Assessing Demand for Products and Services
A technique for assessing the viability of several concepts for new/different products, services, or bundles of products and/or services, using discrete choice modeling, or a combination of discrete choice modeling and monadic concept testing. The core of the invention involves one or more of the following: a methodological technique for combining monadic and discrete choice data, a method for gathering monadic and discrete choice data at the same time during a single fielding, a method for gathering specific diagnostic information, a method for using discrete choice modeling to generate specific diagnostic information, a unique web-enabled interface that helps individuals make quick and accurate choices by displaying concepts at low and high resolution at the same time, a unique web-enabled interface that permits gathering choice data on multiple dimensions for each set of concepts shown, methodological innovations permitting hierarchical and/or Bayesian analysis of discrete choice data using data for multiple dimensions within the same model, and methods and apparatus for storing, organizing, and reporting input and output from this system.
1 . A method for predicting market success of an offering, the method comprising:
receiving a first set of market research data regarding the offering, the first set being based on one or more discrete choice data collection surveys;
receiving a second set of market research data regarding the offering, the second set being based on one or more monadic data collection surveys;
calibrating the first set of market research with the second set of market research based on commonalities among participants in the discrete choice data collection surveys and the monadic data collection surveys;
modeling the participants predicted affinity for the offering based on the calibrated data.
2 . A method for synthesizing improved market success predictors of a specific type by fusing data of a different type, the method comprising:
integrating monadic and discrete choice data along one or more dimensions into a unified model of consumer behavior that can generate superior monadic concept scores at the aggregate or subgroup levels
predicting individual-level monadic scores for individual consumers who have not seen specific concepts, contingent on their responses to one or more concepts and/or one or more choice tasks, along one or more response dimensions, and in combination with the behavior of other individuals