Algorithmic modeling in Audience Manager refers to the use of data science to either expand your existing audiences or classify them into personas.
This is done through two types of algorithms: Look-Alike Modeling and Predictive Audiences.
Look-Alike Modeling helps you discover new, unique audiences through automated data analysis. The process starts when you select a trait or segment, a time interval, and first and third-party data sources. Your choices provide the inputs for the algorithmic model. When the analytics process runs, it looks for eligible users based on shared characteristics from the selected population.
Upon completion, this data is available in Trait Builder where you can use it to create traits based on accuracy and reach. Additionally, you can build segments that combine algorithmic traits with rules-based traits and add other qualification requirements with Boolean expressions and comparison operators.
Look-Alike Modeling gives you a dynamic way to extract value from all your available trait data.
To learn more about Look-Alike Modeling, see Understanding Look-Alike Modeling.
Predictive Audiences helps you classify an unknown audience into distinct personas, in real-time, using advanced data science techniques.
In a marketing context, a persona is an audience segment defined by visitors, users, or potential buyers, who share a specific set of traits, such as demographics, browsing habits, shopping history, etc.
Predictive Audiences models take this concept a step further, by using Audience Manager’s machine learning capabilities to automatically classify unknown audiences into distinct personas. Audience Manager achieves this by calculating the propensity of your unknown audience for a set of known audiences.
To learn more about Predictive Audiences, see Predictive Audiences Overview.