SYSTEM AND METHOD FOR ASSESSING DIGITAL CONTENT PRESENTATIONS
A computer-implemented method and a system are provided for estimating values for user events associated with digital content presentations. An example method includes: providing data having a plurality of targeting features for a plurality of users of a software application; performing regression analyses to generate a first predictive model and a second predictive model, wherein the first predictive model is configured to receive targeting features as input and provide as output a prediction of an amount of revenue generated per payer, and wherein the second predictive model is configured to receive at least one targeting feature as input and provide as output a prediction of a number of payers per user event; using the first and second models to determine a value of a user event for a set of targeting parameters; and facilitating a presentation of content on a plurality of client devices based on the determined value.
1 . A computer-implemented method for determining a value of a user event, the method comprising:
providing data comprising a plurality of targeting features for a plurality of users of a software application;
identifying a plurality of payers within the plurality of users;
performing a first regression analysis on the data to generate a first predictive model, the first predictive model being configured to receive at least one targeting feature as input and provide as output a prediction of an amount of revenue generated per payer for the software application;
performing a second regression analysis on the data to generate a second predictive model, the second predictive model being configured to receive at least one targeting feature as input and provide as output a prediction of a number of payers per user event for the software application;
providing a set of targeting parameters to the first predictive model and the second predictive model;
receiving outputs from the first predictive model and the second predictive model;
determining a value of the user event based on a combination of the outputs; and
facilitating a presentation of content on a plurality of client devices based on the determined value.
2 . The method of claim 1 , wherein the data comprises a history of user interactions with the software application.
3 . The method of claim 1 , wherein the targeting features comprise at least one of a user segment and an external feature.
4 . The method of claim 1 , wherein performing the first regression analysis comprises calculating, based on the data, an amount of revenue generated by each payer for the software application.
5 . The method of claim 1 , wherein performing the second regression analysis comprises calculating, based on the data, a number of payers per user event.
6 . The method of claim 1 , wherein at least one of the first predictive model and the second predictive model comprises a Random Forest model.
7 . The method of claim 1 , wherein the user event comprises at least one of an installation of the software application and a user accomplishment in the software application.
8 . The method of claim 1 , wherein the number of payers per user event comprises a payer-to-install ratio.
9 . The method of claim 1 , wherein providing the set of targeting parameters comprises determining the set of targeting parameters for a group of prospective users of the software application.
10 . The method of claim 1 , wherein determining the value of the user event comprises multiplying output from the first predictive model by output from the second predictive model.
11 . A system, comprising:
one or more computer processors programmed to perform operations comprising:
providing data comprising a plurality of targeting features for a plurality of users of a software application;
identifying a plurality of payers within the plurality of users;
performing a first regression analysis on the data to generate a first predictive model, the first predictive model being configured to receive at least one targeting feature as input and provide as output a prediction of an amount of revenue generated per payer for the software application;
performing a second regression analysis on the data to generate a second predictive model, the second predictive model being configured to receive at least one targeting feature as input and provide as output a prediction of a number of payers per user event for the software application;
providing a set of targeting parameters to the first predictive model and the second predictive model;
receiving outputs from the first predictive model and the second predictive model;
determining a value of the user event based on a combination of the outputs; and
facilitating a presentation of content on a plurality of client devices based on the determined value.
12 . The system of claim 11 , wherein the targeting features comprise at least one of a user segment and an external feature.
13 . The system of claim 11 , wherein performing the first regression analysis comprises calculating, based on the data, an amount of revenue generated by each payer for the software application.
14 . The system of claim 11 , wherein performing the second regression analysis comprises calculating, based on the data, a number of payers per user event.
15 . The system of claim 11 , wherein at least one of the first predictive model and the second predictive model comprises a Random Forest model.
16 . The system of claim 11 , wherein the user event comprises at least one of an installation of the software application and a user accomplishment in the software application.
17 . The system of claim 11 , wherein the number of payers per user event comprises a payer-to-install ratio.
18 . The system of claim 11 , wherein providing the set of targeting parameters comprises determining the set of targeting parameters for a group of prospective users of the software application.
19 . The system of claim 11 , wherein determining the value of the user event comprises multiplying output from the first predictive model by output from the second predictive model.
20 . An article, comprising:
a non-transitory computer-readable medium having instructions stored thereon that, when executed by one or more computer processors, cause the computer processors to perform operations comprising:
providing data comprising a plurality of targeting features for a plurality of users of a software application;
identifying a plurality of payers within the plurality of users;
performing a first regression analysis on the data to generate a first predictive model, the first predictive model being configured to receive at least one targeting feature as input and provide as output a prediction of an amount of revenue generated per payer for the software application;
performing a second regression analysis on the data to generate a second predictive model, the second predictive model being configured to receive at least one targeting feature as input and provide as output a prediction of a number of payers per user event for the software application;
providing a set of targeting parameters to the first predictive model and the second predictive model;
receiving outputs from the first predictive model and the second predictive model;
determining a value of the user event based on a combination of the outputs; and
facilitating a presentation of content on a plurality of client devices based on the determined valu