Determining targeting information based on a predictive targeting model

Abstract

A targeting system based on a predictive targeting model based on observed behavioral data including visit data, user profile and/or survey data, and geographic features associated with a geographic region. The predictive targeting model analyzes the observed behavioral data and the geographic features data to predict conversion rates for every cell in a square grid of predefined size on the geographic region. The conversion rate of a cell indicates a likelihood that any random user in that cell will perform a targeted behavior.

We claim: 
     
         1 . A method for use by at least one data processing device, the method comprising:
 receiving targeting criteria, including a targeted behavior and a geographic region;   segmenting the geographic region using a grid into cells, wherein each cell has a cell identifier;   receiving behavioral information associated with multiple users, wherein the behavioral information includes time-stamped place visit data corresponding to visits to places by the multiple users;   calculating a behavior match metric for each cell based on the behavioral information;   receiving feature data for each cell;   labeling the feature data for each cell using the behavior match metric for the corresponding cell to obtain labeled feature data;   training a model for predicting a conversion rate of each cell based on a set of the labeled feature data, wherein the conversion rate provides a probability of a user in a cell performing the targeted behavior;   applying the model to the feature data to predict the conversion rate of each cell; and   identifying targeting information based on the conversion rates of the cells.