Ad Targeting via Demographic Segmentation and Relevance Scoring
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Solution Overview
Problem
Current methods for providing targeted advertising to television set-top boxes lack effective individual-specific targeting, leading to suboptimal ad relevance and yield for television networks and advertisers.
Innovation Solution
The system recommends television ad placement by identifying media slots, obtaining program information with viewing data, calculating relevance of advertiser industries for each program, and generating recommendations based on demographic matching between viewers and potential buyers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional television advertising methods are used with loose constraints on ad placement, then network revenue generation is maintained at high levels, but ad relevance and targeting precision deteriorate
Solution Approach 1:
The patent segments the television audience into distinct demographic groups (e.g., age, gender, income levels) and segments ad inventory into specific time slots and programs. This segmentation enables precise targeting of ads to specific demographic segments during specific programs, improving ad relevance while maintaining yield through optimized placement decisions.
Solution Approach 2:
The system dynamically adjusts ad placement decisions based on real-time viewing data and demographic information. The ad insertion strategy changes adaptively according to the program being aired, the audience demographics, and available inventory, allowing the system to optimize both relevance and yield dynamically rather than using static placement rules.
2Measurement precision
If individual-specific targeting is implemented, then ad relevance for individual viewers is improved, but system complexity and data processing requirements worsen
Solution Approach 1:
Instead of creating individual profiles for each viewer, the system segments the audience into demographic groups and uses program-level viewing data. This approach achieves individual-specific targeting effects without requiring complex individual-level tracking, reducing system complexity while maintaining precision through demographic segmentation and program context analysis.
Solution Approach 2:
The patent uses program information and viewing data as intermediaries to infer audience demographics and preferences. Rather than directly analyzing individual viewer behavior, the system uses program characteristics and aggregate viewing patterns as mediators to determine appropriate ad placements, simplifying the targeting mechanism while achieving precise demographic targeting.
3Productivity
If multiple advertisers are targeted with optimized ad placement, then advertiser yield is maximized, but the complexity of calculating and managing multiple relevance scores increases
Solution Approach 1:
The system segments advertisers into industry categories and segments programs by content type and audience demographics. By organizing both advertisers and programs into segments, the system can calculate relevance scores more efficiently by matching segments rather than evaluating every possible advertiser-program combination individually, reducing computational complexity while maximizing yield across multiple advertisers.
Solution Approach 2:
The patent performs preliminary calculations of relevance scores for different advertiser-industry combinations against program demographics before actual ad placement decisions are made. This preliminary scoring allows the system to pre-rank potential ad placements and make optimized decisions faster during actual ad insertion, reducing real-time computational complexity while maintaining high advertiser yield.
Data Source
AI summary
Systems and methods are disclosed for targeting of advertising content for a consumer product, by obtaining consumer demographic data, the consumer demographic data including a plurality of demographic attributes for each person; identifying a plurality of media slots; and obtaining program information for a respective identified program aired in each media slot among the plurality of media slots, the program information including viewing data of a plurality of viewing persons viewing the program and each viewing person being among the plurality of persons. The methods also include enriching the viewing data with the consumer demographic data; identifying a plurality of advertiser industries; enriching the product purchaser data with the consumer demographic data; calculating a relevance of each advertiser industry among the plurality of advertiser industries for each identified program based on demographic attributes of the product purchasers in each advertiser industry and demographic attributes of the viewing persons.