Abstract: Methods and systems are provided for determining advertising reach based on machine learning. In particular, a reach calculator is provided to determine reach for advertisement campaigns in real time through the use of machine learning. The reach calculator increases the speed at which reach calculations can be done by using a trained machine learning model and a set of aggregated features as opposed to using a direct calculation approach that directly analyzes a massive amount of user data.

Abstract: Methods and systems are disclosed for an optimization system that recommends amounts of resources for use in reaching viewers during a media asset. The optimization system, using control circuitry, receives a user input of a first value of a resource and determines a first rate at which a number of unique household viewers reached during a first media asset changes for the first value. The optimization system compares the first rate to a threshold rate and determines whether the first rate equals or exceeds the threshold rate. The optimization system, in response to determining that the first rate does not equal or exceed the threshold rate, recommends a second value to the user.

Abstract: Methods and systems for performing non-linear optimization of reach are described herein. The methods and systems may be used to compute the reach associated with different advertisement campaigns involving different combinations of spots. The method includes computing probabilities that a user will access a certain spot. The method includes retrieving a weight associated with the targeted user demographic. The method includes computing a first reach value using a first non-linear function, and a second reach value using a second non-linear function. The method includes comparing the two reach values to each other and to a predetermined reach threshold. The higher reach value larger than the predetermined reach threshold is selected and information associated with the selected reach value is provided to the user.

Abstract: Methods and systems for performing non-linear optimization of reach are described herein. The methods and systems may be used to compute the reach associated with different advertisement campaigns involving different combinations of spots. The method includes computing probabilities that a user will access a certain spot. The method includes retrieving a weight associated with the targeted user demographic. The method includes computing a first reach value using a first non-linear function, and a second reach value using a second non-linear function. The method includes comparing the two reach values to each other and to a predetermined reach threshold. The higher reach value larger than the predetermined reach threshold is selected and information associated with the selected reach value is provided to the user.

Abstract: Methods and systems are provided for determining advertising reach based on machine learning. In particular, a reach calculator is provided to determine reach for advertisement campaigns in real time through the use of machine learning. The reach calculator increases the speed at which reach calculations can be done by using a trained machine learning model and a set of aggregated features as opposed to using a direct calculation approach that directly analyzes a massive amount of user data.

Abstract: Methods and systems are disclosed for an optimization system that recommends amounts of resources for use in reaching viewers during a media asset. The optimization system, using control circuitry, receives a user input of a first value of a resource and determines a first rate at which a number of unique household viewers reached during a first media asset changes for the first value. The optimization system compares the first rate to a threshold rate and determines whether the first rate equals or exceeds the threshold rate. The optimization system, in response to determining that the first rate does not equal or exceed the threshold rate, recommends a second value to the user.