# Satisfaction

Satisfaction captures how users feel at the end of an experience. It tells you whether the effort felt worth it and if the overall interaction left a good impression.Use this metric after tasks, flows, or moments that matter. It works well post-onboarding, after feature use, or anytime you want to check if something feels smooth, frustrating, or forgettable.Satisfaction helps you catch emotional friction that might not show up in performance data. It’s a quick way to understand if your design is leaving people feeling confident or worn out.Interpreting the ResultsUse this key to understand what your Satisfaction score means and how to interpret that for your product experience:How to Calculate SatisfactionThis formula allows you to calculate a single metric that evaluates the user’s satisfaction with your concepts or designs. Using the data from a customer satisfaction (CSAT) survey, you can gauge how well your design resonates with users.Set up questionsAfter presenting the product experience to participants, ask this Likert scale question along with a qualitative follow-up:Follow-up question: Why did you choose that option?Collect dataThis is what the data from your Likert scale question may look like in the data report of the survey platform:Plug data into formulaPlug the data from the Likert scale question above into the following formula to produce your Satisfaction score:Calculate the Satisfaction scoreFor example, if 68 out of 100 participants selected Satisfied or Very Satisfied, then:This results in a Good Satisfaction score on a scale from Very Poor to Very Good, indicating that a strong majority of users had a positive experience.When to Use Satisfaction MetricsSatisfaction metrics help evaluate the emotional and practical outcomes of specific tasks within a product or workflow. By tracking user satisfaction immediately after task completion, teams can identify areas that are intuitive, as well as those that cause frustration or inefficiency. Here are a few common use cases:Complex WorkflowsEvaluate how satisfied users feel after completing multi-step processes, such as onboarding, form submissions, or configuration tasks.Checkout ExperiencesMeasure satisfaction levels after users complete purchase-related tasks to uncover friction in payment, shipping, or confirmation steps.Feature InteractionsTrack user satisfaction after engaging with new or key features to ensure tasks are intuitive and meet expectations.How Indiana University Measured Satisfaction with Their Degree Search PageAs part of a broader initiative to improve its online enrollment journey, Indiana University wanted to ensure prospective students could easily and confidently explore degree options through their new search experience. To validate the design’s effectiveness, the team tested user satisfaction using the Satisfaction UX metric.The SetupSatisfaction is measured using the CSAT methodology, which asks users to rate their experience on a five-point scale: Very Dissatisfied, Somewhat Dissatisfied, Neutral, Somewhat Satisfied, and Very Satisfied.The team asked participants to rate how satisfied they were after using the degree search interface to explore academic programs.The ResultsThe Likert scale question in our survey produced the following results:We plugged this data into the Satisfaction formula to reveal the score:The design received an overall Satisfaction Score of 71%, which qualifies as Good71% of users reported being satisfied (combining Very and Somewhat Satisfied)Only 4% expressed dissatisfaction, and 25% were neutralUsers noted that the clean layout and filter options made browsing “straightforward and efficient”Some participants wanted “more visual cues to help differentiate program types” or “highlight unique program benefits”The ImpactEncouraged by the strong Satisfaction score, the team identified two areas to improve before launch:They added brief program descriptions to make each result more scannableThey introduced icons to help users visually distinguish between undergraduate, graduate, and certificate programsWith these refinements, Indiana University further strengthened confidence in their digital enrollment experience.SourceCSVHelio SurveyHow to Use AI to Measure SatisfactionUsing the 5-point Likert scale question outlined in the How to Calculate section above, gather responses on a survey from an audience of at least 100 respondents. We find that 100 responses is statistically significant in most markets. Once the responses are collected, download the CSV file of your data report and upload it into an AI platform along with the prompt below.Copy this AI prompt to calculate your own Satisfaction score, and check out the type of output it would produce:Technicals for Measuring SatisfactionThe code snippets below show how UX metrics can be measured using data from a survey platform. Take a peak into the development of these metrics, and even become a contributor in ourpublic repo.Data StructureThe JSON structure below records user satisfaction responses after task completion. It follows a sentiment pattern with percentages assigned to various satisfaction levels.This data structure captures how satisfied users are immediately after completing a task, which is a key indicator of the overall user experience.{\n  \u0022choices\u0022: [\n    {\n      \u0022text\u0022: \u0022Very satisfied\u0022,\n      \u0022percentage\u0022: 41\n    },\n    {\n      \u0022text\u0022: \u0022Somewhat satisfied\u0022,\n      \u0022percentage\u0022: 37\n    },\n    {\n      \u0022text\u0022: \u0022Neutral\u0022,\n      \u0022percentage\u0022: 19\n    },\n    {\n      \u0022text\u0022: \u0022Somewhat dissatisfied\u0022,\n      \u0022percentage\u0022: 1\n    },\n    {\n      \u0022text\u0022: \u0022Very dissatisfied\u0022,\n      \u0022percentage\u0022: 2\n    }\n  ]\n}Parsing DataThe Ruby code below calculates theNet Promoter Score (NPS)to evaluate post-task satisfaction. The NPS is derived by subtracting the detractors and neutral participants from the promoters.This calculation produces anet promoter score, which helps assess user satisfaction after completing a task. A higher score indicates greater satisfaction.class UxMetric\n  def net_satisfaction_score\n    metrics = { promoters: 9..10, neutral: 7..8, detractors: 1..6 }\n    count = metrics.each { |k, v| metrics[k] = score_data.count { |e| v === e } }\n    @score ||= ((count[:promoters] - (count[:detractors] + count[:neutral])).to_f / score_data.size * 100).round(2)\n  end\nendValidationThe following JavaScript code ensures thePost-Task Satisfactionsurvey is valid. It checks that the user responses follow a Likert scale format and contain the expected text labels for satisfaction.This validation ensures the survey adheres to the correct format, providing reliable data for analyzing user satisfaction.function isValidPostTaskSatisfactionQuestion(question) {\n  let postTaskSatisfactionQuestions = [\n    'Very satisfied',\n    'Somewhat satisfied',\n    'Neutral',\n    'Somewhat dissatisfied',\n    'Very dissatisfied'\n  ];\n\n  for (let i = 0; i \u003c question.choices.length; i++) {\n    if (question.choices[i].text !== postTaskSatisfactionQuestions[i]) {\n      return false;\n    }\n  }\n  return true;\n}\nScore TranslationThis JavaScript function translates thePost-Task Satisfactionscore into qualitative categories:High,Average, orLow.This function helps interpret thePost-Task Satisfactionscore, making it easier to assess how users feel about their experience after completing a task.function translatePostTaskSatisfactionScore(score) {\n  if (score \u003e 30) return 'High';\n  else if (score \u003e= 10 \u0026\u0026 score \u003c= 30) return 'Avg';\n  else return 'Low';\n}Templates & Presentation MaterialsCreate effective presentation slides, document design concepts, and implement UX Metrics with templates and resources.We've done the work to provide professional layouts that communicate to your stakeholders. UX Metric cards clearly communicate the totals, allow space for breakdowns, and styled to allow for your own brand.Visit Findings for TemplatesTake This Further with the UX Metrics AI SkillsSatisfaction measures how happy users are with their experience using survey questions turned into a single number score. TheUX Metrics AI Skillsis a package you load into your LLM so you can ask questions and get expert answers anytime.Write survey questions that measure satisfaction clearlyUnderstand what is driving your score up or downTrack satisfaction changes across product updatesUse satisfaction data to make the case for design improvementsDrop it into your LLM and start asking questions right away.