Audience Model Inference for TV Ad Pricing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Traditional television advertising lacks audience information, making it difficult for buyers to make informed purchasing decisions and leading to suboptimal pricing and inventory management.
Innovation Solution
A computer system infers the target audience for advertisement placement opportunities by correlating order data with audience characteristics associated with the television program, generating detailed demand target data that includes additional attributes beyond traditional demographic information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional airtime information is provided to buyers, then the advertising opportunity can be sold, but the audience information is insufficient making it difficult for buyers to make informed purchasing decisions
Solution Approach 1:
The system performs preliminary actions by collecting audience measurement data and characteristics data before the advertising purchase decision is made. Audience models are pre-computed and stored, containing detailed demographic, psychographic, and behavioral attributes. When a buyer requests information about an advertising opportunity, the system has already prepared the audience insights, enabling informed decision-making without requiring complex real-time analysis.
Solution Approach 2:
The patent introduces an intermediary component - the audience model - that mediates between the airtime information and the buyer's decision-making process. The audience model acts as a bridge, translating raw audience measurement data and program characteristics into actionable insights about target audiences. This intermediary layer provides comprehensive audience information without requiring the buyer to directly process complex raw data.
2Productivity
If buyers make purchasing decisions based on incomplete airtime information, then transactions can occur quickly, but the pricing is suboptimal and inventory management is inefficient
Solution Approach 1:
The system implements feedback mechanisms where audience model data is continuously refined based on actual viewing measurements and program characteristics. The audience models are updated and improved over time, providing increasingly accurate predictions of audience composition. This feedback loop enables both efficient transactions and precise pricing, as the system learns from accumulated data to improve future pricing and matching of advertising opportunities to buyer needs.
Solution Approach 2:
The patent transforms the pricing and evaluation parameters from simple airtime metrics to comprehensive audience-based metrics. Instead of pricing based solely on time slot and program popularity, the system uses multiple parameters from audience models including demographic breakdowns, psychographic profiles, behavioral attributes, and predicted audience size. This parameter transformation enables more accurate pricing that reflects the true value of reaching specific target audiences.
Data Source
AI summary
A system draws an inference about the audience being targeted by the buyer of an advertisement placement opportunity by correlating the order for the advertisement placement opportunity with characteristics of the audience associated with the television program in which the advertisement placement opportunity is embedded. Sellers of advertisement placement opportunities may use the inferred target audience information to appropriately price their advertising inventories and to fulfill orders more effectively.