Advertising Schedule Optimization via Demographic Data Filtering
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Solution Overview
Problem
Current techniques for predicting and achieving Gross Rating Points (GRP) performance in advertising campaigns are cumbersome and less than perfect, making it difficult for radio stations and advertisers to effectively measure and optimize advertising reach and effectiveness.
Innovation Solution
A system that uses historic data filtering based on consumer demographic information, adjusts scheduling parameters, and automatically reschedules advertisements to maintain predicted GRP values within specified boundaries, ensuring optimal advertising performance by selecting high-scoring advertisement breaks and adjusting scheduling parameters such as time and station identifiers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current techniques are used to predict GRP performance, then advertisers can measure advertising reach, but the process is cumbersome and less than perfect
Solution Approach 1:
The system changes parameters by using filtered historic data with multiple filtering criteria (station, day, time, demographic) to generate predicted GRP values. This transforms the prediction process from cumbersome manual calculations to automated parameter-based predictions that are both accurate and efficient
Solution Approach 2:
The patent introduces an intermediary computing system that acts as a mediator between historic data and GRP predictions. This system filters historic data, applies demographic information, and generates predicted GRP values, simplifying the overall process while improving accuracy
2Productivity
If advertising schedules are manually adjusted, then GRP performance can be monitored, but real-time updates and optimizations are difficult to achieve
Solution Approach 1:
The system implements feedback by continuously monitoring predicted GRP values against target ranges and automatically adjusting advertising schedules. When predictions fall outside acceptable ranges, the system reschedules advertisements to optimize performance, creating a closed-loop feedback system that improves productivity without manual intervention
Solution Approach 2:
The advertising schedule optimization performs self-service by automatically detecting when GRP predictions need adjustment and executing rescheduling without human intervention. The system monitors its own performance and makes corrections autonomously, eliminating time losses associated with manual schedule management
3Measurement precision
If multiple filtering criteria are applied to historic data, then predicted GRP values become more accurate, but data processing complexity increases
Solution Approach 1:
The patent applies segmentation by dividing historic data into distinct segments based on multiple filtering criteria (station identifier, day of week, time period, demographic information). This segmentation organizes complex data into manageable segments that can be processed systematically, achieving accurate predictions without overwhelming complexity
Data Source
AI summary
Multiple filtering criteria derived from consumer surveys can be used to adjust an advertising schedule to achieve improved performance. Historical performance data related to performance factors, such as gross rating points, can be filtered using the multiple filtering criteria. The filtered data can then be used as the basis for predicting future performance of an advertisement or advertising campaign. The historical data can be updated as new performance data becomes available, and the predicted future performance updated. An advertising schedule can be adjusted to maintain performance factors within designated upper and lower limits.