- Number
- 20250371565
- Published
- 2025-12-04
- Filed
- 2025-04-15
- Assignee
- Disney Enterprises, Inc.
- Inventors
- STEUBER; Tara L. et al.
- CPC
- G06Q30/0202; G06Q30/0204
- Verdict
- Set aside ad/marketing forecast models, business
- Source
- Google Patents · FreePatentsOnline
Abstract
Embodiments provide for improved machine learning. A first distribution plan for content is accessed, where the first distribution plan comprises a first target segment and identifies a first set of distribution outlets. A base segment corresponding to the target segment is determined, where the target segment is defined based on a plurality of member attributes and the base segment is defined based on a subset of the plurality of member attributes. A set of forecasts is generated using, for each respective distribution outlet of the first set of distribution outlets, a respective machine learning model trained based on the base segment. A forecasted reach metric for the first distribution is generated plan based on the set of forecasts.
Background
BACKGROUND
Reach and frequency are important metrics for a variety of distribution plans, including a wide variety of marketing campaigns. Typically, reach and frequency are calculated post-campaign based on viewers of content compared to the overall potential viewing universe. Forecasting reach and frequency, prior to a campaign, is a challenging problem.
Claims
1. A method, comprising: accessing a first distribution plan for content, wherein the first distribution plan comprises a first target segment and identifies a first set of distribution outlets; determining a base segment corresponding to the target segment, wherein the target segment is defined based on a plurality of member attributes and the base segment is defined based on a subset of the plurality of member attributes; generating a set of forecasts using, for each respective distribution outlet of the first set of distribution outlets, a respective machine learning model trained based on the base segment; and generating a forecasted reach metric for the first distribution plan based on the set of forecasts. ||
10. One or more non-transitory computer readable media containing, in any combination, computer program code that, when executed by operation of any combination of one or more processors, performs an operation comprising: accessing a first distribution plan for content, wherein the first distribution plan comprises a first target segment and identifies a first set of distribution outlets; determining a base segment corresponding to the target segment, wherein the target segment is defined based on a plurality of member attributes and the base segment is defined based on a subset of the plurality of member attributes; generating a set of forecasts using, for each respective distribution outlet of the first set of distribution outlets, a respective machine learning model trained based on the base segment; and generating a forecasted reach metric for the first distribution plan based on the set of forecasts. ||
16. A system, comprising: one or more processors; and one or more memories storing a program, which, when executed on any combination of the one or more processors, performs operations, the operations comprising: accessing a first distribution plan for content, wherein the first distribution plan comprises a first target segment and identifies a first set of distribution outlets; determining a base segment corresponding to the target segment, wherein the target segment is defined based on a plurality of member attributes and the base segment is defined based on a subset of the plurality of member attributes; generating a set of forecasts using, for each respective distribution outlet of the first set of distribution outlets, a respective machine learning model trained based on the base segment; and generating a forecasted reach metric for the first distribution plan based on the set of forecasts.