Dynamic Bid Pacing via Real-Time ML Feedback
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Solution Overview
Problem
Machine learning algorithms used in search engine marketing face challenges in interpreting volatile data, leading to overpredicted outcomes and recurring overpayments, particularly in automated trading and marketing algorithms.
Innovation Solution
A system and method that utilize a predictive algorithm via a machine learning model to determine and adjust bids for search engine marketing campaigns, incorporating real-time data for continuous training and pacing of bids through periodic intervals, ensuring accurate and dynamic pricing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If machine learning algorithms are used for automatic purchases in search engine marketing, then prediction speed and automation are improved, but measurement precision deteriorates due to overprediction of volatile data
Solution Approach 1:
The system implements feedback by continuously monitoring actual performance data and using it to adjust future predictions. The machine learning model receives feedback from real-time campaign performance, allowing it to correct overpredictions and improve accuracy while maintaining fast automated bidding
Solution Approach 2:
The system transitions from static historical data analysis to dynamic real-time prediction adjustment. The machine learning model dynamically adapts to changing market conditions and volatile data by continuously updating predictions based on current performance metrics, resolving the contradiction between speed and precision
2Stability of the object's composition
If machine learning models are trained on historical data only, then model stability is improved, but adaptability deteriorates due to inability to interpret volatile real-time data
Solution Approach 1:
The system maintains continuous learning by constantly feeding real-time performance data back into the machine learning model. This continuous training process ensures the model remains stable while simultaneously adapting to volatile real-time conditions, combining historical patterns with current market dynamics
Solution Approach 2:
The system performs preliminary training on historical data to establish a stable baseline model, then continuously refines this model with real-time data. This preliminary action creates a stable foundation that can subsequently adapt to volatile conditions without losing its core predictive capabilities
Data Source
AI summary
A system can include one or more processors and one or more non-transitory computer-readable media storing computing instructions that, when executed on the one or more processors, cause the one or more processors to perform operations including: (1) determining, by a predictive algorithm via a machine learning model, one or more predicted bids for one or more keywords in one or more campaigns; (2) adjusting the one or more predicted bids for the one or more keywords in the one or more campaigns; (3) pacing the one or more predicted bids for the one or more keywords by multiplying the one or more predicted bids by a pacing factor; iteratively adding real-time data to a training data set for the predictive algorithm; and iterating (1)-(3) at one or more periodic intervals as the real-time data is added to the training data set. Other embodiments are disclosed herein.