Media Plan Generation Using Ridge Regression Analysis
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Solution Overview
Problem
Traditional television advertising campaigns face inefficiencies in targeting the right audience, leading to wasted budget and a manual, iterative process that is time-consuming and costly, with existing methods failing to optimize key metrics like Cost-per-Mille (CPM) and reach effectively.
Innovation Solution
The use of ridge regression analysis and greedy heuristic algorithms to generate media plans based on consumption data from set-top boxes, predicting target impressions and optimizing advertisement slot placement to minimize costs while maximizing reach, using a combination of cost-per-mille and reach criteria.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional manual media planning methods are used, then ease of operation is maintained, but productivity is low and time-consuming
Solution Approach 1:
The patent replaces manual mechanical media planning processes with automated computer-based systems that use algorithms to generate media plans. The system automatically processes consumption data, applies optimization algorithms, and generates media plans without manual intervention, thereby increasing productivity while managing complexity through software automation.
Solution Approach 2:
The system enables self-service media planning by automatically generating media plans based on consumption data and campaign criteria. The automated system performs analysis, optimization, and plan generation independently without requiring manual expert intervention, allowing rapid campaign creation while the system manages its own complexity internally.
2Measurement precision
If traditional advertising methods are used, then device complexity is low, but measurement precision of advertising effectiveness is insufficient
Solution Approach 1:
The system incorporates feedback mechanisms by analyzing actual consumption data from set-top boxes and using this information to optimize media plans. The system continuously monitors advertising effectiveness metrics and adjusts plans based on real performance data, improving measurement precision of CPM and reach through iterative optimization based on actual viewer behavior feedback.
Solution Approach 2:
The patent replaces simple manual measurement methods with sophisticated computer-based analytics systems that process consumption data to precisely measure advertising effectiveness. The system uses algorithms to calculate accurate CPM and reach metrics by analyzing actual viewing patterns, replacing imprecise manual estimation with precise automated measurement while managing the complexity through computational methods.
3Loss of time
If manual media planning is used, then ease of operation is high, but loss of time is excessive
Solution Approach 1:
The system performs preliminary actions by pre-processing consumption data, pre-calculating optimization parameters, and pre-generating media plans before actual campaign execution. The system prepares all necessary analysis and planning work in advance using automated algorithms, eliminating the need for time-consuming manual planning during campaign development and significantly reducing overall time loss.
Solution Approach 2:
The patent replaces time-consuming manual planning operations with automated computer systems that rapidly process data and generate media plans. The automation substitutes human manual operations with computational algorithms that execute much faster, reducing campaign development time while the system manages its own operational complexity internally.
4Manufacturing precision
If traditional advertising optimization is used, then device complexity is low, but manufacturing precision of audience targeting is insufficient
Solution Approach 1:
The system achieves precise audience targeting by dynamically changing and optimizing parameters such as CPM bids, reach targets, and slot selections based on consumption data. The system adjusts these parameters through optimization algorithms to precisely match advertising campaigns with target audiences, improving targeting precision while the system manages the complexity of parameter optimization internally.
Solution Approach 2:
The patent replaces simple manual targeting methods with sophisticated computational optimization algorithms that automatically adjust targeting parameters. The system uses computer-based algorithms to process consumption data and generate precise audience targeting strategies, achieving high manufacturing precision in audience selection while managing the complexity through automated computational methods rather than manual processes.
Data Source
AI summary
A system that incorporates teachings of the subject disclosure may include, for example, determining identified impressions that are detected from consumption data collected from a group of media processors where the identified impressions represent viewing of selected content and where the consumption data indicates channel tuning events at the group of media processors including changing of channels, applying a ridge regression analysis to the identified impressions to determine a predicted number of target impressions per advertisement slot, and generating a media plan based on a ratio of an advertisement slot cost to the predicted number of target impressions per advertisement slot. Other embodiments are disclosed.