ML Marketing Simulator Optimizes Bid Pricing Accuracy
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Solution Overview
Problem
Current digital marketing techniques rely on subjective business knowledge and heuristics, leading to inefficient prediction of optimized bid prices for advertisements, resulting in overpayment and ineffective marketing campaigns due to incorrect predictions.
Innovation Solution
A marketing simulator system utilizing machine learning models to transform metric and share of voice data into predictive models, selecting the best models based on training, test, and validation results to accurately predict share of voice, click-through rate, and conversion rate, thereby optimizing digital marketing strategies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning models are trained and validated with multiple datasets, then prediction accuracy is improved, but computing resource consumption increases
Solution Approach 1:
The system performs preliminary actions by dividing data into training, test, and validation sets before model development. Multiple machine learning models are trained in advance on training data, then systematically evaluated on test and validation data to predict marketing metrics. This preliminary training and validation framework enables accurate predictions while optimizing resource usage through structured model selection.
Solution Approach 2:
The system changes parameters by evaluating multiple machine learning models with different architectures and configurations. Each model is trained on training data and evaluated on test and validation data to determine optimal performance. This parameter variation approach allows the system to identify the most accurate model for predicting share of voice, click-through rates, and conversion rates while managing computing resources through systematic comparison.
2Ease of operation
If heuristic-based approaches are used for digital marketing predictions, then implementation simplicity is maintained, but prediction accuracy deteriorates
Solution Approach 1:
The system replaces manual heuristic-based approaches with automated machine learning models. Instead of relying on subjective business knowledge and manual bid price optimization, the system uses trained machine learning models that automatically analyze training, test, and validation data to predict share of voice, click-through rates, and conversion rates. This substitution maintains ease of operation through automation while dramatically improving prediction accuracy.
3Reliability
If multiple machine learning models are trained and evaluated, then model selection accuracy is improved, but processing time increases
Solution Approach 1:
The system segments the model evaluation process into distinct phases: training data preparation, test data evaluation, and validation data assessment. By dividing the dataset into training, test, and validation portions, the system can efficiently evaluate multiple machine learning models through structured comparisons. This segmentation enables reliable model selection by systematically comparing performance across different data subsets while managing processing time through organized evaluation workflows.
Data Source
AI summary
A device may receive and transform metric data and share of voice data, associated with digital marketing by an entity, into transformed data, may generate model data from the transformed data, and may divide the model data into training data, test data, and validation data. The device may train models, with the training data, to generate training results, and may process the test data, with the models, to generate test results. The device may process the validation data, with the models, to generate validation results, and may select a first model, a second model, and a third model based on the results. The device may utilize the first model to predict a share of voice, and may utilize the second model to predict a click through rate. The device may utilize the third model to predict a conversion rate, and may perform actions based on the predicted data.