for consumer expertise that accounts for both the quantity and level of past content consumed, we find that the maximum level of past content complexity matches a wide range of applications. For example, in computer games, a player’s level is usually defined as a function of most complex completed content; in TV series, viewership of the latest episode shown is also a good representation of the most useful knowledge about the storyline events. In other applications, that might not be true. For example, in educational products, the amount and level of content absorbed are both important to measure consumer progression in learning. In that case, a more complex expertise function is necessary. We note that both \(l_{it}\) and \(l_{t}\) are discretized for the estimation algorithm.

  5. The probability \(Pr(\tilde{p}_{t+1}=\tilde{p_{t}}+1|\tau _{\widetilde{p}t})\) reflects the firm’s propensity to invest in resources to generate more content. From the consumer’s point of view, this propensity is assumed to be exogenous; each individual consumer believes that her actions will not influence the firm’s decision and timing of product updates. In our application, the firm appears to have invested a certain amount of resources in the product (e.g., hired programmers) to support a fairly stable schedule of content introduction; according to announcements from the firm, new content is launched whenever ready and stable for usage, not before and not after. This decision resulted in several updates in the first half of the product lifecycle, followed by a longer period of time without additional introductions before the next version of the product. A similar schedule had also happened before our analysis period and supports the assumption that the schedule of product updates is an exogenous decision by the firm and not directly influenced by a consumer’s usage decisions. In addition, in our application, the transition to a new product update can occur only on Tuesdays, when server maintenance is performed, which makes \(Pr(\tilde{p}_{t+1}=\tilde{p_{t}}+1|\tau _{\widetilde{p}t},\varvec{X}_{t})=0\) except when \(X_{t}^{Monday}=1\). For the estimation of the duration model, \(\tau _{\widetilde{p}t}\) is measured in weeks. The model was estimated by maximum likelihood and the estimated parameters with standard errors in parentheses are \(\chi _{1}=-2.885\) (0.738) and \(\chi _{2}=0.228\) (0.126).

  6. Given that \(w_{ijt}\) are known, we estimate the vector of parameters \(\varvec{\omega }\) using a logit model, where the dependent variable is the competitive position in period \(t+1\) and independent variables represent expectation relevant information available to consumer in period t that we collect in vector \(\varvec{Y}_{t}\). The estimates for the intercept, current level of expertise, age of content, and update-specific effects, and current competitive position, with standard errors in parentheses, are \(\omega _{0}=-33.998\) (0.809), \(w_{max(a,l)}=2.311\) (0.061), \(\omega _{\tilde{p}\,age}=-1.825\) (0.052), \(\omega _{p=1}=22.743\) (0.623), \(\omega _{p=2}=19.787\) (0.567), \(\omega _{p=3}=13.002\) (0.432), and \(\omega _{\check{l}}=9.402\) (0.099).

  7. We estimated our model using both finite and infinite horizons, and the results do not change significantly between these two cases. Our results are based on the infinite horizon formulation. Since we use the infinite horizon approach, we need to cap the time variable for content aging. We choose a large number — 210 days — from the introduction of an update, after which the content does not age.

  8. Our model can be applied even when consumers are unsure about the number of product updates, but the expectations about this quantity would need to be formulated.

  9. In our application, we set the time discount rate to 0.975 per day, which corresponds to 0.84 per week. This time discount rate is in line with values used in the literature on entertainment and experiential products. For example, [18] use a similar value, while [25] use a discount rate of 0.86 per week for video games. See [54] for the discussion of consumer time discount rates. We tested other discount rates and got similar substantive results.

  10. For more information, visit

  11. Available at

  12. Given that the objective of the paper is to measure the impact of innovation on product usage, the data includes only content related to the game’s main storyline. There are other unrelated tasks that we do not include in our analysis.

  13. We note that our data is more detailed than the patterns presented in the figure, since it includes the transition of players at the daily level and not just when product updates are introduced. On the rare occasions when we observed a user do multiple tasks within the same day, we chose the higher level task to code participation on that day.

  14. In practice, if this is a serious concern in other applications, the following change in choice probabilities can be implemented to account for this limitation: every time a consumer completes one achievement included in alternative j, subtract \(\frac{1}{C_{j}}Pr(j)\) from the probability of choosing action j and add it to the outside good probability, where term \(C_{j}\) is the number of tasks included in choice set j.

  15. We note that more challenging content that demands cooperation of multiple players can still be performed by individuals who are not part of a game community. The difference is that to perform these tasks, users form temporary groups just before attempting a task using an option called “looking for group.”

  16. After reading multiple user forums discussions, the decision of changing a group seems not to be primarily driven 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 have information about group characteristics or objectives and hence we do not model this social aspect of match between group and individual members.

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  18. Although the temporal patterns allow for partial identification of content aging, the (quadratic) functional form assumption contributes to its identification, by giving a specific pattern to aging that separates it from other components, such as forward-looking behavior.

  19. From the technical point of view, our formulation can be seen as a regular infinite horizon formulation, with the evolution of the membership state from period t to period t+1 being stochastic.

  20. We solve for the value function at the points that are multiples of 42 days and use linear approximation of the value function in the objective function.

  21. We found the unobserved heterogeneity to be insignificant for the social interaction decisions and present the simpler results with observed heterogeneity across the ability dimension only. We allow for both unobserved and observed heterogeneity for the content choice stage.

  22. For clarity of exposition, we do not present the \(J-1\) intercepts of each choice \((J-1=19)\) nor the \(X_{t}\) coefficients for each weekday in Table 3. For the alternative intercepts, they vary between 0.16 and 1.27, with the second product update having the most positive intercepts. Before our analysis period, and looking at past records of the game, we found that content at the middle of each expansion is usually the most valuable to users, by providing a strong progress or completion of the storyline behind the game, which justifies the knowledge of quality of content by consumers before launch. For the weekday intercepts, they vary between −0.13 and 0.33. The higher estimates are for Saturday and Sunday, when users have more time to play the game.

  23. The probability of belonging to one of the three segments is above 85% for most individuals.

  24. In the actual scenario, we use the stochastic belief about the timing of introduction, as in the estimation.

  25. An alternative way to implement an increase in content difficulty would be the change the success rates of completion.


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Correspondence to Paulo Albuquerque.

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Albuquerque, P., Nevskaya, Y. The Impact of New Content and User Community Membership on Usage of Online Games. Cust. Need. and Solut. 9, 1–24 (2022).

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  • Product usage
  • Dynamic demand models
  • Forward-looking consumers
  • Online content
  • Experiential products