Media Mix Modeling with Transfer-Learned Prior Integration
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Solution Overview
Problem
Conventional media mix modeling techniques struggle to accurately integrate multiple priors from diverse sources, leading to biased and inaccurate resource allocation due to subjective manual adjustments and conflicting data sets, which complicates the modeling process.
Innovation Solution
An end-to-end framework that uses machine-learning-based transfer learning to align and integrate multiple priors, generating a combined model through objective calibration, ensuring unbiased and reliable marketing channel contribution assessments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple priors from diverse sources are integrated using conventional media mix modeling techniques, then the model incorporates more data sources, but the accuracy and reliability of resource allocation deteriorates due to subjective manual adjustments and conflicting data sets
Solution Approach 1:
The patent introduces transfer learning as an intermediary mechanism between multiple priors and the base model. Instead of directly combining conflicting priors through manual adjustment, the system uses transfer learning to adapt each prior to the base model's parameter space, allowing indirect integration that resolves conflicts objectively through learned transformations rather than subjective weighting.
Solution Approach 2:
The patent replaces the mechanical manual adjustment process with an automated machine learning-based transfer learning system. The objective calibration through transfer learning algorithms substitutes the subjective mechanical process of manually weighting and combining priors, eliminating human bias while maintaining the ability to integrate multiple diverse data sources.
2Ease of operation
If manual adjustments are used to integrate multiple priors, then the modeling process allows flexibility in combining data sources, but the objectivity and reliability of the model deteriorates due to subjective bias
Solution Approach 1:
The patent enables the system to self-adjust and self-calibrate through automated transfer learning. Instead of requiring manual intervention to balance and combine priors, the machine learning model automatically learns the appropriate transformations and weightings, making the system self-sufficient while eliminating subjective human bias from the integration process.
Solution Approach 2:
The patent replaces the mechanical manual adjustment process with an automated machine learning-based transfer learning system. The objective calibration through transfer learning algorithms substitutes the subjective mechanical process of manually weighting and combining priors, eliminating human bias while maintaining the ability to integrate multiple diverse data sources.
3Adaptability or versatility
If conventional media mix modeling integrates multiple priors with different channels, times, or geographical coverages, then the model covers more dimensions, but the modeling complexity increases and accuracy deteriorates
Solution Approach 1:
The patent transforms the integration problem from combining entire disparate data sets to adjusting model parameters through transfer learning. By changing the approach from data-level integration to parameter-level adaptation, the system handles diverse channels, times, and geographical coverages through parameter transformations rather than complex data merging operations.
Solution Approach 2:
The patent segments the integration task into individual prior adaptations rather than attempting to combine all priors simultaneously. Each prior is independently adapted to the base model through separate transfer learning processes, breaking down the complex integration problem into manageable segments that can be processed independently and then combined.
Data Source
AI summary
In integrating multiple priors into media mix modeling, a processing device receives multiple priors that each includes contribution share for one or more marketing channels, a time period, and a geographical region. A machine-learning model generates a transferred model for each prior by performing hyperparameter tuning of a base model based on the corresponding contribution share. The processing device uses the transferred models to generate a combined prior that includes a proportional contribution of the multiple priors. The machine-learning model then generates a combined model by performing hyperparameter tuning of the base model using the combined prior. Marketers can utilize the combined model to assess the contribution of different marketing efforts and perform budget planning.