The number of active users is a performance metric for the success of an internet product such as a social networking service, online game, or mobile app. It measures how many users visit or interact with the product or service over a given interval or period. This metric is commonly assessed per month as monthly active users (MAU), per week as weekly active users (WAU), per day as daily active users (DAU) or peak concurrent users (PCU).
Active users on any time scale offers a rough overview of the amount of returning customers a product maintains, and comparing the changes in this number can be used to predict growth or decline in consumer numbers.
The ratio of DAU and MAU offers a rudimentary method to estimate customer engagement and retention rate over time. A higher ratio represents a larger retention probability, which often indicates success of a product. Ratios of 0.15 and above are believed to be a tipping point for growth while sustained ratios of 0.2 and above mark lasting success.
Active user data can be used to determine high traffic periods and create behavior models of users to be used for targeted advertising.
Data collection methods and issues
Active users are calculated using the internal data of the specific company. Data is collected based on unique users performing specific actions which data collectors deem as a sign of activity. These actions include visiting the home or splash page of a website, logging in, commentating, uploading content, or similar actions which make use of the product. The number of people subscribed to a service may also be considered an active user for its duration. Each company has their own method of determining their number of active users, and many companies do not share specific details regarding how they calculate them. Some companies make changes to their calculation method over time. The specific action flagging users as active greatly impacts the quality of the data if it does not accurately reflect engagement with the product, resulting in misleading data. Basic actions such as logging into the product may not be an accurate representation of customer engagement and inflate the number of active users, while uploading content or commenting may be too specific for a product and under-represent user activity.
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