Online Ad Position Bidding Optimization System
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Solution Overview
Problem
Conventional systems lack a method to optimize marketing purchases across multiple advertising options to meet specific composite goals and performance criteria, leading to inefficient allocation of resources and potential high costs.
Innovation Solution
A system that models various marketing options using empirical data, performs regression analysis to forecast clicks and revenues, and optimizes marketing decisions through mathematical programming to allocate budgets effectively across different advertising channels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If marketing purchases are optimized individually for each marketing option, then decision-making simplicity is maintained, but overall marketing efficiency and resource allocation effectiveness deteriorate
Solution Approach 1:
The patent combines multiple individual marketing option optimizations into a unified portfolio-level optimization system. The system integrates data from multiple marketing channels (search engines, web sites, etc.) and uses a single optimization engine to allocate budget across all options simultaneously, achieving overall marketing efficiency improvement while managing complexity through systematic integration rather than separate individual optimizations
Solution Approach 2:
The optimization system serves multiple marketing options and channels through a universal platform. The same system handles budget allocation across search engine paid listings, contextual advertisements, and other marketing vehicles, providing multi-functional capability that improves overall efficiency without requiring separate systems for each channel
2Reliability
If budget is allocated across multiple marketing options without optimization, then marketing coverage is maintained, but cost effectiveness and return on investment deteriorate
Solution Approach 1:
The system dynamically adjusts budget allocation across marketing options based on real-time performance data, click-through rates, and conversion metrics. The optimization is not static but continuously adapts to changing conditions, ensuring performance criteria are met while minimizing costs by shifting budget from underperforming to high-performing channels
Solution Approach 2:
The system changes key parameters such as bid amounts, budget distribution ratios, and spending timing based on optimization calculations. By dynamically adjusting these parameters across different marketing options, the system achieves reliable performance outcomes while reducing overall marketing costs through data-driven parameter optimization
3Measurement precision
If detailed performance measurement and analysis are conducted for each marketing option, then decision accuracy is improved, but time consumption and operational complexity deteriorate
Solution Approach 1:
The system merges performance measurement and analysis functions into a unified automated platform that simultaneously tracks multiple marketing options. Instead of separately measuring and analyzing each channel, the system consolidates data collection, processing, and analysis across all channels, achieving precise measurement without proportionally increasing time consumption
Solution Approach 2:
The system implements automated feedback loops that continuously collect performance data, analyze results, and adjust budget allocations without manual intervention. This automated feedback mechanism maintains high measurement precision while reducing decision-making time by eliminating manual analysis steps and enabling real-time or near-real-time optimization adjustments
Data Source
AI summary
A method and system for determining a bidding strategy for on-line query answer set or contextual advertisement positions for marketing options is described herein.