- Number
- 11494814
- Published
- 2022-11-08
- Filed
- 2017-08-25
- Assignee
- Disney Enterprises, Inc.
- Inventors
- Schnoor; Dustin A., Parker; David T., Denslow; Thomas W., Ruddy; John B.
- CPC
- G06N20/00; G06N5/022; G06N20/20; G06Q30/0282
- Verdict
- Set aside ratings-forecast predictive analytics, business
- Source
- Google Patents · FreePatentsOnline
Abstract
A technique for predictive modeling to generate ratings forecasts in a media network is described. An episode-level programming schedule is imported into a viewership forecasting application to generate episode-level ratings predictions. Episode-level ratings predictions for media content in the episode-level programming schedule are generated by implementing multiple different predictive algorithms in parallel for each instance of specific media content in the programming schedule. In addition, for each such predicted viewership value, an accuracy value is generated that indicates the likely accuracy of that predicted viewership value. The episode-level ratings predictions can be uploaded by a business unit of the media network, and merged with a programming schedule currently employed by the business unit.
Background
BACKGROUND OF THE INVENTION Field of the Invention (1) The present disclosure relates generally to computer science and forecasting techniques, and, more specifically, to predictive modeling techniques for generating ratings forecasts. Description of the Related Art (2) As digital and mobile communications have become ubiquitous, the consumption environment for media content has become increasingly complex. Thus, there are now many options for end users to enjoy media content besides the traditional watching broadcast television (TV) and watching movies in-theater. For example, end users can now stream free and subscription content to televisions or mobile devices, rent DVDs, purchase pay-per-view rights to specific digital content, and so on. Accordingly, producers and publishers of media content can now offer to potential customers, such as advertisers, many new advertising opportunities. (3) To effectively leverage the diverse advertising products that are now available to advertisers, media content providers need to quantifiably demonstrate to potential advertisers that a particular instance of content is an effective vehicle for marketing the goods or services of those advertisers. In that regard, the predicted viewership for a particular instance of media content, such as a particular TV presentation, can enable a media content provider to successfully market advertising time associated with that instance of media content. Thus, accurately predicting the expected viewer
Claims
1. A method for scheduling an instance of media content, the method comprising: generating, via a first predictive modeling algorithm, a first predicted viewership value for a future showing of a first instance of media content; receiving one or more second predicted viewership values previously generated via the first predictive modeling algorithm for the first instance of media content shown at one or more historical times; determining a different media content that shares at least one shared programming attribute with the first instance of media content; receiving historical viewership data for one or more broadcast instances of the different media content; calculating a first accuracy value for the first predicted viewership value based on a comparison of the one or more second predicted viewership values with the historical viewership data for the one or more broadcast instances of the different media content; generating, via a second predictive modeling algorithm, a third predicted viewership value for the future showing of the first instance of media content, wherein the second predictive modeling algorithm comprises a machine-learning-based algorithm; receiving one or more fourth predicted viewership values previously generated via the second predictive modeling algorithm for the first instance of media content shown at the one or more historical times; calculating a second accuracy value for the third predicted viewership value based on a comparison of the one or more fourth predicted viewership values with the historical viewership data for the one or more broadcast instances of the different media content; selecting either the first predicted viewership value or the third predicted viewership value as a recommended predicted viewership value for the future showing of the first instance of media content based on a comparison of the first accuracy value and the second accuracy value; and setting at least one time to show the first instance of media content based on the recommended predicted viewership value. ||
9. A non-transitory computer-readable storage medium including instructions that, when executed by a processor included in a computing device, cause the processor to perform the steps of: generating, via a first predictive modeling algorithm, a first predicted viewership value for a future showing of a first instance of media content; receiving one or more second predicted viewership values previously generated via the first predictive modeling algorithm for the first instance of media content shown at one or more historical times; determining a different media content that shares at least one shared programming attribute with the first instance of media content; receiving historical viewership data for one or more broadcast instances of the different media content; calculating a first accuracy value for the first predicted viewership value based on a comparison of the one or more second predicted viewership values with the historical viewership data for the one or more broadcast instances of the different media content; generating, via a second predictive modeling algorithm, a third predicted viewership value for the future showing of the first instance of media content, wherein the second predictive modeling algorithm comprises a machine-learning-based algorithm; receiving one or more fourth predicted viewership values previously generated via the second predictive modeling algorithm for the first instance of media content shown at the one or more historical times; calculating a second accuracy value for the third predicted viewership value based on a comparison of the one or more fourth predicted viewership values with the historical viewership data for the one or more broadcast instances of the different media content; selecting either the first predicted viewership value or the third predicted viewership value as a recommended predicted viewership value for the future showing of the first instance of media content based on a comparison of the first accuracy value and the second accuracy value; and setting at least one time to show the first instance of media content based on the recommended predicted viewership value. ||
17. A computing device, the computing device comprising: a memory that stores instructions; and a processor that is coupled to the memory and, when executing the instructions, performs the steps of: generating, via a first predictive modeling algorithm, a first predicted viewership value for a future showing of a first instance of media content, receiving one or more second predicted viewership values previously generated via the first predictive modeling algorithm for the first instance of media content shown at one or more historical times, determining a different media content that shares at least one shared programming attribute with the first instance of media content, receiving historical viewership data for one or more broadcast instances of the different media content, calculating a first accuracy value for the first predicted viewership value based on a comparison of the one or more second predicted viewership values with the historical viewership data for the one or more broadcast instances of the different media content, generating, via a second predictive modeling algorithm, a third predicted viewership value for the future showing of the first instance of media content, wherein the second predictive modeling algorithm comprises a machine-learning-based algorithm, receiving one or more fourth predicted viewership values previously generated via the second predictive modeling algorithm for the first instance of media content shown at the one or more historical times, calculating a second accuracy value for the third predicted viewership value based on a comparison of the one or more fourth predicted viewership values with the historical viewership data for the one or more broadcast instances of the different media content, selecting either the first predicted viewership value or the third predicted viewership value as a recommended predicted viewership value for the future showing of the first instance of media content based on a comparison of the first accuracy value and the second accuracy value, and setting at least one time to show the first instance of media content based on the recommended predicted viewership value.