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      Penalty Analysis

      Identify Attribute Impacts on Overall Liking

      Overview

      Penalty Analysis uses responses to "Just About Right" (JAR) questions about product attributes to help you understand how individual product attributes impact respondents' reaction to the Overall Liking question.

      Accessing Penalty Analysis

      Penalty Analysis is available for Projects that include an "Overall Liking" question and "Just About Right" (JAR) questions that ask respondents to rate attributes (ex. flavor, texture) on a scale like "Too Little," "Just About Right," or "Too Much". Once the project's surveys have closed, Penalty Analysis becomes available for relevant projects.

      To access Penalty Analysis, navigate to the Insights section of a project, then click into the Analysis menu and select Penalty Analysis.

      Understanding the Penalty Analysis Tables

      Each tested product gets its own table, with rows for each JAR question and the attribute being measured (e.g., "JAR - Flavor"). Labels match your survey’s wording for clarity. Each question breaks down into three rows:

      • Low end: E.g., "Too Little" (bottom 2 box for a 5-point scale).
      • Just About Right: Middle box.
      • High end: E.g., "Too Much" (top 2 box for a 5-point scale).

      The table includes four columns:

      • Percent: Proportion of respondents in each group (e.g., a percentage for "Too Little").
      • Mean: Average Overall Liking score for that group. Always shown for the "Just About Right" group; shown for top/bottom 2 box groups if they’re 15% or more of responses.
      • Penalty: Difference between the "Just About Right" mean and the top/bottom 2 box mean.
      • Weighted Penalty: Penalty multiplied by the percent, showing the scaled impact.

      Interpreting Risk Levels

      Penalty Analysis flags attributes needing attention with two risk thresholds:

      • Moderate Risk (weighted penalty >0.3): Highlighted in yellow.
      • Critical Risk (weighted penalty >0.5): Highlighted in red.

      A high weighted penalty in a top or bottom 2 box row (e.g., "Too Much") suggests that attribute strongly affects overall liking negatively. Focus on these areas to guide product adjustments.

      Tip: Prioritize Critical Risk rows to address the most significant impacts on liking.

      Example Calculation

      Here’s a simple example of how Penalty Analysis calculates the weighted penalty for a JAR question about "Flavor":

      • Data:
        • "Just About Right" (middle box): 70% of respondents, Mean Overall Liking = 7.0.
        • "Too Much" (top 2 box): 15% of respondents, Mean Overall Liking = 4.5.
      • Calculation:
        1. Penalty: Difference between "Just About Right" mean and "Too Much" mean.
          • 7.0 - 4.5 = 2.5
        2. Weighted Penalty: Penalty multiplied by the "Too Much" percentage (as a decimal).
          • 2.5 × 0.15 = 0.375
        3. Risk Check: Compare to thresholds.
          • 0.375 is between 0.3 (Moderate Risk) and 0.5 (Critical Risk), so it’s Moderate Risk (yellow).

      This shows how a 2.5-point liking drop for 15% of respondents results in a moderate issue to address.

      Applying Insights

      Use Penalty Analysis to identify which attributes drag down overall liking the most. High weighted penalties, especially in red, indicate areas where tweaking the product (e.g., adjusting flavor or texture) could boost customer satisfaction.