Bayesian Non-parametric model to Target Gamification Notifications Using Big Data

2 INTRODUCTION User generated content (UGC) is the cornerstone of social and online marketing. However, the key challenge for online marketers to leverage UGC is to encourage users to generate more quality content. To overcome this burden, practitioners have started to use video game concepts such as badges, leaderboard, and points to encourage users, under the umbrella of an approach called Gamification. Online marketers require a data driven approach to target users based on their response to gamification elements. Knowing the response of individual users to various game elements can help the online marketer to emphasize various content generating tasks in its personal messaging, to maximize the total number of user generated contents. For example, knowing that a user reduces its content contribution after receiving a badge, an online marketer can create a diversified list of content generating tasks for user in a customized message, to make badge earning more difficult. Moreover, knowing that a user increases its content contribution after earning more points, the online marketer can create a targeted list of content generating tasks for users in a customized message, to make badge earning simpler. Online marketers can leverage their massive data sets of users' content generations to create more customized targeted messages. This big data usually consists of several little data sets for each user, but its key advantage relative to the classic data sets is that it has more information about the tail of the distribution of customer response. This tail is relevant for targeting. Of course, a model can accommodate capturing the behavior on tail, if it allows the number of parameters to grow with the size of the data set. A useful method shall not through away these data by sampling, but it shall be flexible to not to misfit. 3 Hierarchical Bayesian (HB) approaches are well known for their estimation of individual specific parameters, and for allowing for unobserved heterogeneity, while sharing statistical strength across individual parameters. However, to be flexible, an HB model shall deviate from the normal prior on the consumer response parameters to the mixture normal structure, to capture behavior parameter of users in tail. Furthermore, a suitable method for Big Data shall be not only scalable, but also fast, to allow an online marketer to target its users in timely manner. In summary, a suitable approach shall create a computationally tractable solution for the computationally hard gamified targeting problem …