Random Forest Media Mix Modeling for Non-Linear Response Curves
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Solution Overview
Problem
Conventional Bayesian regression approaches for media mix modeling are inefficient due to the need for numerous parameters to be guessed, suffering from collinearity and lack of non-linearity and interaction effects, which requires additional components and significant manual intervention.
Innovation Solution
The use of tree-based approaches, such as random forest models, to compute response curves for media channels, which capture non-linearity and interplay between variables without requiring pre-specification of numerous parameters, resulting in more accurate, faster, and scalable models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional Bayesian regression approaches are used for media mix modeling, then the models can capture linear relationships between media channels and responses, but the models require significant manual intervention to specify numerous parameters and suffer from collinearity issues
Solution Approach 1:
The patent replaces the manual parameter specification process (mechanical system) with an automated machine learning model that automatically learns optimal parameters from data. The ML model substitutes the manual Bayesian regression process, automatically handling parameter selection, collinearity resolution, and model training without requiring extensive manual intervention or domain expertise.
Solution Approach 2:
The machine learning model is self-service in that it automatically performs parameter specification, feature selection, and model optimization without requiring manual intervention. The system self-adjusts to handle collinearity issues and automatically determines the relationships between media channels and responses, eliminating the need for manual parameter guessing and adjustment.
2Adaptability or versatility
If additional components such as Hill transformations, adstocking, and interaction terms are added to Bayesian models to overcome shortcomings, then the models can capture non-linearity and interaction effects, but the number of parameters requiring pre-existing knowledge increases significantly
Solution Approach 1:
The patent segments the complex model into multiple independent decision trees, each handling specific relationships between features and responses. This segmentation allows the model to capture non-linearity and interaction effects through the ensemble of trees without requiring explicit specification of interaction terms or transformations, as each tree automatically learns its own relationships from the data.
Solution Approach 2:
The machine learning model dynamically adapts to the data by automatically adjusting its structure and parameters during training. Unlike static Bayesian models that require pre-specification of transformations, the ML model dynamically learns the appropriate non-linear relationships and interactions present in the specific dataset, making the model both versatile and parameter-efficient.
3Adaptability or versatility
If manual determination and adjustment of parameters is performed to overcome Bayesian model limitations, then the models can be customized to specific contexts, but the process is not data-driven and results in significant time-consuming manual effort
Solution Approach 1:
The patent replaces the manual model customization process with an automated machine learning system that adapts to specific contexts through data-driven learning. The ML model automatically customizes itself to each dataset and business context without requiring manual parameter adjustment, thereby maintaining adaptability while dramatically improving productivity and eliminating time-consuming manual efforts.
4Extent of automation
If tree-based approaches such as random forest models are used to compute response curves, then the models can capture non-linearity and interplay between variables without requiring pre-specification of numerous parameters, but the models require computational resources for training and processing
Solution Approach 1:
The patent changes the computational parameters of the model by using ensemble tree-based methods with controlled complexity. By adjusting parameters such as the number of trees, tree depth, and sampling strategies, the model achieves high automation in parameter specification while controlling computational resource consumption. These parameter changes allow the model to balance automation benefits with practical computational constraints.
Data Source
AI summary
Disclosed are methods and systems for generating a media mix model. A time series data set is received specifying media delivered to recipients via a plurality of media channels at a plurality of times and one or more responses at the plurality of times. A random forest model is trained, the random forest splitting the time series data into subsets based on media channel of the plurality of media channels. Response curves are generated using the trained random forest model, each of the response curves corresponding to a media channel of the plurality of media channels, the response curves forming a media mix model adapted to predict responses based on media delivered and media channel.