Daily Active Users – Definition
The number of unique users interacting with an online product on a given day is calculated as the daily active users. It is a way to measure the success of an online product, be it an online game, social networking site or online publication.
A Little More on What is a Daily Active Users
The daily-active-users calculation assesses the “stickiness” of an internet product. The DAU calculation registers an active user only when a visitor accesses the site, engages with it or takes any action. The action may differ from one type of product to another. The activity on social media site includes creating account, signing in, posting, reacting and commenting on a post, watching a video etc., for an online publication the activities are visiting, subscribing, reading or saving a story, for mobile app it is downloading the app, signing up, using the app and sharing it with friends.
Generally, a user is considered active if they view the product online or establish an interaction with it. It ranges from just visiting the splash page of the product, commenting on its Social media page to actually using the product. For some companies, login and engagement are required for considering someone to be an active user. This can also be calculated on a monthly basis, known as Monthly Active Users. Depending on the nature of the product, daily or monthly active users are calculated to have an idea about the reach of that product.
References for Daily Active Users
Academic Research on Daily Active Users
· Unveiling facebook: a measurement study of social network based applications, Nazir, A., Raza, S., & Chuah, C. N. (2008, October). In Proceedings of the 8th ACM SIGCOMM conference on Internet measurement (pp. 43-56). ACM. The researchers developed and launched 3 applications on the Facebook platform. The applications achieved a combined subscription base of over 8 million users. This article shares analysis of the data gathered through those applications, including insights into the development of communities, user interactions, and social gaming applications.
· Literature review on web application gamification and analytics, Xu, Y. (2011). Honolulu, HI, 11-05. This document reviews different gamification design theories and approaches and examines the usage and effectiveness of commonly employed game mechanics. The author also examines the gamification metrics of some gamified applications in order to provide insight into gamification research.
· Modeling and predicting the growth and death of membership-based websites, Ribeiro, B. (2014, April). In Proceedings of the 23rd international conference on World Wide Web (pp. 653-664). ACM. This article analyzes the daily number of users (DAU) of twenty-two membership-based websites over 6 years. The author proposes processes for explaining the DAU trends and for predicting the sustainability of the websites’ membership bases.
· The freemium business model, Wilson, F. (2006). A VC Blog, March, 23, 201. The author describes the freemium business model for online applications, and offers discusses examples from the dating app and online gaming markets.
· Gamification, virality and retention in educational online platform. Measurable indicators and market entry strategy, Osipov, I. V., Volinsky, A. A., & Grishin, V. V. (2014). This paper describes gamification (the application of gaming elements), virality (growth in user base) and retention of existing users in a freemium educational online platform. The paper uses real examples to discuss the relationships between virality and retention. The authors propose the K-growth factor, which combines virality and retention, to measure user base growth, and show how the K-growth factor can determine the potential success of a large scale market launch.
· Game industry metrics terminology and analytics case study, Fields, T. V. (2013). In Game Analytics (pp. 53-71). Springer, London. This article explains the context, process and terminology of automatic feedback loops between online game users and developers.
· The lifecycles of apps in a social ecosystem, Kloumann, I., Adamic, L., Kleinberg, J., & Wu, S. (2015, May). In Proceedings of the 24th International Conference on World Wide Web (pp. 581-591). International World Wide Web Conferences Steering Committee. This paper analyzes a collection of apps that use Facebook Login. It proposes a model that shows a user’s tendency to keep using an app over time. The authors also seek to explain the likelihood that a user will adopt an app by examining the user’s real-life social network. Finally, the authors present models that use the features of apps to predict their success.
· Modeling website popularity competition in the attention-activity marketplace, Ribeiro, B., & Faloutsos, C. (2015, February). In Proceedings of the Eighth ACM International Conference on Web Search and Data Mining (pp. 389-398). ACM. This paper examines Facebook’s defeat of MySpace and other social networking platforms in order to construct a new model of competition. The model offers insights into the growing competition for user attention, describes the relative popularity of Facebook over its competitors and predicts the daily active users four years into the future.
· Towards a workload model for online social applications: ICPE 2013 work-in-progress paper, Olteanu, A. C., Iosup, A., & Ţãpuş, N. (2013, April). In Proceedings of the 4th ACM/SPEC International Conference on Performance Engineering (pp. 319-322). ACM. The authors analyse data from hundreds of Facebook apps and describe their popularity and their retentions of users over time. A key finding is that most apps peak in popularity within a third of their lifetime.
· Awareness, interest, and purchase: The effects of user-and marketer-generated content on purchase decision processes, Scholz, M., Dorner, V., Landherr, A., & Probst, F. (2013). In 34th International Conference on Information Systems (pp. 1-17). The authors use data from a large German e-tailer to determine the role that user-generated content (UGC) and market-generated content (MGC) play in purchasing decisions. They offer a 3-stage model of the purchase decision process that shows the effects of UGC and MGC.
· Awareness, interest, and final decision: The effects of user-and marketer-generated content on consumers’ purchase decisions, Scholz, M., Dorner, V., Landherr, A., & Probst, F. (2013). The authors use data from a large German e-tailer to determine the role that user-generated content (UGC) and market-generated content (MGC) play in purchasing decisions. They offer a 3-stage model of the purchase decision process that shows the effects of UGC and MGC.