Steering UIO Connection Distributions via Predicted Weightings
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Solution Overview
Problem
Marketers face challenges in effectively configuring online advertising campaigns for uniquely identifiable objects (UIOs) due to uncertainty in initial PPC values and inefficient budget allocation, leading to ineffective campaigns.
Innovation Solution
A system and method for steering distributions of connections from UIO campaigns based on predicted distributions, which automatically identifies comparable UIOs, generates initial connection weightings, and dynamically adjusts weights based on actual connection data to optimize ad placement and budget allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If initial PPC values are assigned manually without historical data, then campaign configuration can start immediately, but the campaign effectiveness is reduced due to lack of optimization
Solution Approach 1:
The system performs preliminary actions by automatically identifying comparable UIOs and predicting connection distributions before the campaign officially starts. This allows initial PPC values to be pre-optimized based on historical data from similar objects, so when the campaign begins, it already has data-driven weighting rather than relying on manual guesses, thus resolving the contradiction between quick setup and effective optimization
Solution Approach 2:
The system introduces an intermediary mechanism - the prediction module that uses historical connection data from comparable UIOs to generate initial connection weightings. This intermediary translates historical patterns into optimized initial PPC values, allowing the campaign to start quickly while inheriting optimization insights from past performance of similar objects
2Ease of operation
If budget is allocated uniformly among multiple advertisements, then configuration is simple, but budget allocation efficiency is reduced
Solution Approach 1:
The system applies local quality by allocating budget differently to different advertisements based on their predicted connection distributions. Instead of uniform allocation, each advertisement receives a customized budget share proportional to its predicted performance, allowing simple configuration that automatically adapts to individual ad characteristics and optimizes overall budget efficiency
Solution Approach 2:
The system changes the budget allocation parameter dynamically based on predicted connection distributions. The initial connection weightings derived from historical data serve as parameters that automatically adjust budget distribution across multiple advertisements, transforming a simple uniform allocation into an optimized differentiated allocation without requiring complex manual intervention
3Reliability
If connection weightings are adjusted dynamically based on real-time data, then campaign optimization is improved, but system complexity increases
Solution Approach 1:
The system implements feedback mechanisms where actual connection data is continuously monitored and fed back to adjust connection weightings. The system compares predicted versus actual connections and dynamically recalibrates the weighting of different UIOs, creating a self-optimizing loop that improves campaign performance through real-time learning without requiring complex manual reconfiguration
Solution Approach 2:
The system enables self-service optimization by automatically adjusting connection weightings based on real-time feedback from actual connection data. The prediction module and weighting adjustment mechanisms operate autonomously, allowing the campaign to self-optimize without requiring continuous manual intervention, thus achieving sophisticated optimization with manageable system complexity
Data Source
AI summary
Connections (e.g., click-throughs) for online campaigns are steered. A plurality of UIOs and parameters for configuring a campaign (e.g., advertisement campaign) for the plurality of UIOs is received. UIOs comparable to each of the plurality of UIOs of the campaign are automatically identified and displayed in as an array of options that can be selected for more detailed information. A distribution of connections is predicted from resulting from displays for the plurality of UIOs of the campaign, based on historical information of connections for the identified comparable UIOs.