Monadic Score Calibration Using Discrete Choice Probabilities
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Solution Overview
Problem
Monadic testing in product concept evaluation suffers from high measurement noise and poor discrimination between similar product concepts, leading to inaccurate scores and difficulty in differentiating between related ideas, while discrete choice methods can exaggerate small differences in preference.
Innovation Solution
Combining monadic and discrete choice methodologies to reduce noise in monadic scores by integrating utility values and probabilities, using maximum likelihood estimation and a weighting parameter to align monadic scores with discrete choice preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If monadic testing is used to assess product concepts, then individual product evaluation is achieved, but measurement noise increases and discrimination between similar concepts deteriorates
Solution Approach 1:
The patent combines monadic testing and discrete choice testing into an integrated framework. Monadic testing evaluates products individually while discrete choice testing compares products against each other. By merging these two approaches, the system leverages the strengths of both methods to reduce measurement noise and improve discrimination between similar product concepts simultaneously.
2Reliability
If discrete choice methods are used to compare product preferences, then discrimination between products is improved, but small differences in preference are exaggerated
Solution Approach 1:
The patent applies parameter changes by using different weighting schemes for monadic and discrete choice data. The weighting parameter is adjusted based on the specific research context, product type, and concept similarity. This allows the system to control the degree to which discrete choice data influences the final scores, preventing over-exaggeration of small preference differences while maintaining improved discrimination.
3Ease of operation
If monadic testing is used for product concept evaluation, then individual concept assessment is achieved, but score accuracy deteriorates due to high measurement noise
Solution Approach 1:
The patent introduces discrete choice testing as an intermediary component that mediates between individual monadic assessments and final score accuracy. The discrete choice data serves as a reference framework that helps calibrate and adjust monadic scores, reducing measurement noise while preserving the ease of individual concept assessment.
Data Source
AI summary
Methods and apparatus disclosed herein reduce signal-to-noise ratio (SNR) of monadic scores. An example apparatus to reduce a signal-to-noise ratio (SNR) of monadic scores, the apparatus includes memory, machine readable instructions, and processor circuitry to execute the machine readable instructions to at least identify a discrete choice probability of selection corresponding to a first product, generate a scale question corresponding to the first product, calculate a monadic probability corresponding to the first product based on the scale question for the first product, and reduce the SNR of the monadic probability by joining the discrete choice probability of selection of the first product with the monadic probability of selecting the first product.