Probabilistic modeling is a method of using probability theory to model uncertain events or systems. It uses mathematical equations and algorithms to represent the likelihood of different outcomes or states based on available information and assumptions. Probabilistic models can be used to make predictions about future events or to understand the underlying processes that drive a system.
Probabilistic modeling can be used in mobile attribution to determine the likelihood that a specific ad or marketing campaign led to a user installing an app or making a purchase. In this context, probabilistic modeling is used to establish a causal link between the ad exposure and the user's action. The process typically involves collecting data on users' interactions with ads and their actions, such as app installs and in-app purchases. This data is then used to create a probabilistic model that can estimate the probability that a particular ad or campaign was the cause of a user's action. The model considers various factors, such as the ad exposure's timing, the user's location, and the device they are using. Then, it assigns a probability score to each ad exposure, indicating how likely it is that the ad exposure led to the conversion. These probability scores are then used to determine which ad campaigns are most effective in driving conversions and, as a result, which campaigns to allocate more budget to and which to stop. By assigning a probability to each ad or campaign, mobile attribution can identify which marketing efforts are most effective in driving conversions.
