Dynamic Bid Adjustment via Time Partitioning
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Solution Overview
Problem
Conventional systems for determining bid prices for advertisements in electronic environments are static and fail to dynamically adjust for variations in performance across different times, days, and geographical locations, leading to inefficient advertising budget allocation.
Innovation Solution
An automated system that dynamically determines bid price adjustments by forecasting advertising efficiency for each period, creating partitions based on efficiency thresholds, and adjusting bids accordingly to optimize spending across days, times, and locations, referred to as 'day parting'.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional static bid pricing systems are used, then system simplicity is maintained, but advertising budget allocation efficiency deteriorates
Solution Approach 1:
The patent implements dynamic bid pricing by automatically adjusting bid amounts based on real-time performance data, time of day, day of week, and geographic location. The system transitions from static predetermined bids to dynamic bids that adapt continuously to changing conditions, resolving the contradiction by accepting increased system complexity to achieve superior budget allocation efficiency
Solution Approach 2:
The system incorporates feedback mechanisms where bid adjustments are made based on monitored performance metrics such as conversion rates and revenue. The automated system continuously receives feedback on ad performance and adjusts bid prices accordingly, enabling efficient budget reallocation without manual intervention while managing complexity through systematic feedback loops
2Productivity
If manual bid adjustment is used, then system complexity is reduced, but time consumption and labor requirements increase
Solution Approach 1:
The patent implements self-service automation where the system automatically monitors performance data, determines optimal bid adjustments, and executes bid changes without human intervention. The automated system serves itself by making bid decisions based on predefined algorithms and performance metrics, eliminating manual labor and time consumption while maintaining high adjustment speed
Solution Approach 2:
The system performs preliminary actions by pre-configuring bid adjustment rules, performance thresholds, and optimization parameters before deployment. Once configured, the system automatically executes bid adjustments based on real-time conditions without requiring manual input, resolving the contradiction by shifting complexity to the initial setup phase rather than ongoing operations
3Measurement precision
If average-based bid adjustments are used, then data processing simplicity is maintained, but measurement precision deteriorates
Solution Approach 1:
The patent segments bid adjustments by multiple dimensions including time of day, day of week, geographic location, and device type. Instead of applying a single average-based adjustment, the system creates segmented bid strategies for different segments, enabling precise bid control for each segment while managing complexity through structured segmentation frameworks
Solution Approach 2:
The system implements local quality by applying different bid adjustments to different segments based on their specific performance characteristics. Each segment receives customized bid adjustments tailored to its unique patterns and metrics, achieving high measurement precision while managing complexity through localized optimization rather than blanket averaging
Data Source
AI summary
Bid amounts for electronic advertising can be adjusted throughout the day, or for different days, according to a day parting schedule based on factors such as variations in efficiency. For any set of advertisements grouped based on factors such as keyword, landing page, geographic location, user demographic, etc., values can be forecast for each time period throughout the day. A variation threshold is used to group adjacent periods of time into partitions, and the threshold can be varied until the number of partitions meets the requirements of the provider with whom the advertisement is to be placed. An adjustment factor can be determined for each partition using the forecast information, and these factors can be uploaded to the provider to adjust the base bid price for any advertisement in that group at the appropriate times throughout the day.