Automated Advertisement Selection Using a Trained Predictive Model

Number: 20200143414
Published: 2020-05-07
Filed: 2019-10-30
Assignee: Disney Enterprises, Inc.
Inventors: Li; Binbin, Conrad; Amanda, Asselin; Simon, Auslander; Jamie, Rangsikitpho; Joshua, Shi; Zhenyu, Vondrak; Alexander
CPC: G06N3/08; G06Q30/0201; G06Q30/0204; G06N3/09; G06Q30/0251; G06F18/2148; G06F18/24323; G06Q30/0243; G06N20/20; G06N5/01
Verdict: Set aside ad-tech predictive model
Source: Google Patents · FreePatentsOnline

Abstract

An automated advertisement selection system includes a computing platform having a hardware processor and a system memory storing a software code including a trained predictive model and a scoring module. The hardware processor executes the software code to receive an advertising query, the advertising query including a multiple parameters describing a target consumer group, and to identify, using the trained predictive model, candidate advertisements for the target consumer group based on the multiple parameters. The hardware processor also executes the software code to determine, using the scoring module, desirability scores for each one of the plurality of candidate advertisements, each of the desirability scores corresponding to a likelihood of each respective one of the plurality of candidate advertisements enticing the target consumer group, and to select one of the plurality of candidate advertisements based on the desirability scores for distribution to the target consumer group.

Background

BACKGROUND

Advertising campaign strategies are increasingly reliant on the collection of vast amounts of data regarding potential customers to determine when and where to target advertisements in order to best ensure a successful campaign. Such large data collections are often referred to simply as “big data,” which is an expression defined, for example, by the online encyclopedia Wikipedia® as “data sets that are so voluminous and complex that traditional data-processing application software are inadequate to deal with them.”

Due to its very volume, big data can be difficult to analyze and use effectively in shaping an advertising strategy. For example, while a consumer may be expected to align according to traditional metrics such as age group, geography, or other demographic criteria identifiable through the filtering of big data, an advertisement targeted to the consumer based on those metrics may yet be received with indifference or even hostility. However, failure to consistently target consumers with advertising that is appealing to them can undesirably reduce the anticipated return on investment (ROI) of the advertising campaign, and may even compromise the overall success of the campaign.SUMMARY

There are provided systems and methods for automating advertisement selection using a predictive model, substantially as shown in and/or described in connection with at least one of the figures, and as set forth more completely in the claims.

Claims

1. An automated advertisement selection system comprising: a computing platform including a hardware processor and a system memory; a software code stored in the system memory, the software code including a trained predictive model and a scoring module; the hardware processor configured to execute the software code to: receive an advertising query, the advertising query including a plurality of parameters describing a target consumer group; identify, using the trained predictive model, a plurality of candidate advertisements for the target consumer group based on the plurality of parameters; determine, using the scoring module, desirability scores for each one of the plurality of candidate advertisements, each of the desirability scores corresponding to a likelihood of each respective one of the plurality of candidate advertisements enticing the target consumer group; and select one of the plurality of candidate advertisements based on the desirability scores for distribution to the target consumer group. 11. A method for use by an automated advertisement selection system including a computing platform having a hardware processor and a system memory, the system memory including a trained predictive model and a scoring module, the method comprising: receiving, by the software code executed by the hardware processor, an advertising query, the advertising query including a plurality of parameters describing a target consumer group; identifying, by the software code executed by the hardware processor and using the trained predictive model, a plurality of candidate advertisements for the target consumer group based on the plurality of parameters; determining, by the software code executed by the hardware processor and using the scoring module, desirability scores for each one of the plurality of candidate advertisements, the desirability scores corresponding to a likelihood of each respective one of the plurality of candidate advertisements enticing the target consumer group; and selecting, by the software code executed by the hardware processor, one of the plurality of candidate advertisements based on the desirability scores for distribution to the target consumer group.