Using multiple data analysis methods to guide makeup remover wipe optimization in a design of experiments consumer home use test

Abstract

AbstractTo stay ahead of the competition companies often look at the product category for optimization. A consumer design of experiments study was conducted with various analyses to understand the best product feature combinations for maximizing consumer acceptance and purchase intent (PI) for makeup remover wipes. A blind, sequential monadic, 6‐weeks home use test with a balanced incomplete block design was conducted with 963 consumers in the United States and United Kingdom. Eighteen prototypes were selected with different combinations of four factors (formula, thickness, weight, and lotion add‐on). Each participant evaluated six of the 18 prototypes. Weekly online questionnaires were used to capture questions covering overall liking (OL), PI, skin feel, texture of the fabric, and so on. A simulation model was generated to identify the best factor combination with highest PI and OL for product optimization. Driver's analysis; sensitivity analysis, a new method not often used in the sensory and consumer research; and penalty analysis provided more in‐depth understanding of product performance. The learning from this study guided the product development team to determine the factor combination for the product optimization and provide direction on future planning for the category.Practical applicationsThe use of multiple analyses with studies such as a design of experiment or other category appraisal type studies can provide additional information to researchers and help to identify both those aspects that drive acceptance or purchase intent as well as those factors that could reduce consumer interest. Although the information sometimes may appear “at odds with each other,” careful study often provides insights that may have been lost without the additional analyses.