Ad Scheduling Tool with Net Difference Metric Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Human operators face a high task load when manually scheduling ads, as they need to consider various business conditions and potential alternatives for placing unplaced ads into a schedule, which can be time-consuming and inefficient.
Innovation Solution
A scheduling system that analyzes ad placement options using a hardware processor to determine net difference metric values for each placement scenario, sorting options based on value metrics such as monetary value and priority, and presenting them to operators to guide optimal ad placement decisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automatic schedulers are used for scheduling ads, then scheduling efficiency is improved, but human operators still need to manually schedule ads that cannot be automatically placed, resulting in high task load
Solution Approach 1:
The system introduces an intermediary tool that sits between the automatic scheduler and the operator. This tool analyzes ad placement options, calculates net difference metric values, and presents sorted recommendations to operators. It acts as a mediator that handles the complex analysis work, leaving operators to only make final decisions based on presented options, thus reducing their task load while maintaining scheduling efficiency.
Solution Approach 2:
The system enables self-service by allowing the scheduling tool to automatically analyze placement options, calculate metrics, and generate sorted lists of recommendations without requiring operator intervention for each analysis step. The tool serves itself by autonomously performing the complex evaluation work, freeing operators from manual analysis tasks.
2Measurement precision
If operators manually place unplaced ads by considering all alternatives, then ad placement accuracy is improved, but the process becomes time-consuming and inefficient
Solution Approach 1:
The system performs preliminary action by automatically analyzing all possible ad placement options and calculating their net difference metric values before presenting them to the operator. This pre-computation of all alternatives with their metrics allows operators to make accurate decisions quickly, as the time-consuming analysis work has already been completed in advance.
Solution Approach 2:
The system changes parameters by calculating and presenting multiple placement options with different net difference metric values. By transforming the complex decision problem into a sorted list of options with quantified metrics, the system enables operators to make accurate placement decisions based on objective parameter comparisons rather than manual evaluation of all alternatives.
3Productivity
If operators must track various potential alternatives when deciding ad placement, then placement optimization is improved, but cognitive load and complexity increase
Solution Approach 1:
The system applies segmentation by breaking down the complex ad placement decision into separate, manageable components. Each placement option is analyzed independently with its own net difference metric value calculated and presented. This segmentation of the decision problem into discrete, evaluated alternatives reduces the cognitive load on operators while maintaining optimization quality.
Solution Approach 2:
The system reduces decision complexity by changing the presentation parameters from a set of unstructured alternatives to a sorted list with quantified net difference metric values. This parameter transformation converts a complex multi-factor decision problem into a simpler ranking problem, where operators can focus on comparing metric values rather than tracking multiple unstructured alternatives.
Data Source
AI summary
A system and method for providing options for scheduling ads is provided. In example embodiments, a list of one or more ads is presented to an operator. A selection of an ad to be placed into a schedule is received. Placement analysis is performed, using a hardware processor, to identify a plurality of placement options based on placing the ad into the schedule. The placement analysis includes determining a net difference metric value for each placement option. The net difference metric value is determined based on displacement of at least one previously scheduled ad in each placement option. The plurality of placement options are sorted according to a score derived from at least one value metric to create a result. The result is presented to the operator, whereby the result includes the score derived from the at least one value metric for each placement option.