# Comprehension

Comprehension measures how well users understand your product or service. It captures whether they grasp its purpose, how it works, and the value it offers. This includes both their confidence and their ability to explain what they’ve seen.Use this metric during onboarding, walkthroughs, or any time you're introducing something new or complex. It helps uncover confusion in language, layout, or messaging so you can fix it before users get lost.Clarity builds momentum. When users understand what they’re looking at, they’re more likely to engage and succeed. Comprehension helps you make that first impression count.Interpreting the ResultsUse this key to understand what your Effort score means and how to interpret that for your product experience:How to Calculate ComprehensionThe Comprehension metric measures how clearly users understand the purpose and functionality of a product, feature, or screen—capturing both surface-level clarity and deeper understanding.Set up questionsAfter presenting the product experience to participants, ask this Likert scale question along with an open 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 the formulaTo calculate your Comprehension score from the survey responses you collected, we must first translate the Likert scale likelihood answers into numerical ratings.The Likert scale answers are translated into the follow numerical ratings:Understood very well = 4Understood most of it = 3Understood a little = 2Did not understand = 1Once your Likert scale answers have been transferred into numerical ratings, you can start plugging the data from your two survey questions into the formula below:After assigning values to each response, calculate the average score and divide by 4 to express it as a percentage.Calculate the Comprehension scoreUsing the provided data:The reported score rounds up to 91%, which is considered Very Good on a scale from Very Poor to Very Good. This indicates that nearly all participants understood how the feature works, both functionally and conceptually.When to Use Comprehension MetricsComprehension metrics reveal how well users understand your product, allowing you to track their ability to grasp new features, follow instructions, and navigate key experiences. By identifying areas where users struggle, you can take action to improve clarity and usability. Optimize your user experience and predict key outcomes like abandonment rate, bounce rate, and overall engagement.Copy and MessagingUse comprehension metrics to assess whether your product’s copy such as instructions, error messages, or feature descriptions, is clear and easily understood by your users.New FeaturesWhen launching new features, comprehension metrics help you evaluate how easily users understand the functionality. This ensures that the feature is intuitive and aligns with user expectations.OnboardingFor new users, comprehension metrics can reveal whether the onboarding flow or initial product walkthrough effectively communicates how to use the core features. Clear understanding at this stage is key to user retention, as confusion can lead to abandonment.How We Measured Comprehension of Getup’s New Event-Based Outfit Suggestions FeatureTo ensure users could quickly grasp the value of a new smart recommendation tool, we tested comprehension of Getup’s “event-based outfit suggestions” feature. This tool lets users input event details—like the setting, formality, and temperature preference—and receive outfit recommendations tailored to their needs. The team needed to know whether users understood what the feature does and how to use it at a glance.The SetupComprehension is measured through a two-part method:Self-rating – Users rate how well they believe they understood the concept or featureWritten explanation – Users then describe the feature in their own wordsTogether, these responses generate a Comprehension Score, which helps determine if messaging, layout, and UI interactions are clear.The ResultsThe Likert scale question in our survey produced the following results:We plugged this data into our formula to produce the Comprehension score:The outfit suggestion feature received a Comprehension score of 91%, rated Very Good on the Glare scale62% of participants said they “understood very well” what the feature was for and how it workedAnother 33% reported understanding most of it, suggesting overall clarityOnly 4% said they “understood a little,” and 0% indicated they did not understandOpen responses reflected accurate summaries of the tool’s purpose—users described it as “a smart wardrobe assistant based on event type” and “a way to help me pick clothes based on the occasion”The ImpactThe strong comprehension score confirmed that Getup’s concept, copy, and UI for the new feature successfully conveyed its purpose without requiring extra context. By combining a structured layout with intuitive language and minimal friction, the feature helped users quickly understand its value. These results validated the design team's approach and suggested they could confidently move forward without major onboarding or tutorial changes.Source:CSVHelio SurveyHow to Use AI to Measure ComprehensionUsing the 4-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 Comprehension score, and check out the type of output it would produce:Technicals for Measuring ComprehensionThe 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.Parsing DataParsing data from a comprehension question requires multiple scoring.The questions param should define the following.SentimentSentiment defines the percentages of positive, neutral, and negative values that total 100%.KeywordsTotaling the ratio of mismatched keywords from qualitative responses informs the single metric.ChoicesBased on the user choices in a likert question, selection ratios are converted to percentages that total 100%.ValidationValidation can actually get pretty tricky. It depends on how much flexibility you give the user to create and adjust their question and question choices.Currently, we’re only checking the choice texts in a specific order. If a user decides to move a choice around or update the choice’s text, it invalidates.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 SkillsComprehension measures whether users understand what your product is asking them to do. TheUX Metrics AI Skillsis a package you load into your LLM so you can ask questions and get expert answers anytime.Find out where users get confused in a flowTest whether labels, instructions, and content are clearCompare comprehension across different design versionsUse comprehension data to improve copy and layoutDrop it into your LLM and start asking questions right away.