Marketing Model Validation via Simulated Population Segmentation
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Solution Overview
Problem
Complex systems, such as marketing strategy evaluation models, face challenges in accurately measuring the impact of individual factors due to the influence of additional variables, leading to variability in model accuracy and prediction validity, as there is often no known repeatable standard for calibration.
Innovation Solution
Systems and methods for model validation are developed, involving the generation of time series of segmentation states through iterative application of event functions, with comparisons used to validate models and assess performance metrics, establishing a 'ground truth' for evaluating marketing models using a simulator like the Aggregate Marketing System Simulator (AMSS).
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If marketing models analyze aggregated historical data to evaluate marketing strategies, then model coverage and real-world applicability are improved, but measurement precision and accuracy deteriorate due to inability to control for additional factors
Solution Approach 1:
The patent creates a simulated population that copies the structure and characteristics of real populations, generating synthetic data that mirrors real-world marketing scenarios. This allows models to be tested on multiple datasets (real and simulated) to validate accuracy while maintaining real-world applicability.
Solution Approach 2:
The system performs preliminary validation by comparing model predictions against known ground truth from simulated populations before deploying models to analyze actual historical data. This preliminary check ensures measurement precision is adequate before real-world application.
2Adaptability or versatility
If models are calibrated using real-world historical data, then real-world applicability is improved, but reliability deteriorates because there is no known repeatable standard
Solution Approach 1:
The simulated population acts as an intermediary between theoretical model calibration and real-world validation. It provides a controllable intermediate environment where ground truth is known, enabling reliable calibration that can then be transferred to real-world applications.
Solution Approach 2:
The system performs preliminary calibration and validation using simulated data with known ground truth before applying models to real-world historical data. This ensures reliability is established through a repeatable standard before real-world deployment.
3Measurement precision
If simulation complexity is increased to better represent real-world systems, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The simulated population is segmented into distinct segments with specific characteristics, allowing complex real-world systems to be broken down into manageable components. This segmentation enables accurate representation of real-world diversity while maintaining computational tractability.
Data Source
AI summary
Systems and methods for model validation includes generating a first and a second time series of segmentation states for a data set representative of a simulated population, e.g., a collection of membership counts corresponding to respective segments of the simulated population. The first and second time series of segmentation states are generated by respectively processing the data set through a first and a second simulation each comprising iterative application of a plurality of event functions. The first and the second simulation differ in at least one capacity, e.g., one including a first event function configured with a first parameter, and the second not. Analysis of differences between the first and second time series may be compared to analysis of one of the time series using a subject model. The comparison is then used to validate the model or demonstrate accuracies, inaccuracies, and/or model bias with respect to a performance metric.