Marketing Mix Model for Codependent Mode Analysis
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Solution Overview
Problem
Traditional methods for measuring the rate of return on marketing activities are complex, computationally expensive, and lead to inaccurate estimations due to the isolation of marketing modes, failing to account for codependent effects between different marketing strategies.
Innovation Solution
A system that includes rate of return circuitry with data retrieval, transformation, coefficient determination, contribution generation, and mode extraction components to streamline the modeling process by evaluating marketing modes in a codependent manner, using decay and saturation factors to transform data and restrict coefficients within a truncated normal distribution model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional isolated modeling methods are used for each marketing mode, then the modeling process is simple to implement, but the measurement precision of rate of return is poor due to failing to account for codependent effects
Solution Approach 1:
The patent combines multiple isolated marketing mode models into a single integrated marketing mix model that evaluates TV, digital, print, and other marketing modes simultaneously. This integration allows the model to capture codependent effects between different marketing channels while maintaining a unified modeling framework that manages complexity through systematic structure.
Solution Approach 2:
The marketing mix model serves multiple functions simultaneously: it estimates rate of return for each marketing mode, captures interactions between modes, accounts for decay and saturation effects, and provides a comprehensive evaluation framework. This multi-functionality resolves the contradiction by making a single complex model perform all necessary analytical tasks.
2Measurement precision
If traditional isolated modeling methods are used for each marketing mode, then the computational cost is low, but the measurement precision of rate of return is poor due to inaccurate estimations
Solution Approach 1:
By merging all marketing mode estimations into a single simultaneous estimation process, the patent eliminates the need for multiple separate computational passes. The integrated marketing mix model estimates all mode parameters together in one optimization routine, reducing total computational effort while improving accuracy through capturing inter-mode dependencies.
3Reliability
If marketing modes are evaluated independently, then the ease of operation is high, but the reliability of rate of return measurement is poor due to isolation of marketing modes
Solution Approach 1:
The patent merges independent marketing mode evaluations into a unified marketing mix model that simultaneously estimates all modes. This consolidation improves reliability by capturing codependent effects while maintaining ease of operation through systematic model structure and automated estimation procedures.
Solution Approach 2:
The model incorporates feedback mechanisms where the estimation of one marketing mode parameter influences the estimation of other modes. This interdependent feedback structure ensures that codependent effects are captured, improving measurement reliability while the automated iterative estimation process maintains operational simplicity.
4Measurement precision
If traditional modeling processes are used without decay and saturation factors, then the ease of operation is high, but the measurement precision is poor due to lack of data transformation
Solution Approach 1:
The patent applies preliminary data transformation by incorporating decay factors and saturation factors into the model specification before estimation. These transformations are built into the model structure in advance, allowing the estimation process to directly produce accurate results without requiring separate post-processing transformation steps.
Solution Approach 2:
The model dynamically adjusts parameters including decay rates and saturation levels based on the data being analyzed. These parameter changes allow the model to adapt to different marketing contexts and campaigns, improving measurement precision while the parameters are estimated automatically through the optimization process.
Data Source
AI summary
Methods, apparatus, systems, and articles of manufacture are disclosed to improve modeling efficiency identify a first quantity of modes corresponding to a task, apply a model to the first quantity of modes to determine a first contributory effect corresponding to the task, select a first portion of the first quantity of modes to exclude to generate a second quantity of modes corresponding to the task, apply the model to the second quantity of modes to determine a second contributory effect corresponding to the task, and cause a trigger response based on a difference value between the first contributory effect and the second contributory effect.