Category Appraisals

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The purpose of category appraisals is to benchmark against competitors and identify sensory space and market gaps of a product category.  Here, some best practices to put in place on your next one:

The Do’s

  1. Discuss your business objectives and previous research findings with the
    research team. In addition to helping you understanding the scientific process
    and data analysis, the research team ultimately seeks to help you make strong
    recommendations that answer business questions.
  2. Diversify the sensory space by including products that represent a wide
    variety of sensory characteristics. This will help you to achieve a holistic
    understanding of the category and find product gaps and new product
    opportunities in the market place.
  3. Remember that Drivers of Liking are calculated within the space defined by
    the products included in the test. Therefore, it is a good practice to include
    at least one competitive product that you think may perform better than your
    product if you are looking for opportunities to improve your product.
  4. Use the same serving protocol for Consumers and Trained Panelists.
    Panelists may additionally test products in non-consumer ways (e.g., eating
    syrup without a waffle).

The Do Not’s

  1. Push the Trained Panel to profile on sensory characteristics that your team
    believes are important. The Panel Leader can address your concerns during
    training, but it is best to not bias the Panel.
  2. Schedule consumer testing and sensory profiling more than a couple of
    days apart. The age of the samples should be the same for Consumers and
    Trained Panelists.
  3. Include too many or too few products. The ideal number of products to
    include in a category appraisal is eight to fifteen.
  4. Include samples that are extremely divergent or that profile on the opposite
    spectrum from samples selected for category appraisal. The data extreme
    characteristics of the sample profile do not allow for good differentiation of
    other samples and can skew the data to one side.

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