A framework for optimising the cost and performance of concept testing

Abstract

To anticipate the likely market demand better and identify the best customers to target with potential new products early in their development, concept tests need to provide adequate data quality for the objectives of measurement. Generalisability theory provides a framework where a generalisability study can be conducted to identify each possible aspect of a measurement as a factor that may be a potential source of variability. When a limited budget is available, optimising measurement designs involves a trade-off between the accuracy of the data (i.e. generalisability coefficients) and cost considerations. Building on previous and ongoing research, this study presents a multivariate optimisation procedure to achieve the most cost-efficient measurement design under the pre-specified generalisability coefficient constraint. We used the available online concept testing data to illustrate how to optimise the measurement cost by sampling along the facets that contribute to the total error variance in different iso-generalisability designs. The findings may help to facilitate decision making for the sampling of respondents, concepts, items, and occasions in the design of concept test. However, the optimisation framework may be applied more generally to improve the effectiveness and efficiency of other customer tests in marketing.