Desirability

Desirability measures how much people want what you’re offering. It reflects emotional pull and interest, whether or not users have interacted with the product yet.Use this metric in early-stage research to test ideas, prioritize concepts, or validate marketing direction. It works best when you're trying to learn what excites people before you commit to building it.Desirability helps you understand if your product draws people in. It shows whether you’re working on something users actually care about or just another feature that might get ignored.Interpreting the ResultsUse this key to understand what your Desirability score means and how to interpret that for your product experience:How to Calculate DesirabilityThe Desirability metric captures how much users want to engage with or use a product, based on both emotional reactions and stated intent.Set up questionsTo measure Desirability, use two survey questions:A multiple choice question with no selection limit that asks users to choose the words that best describe the design or product.A Likert scale question asking how likely they would be to use or recommend the feature.
 These questions should be placed after the user has experienced the design or interacted with the feature, and can be delivered via a remote usability test or concept feedback survey.Collect dataIn this example, participants used a feature that recommends outfits based on event details. After completing the task, they selected impression words like Modern, Helpful, or Exciting, and then rated how likely they would be to use the feature again on a 5-point scale.Plug data into formulaTo calculate your Desirability 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:Very Likely = 5Somewhat Likely = 4Neutral = 3Somewhat Unlikely = 2Very Unlikely = 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:Calculate the Desirability ScoreThis results in a Desirability score of 86%, which is considered Good on a scale from Very Poor to Very Good. It shows that users found the feature both emotionally appealing and useful enough to want to use it againWhen to Use Desirability MetricsDesirability metrics help evaluate how visually appealing and attractive a product, feature, or design is to users. These metrics capture first impressions and ongoing interest, allowing teams to ensure their designs resonate with users and stand out from competitors. By tracking desirability, teams can identify what draws users in and where designs may lack visual or emotional appeal. Here are some common use cases for measuring desirability:New Product or Feature LaunchesEvaluate how visually and emotionally attractive new products or features are to users. Desirability metrics identify whether the design sparks excitement and interest that drives adoption.Homepage or Landing Page DesignsMeasure users’ first impressions of a homepage or landing page. Desirability metrics reveal whether the design captures attention, communicates value, and motivates further interaction.Brand and Visual UpdatesAssess the impact of updates to branding or visual design elements, such as colors, typography, or imagery. Desirability metrics help ensure the new look aligns with user expectations and enhances brand appeal.How Getup Gauged Desirability of Their Outfit Suggestion FeatureAfter launching a new event-based outfit recommendation tool, the Getup team wanted to understand not just whether users understood it—but whether they wanted to use it. In other words, does the feature make a good first impression, and do people see themselves actually engaging with it?They turned to the Desirability metric to find out.The SetupDesirability is measured by asking participants two core questions:What is your impression of this feature?How likely are you to use or engage with this experience?These two signals—Impression and Likelihood—are combined to produce a single Desirability score that reflects both emotional response and behavioral intent.The ResultsThe responses from participants produced the following data sets:"What impressions does this feature give you?” (Multi-select question)“How likely would you be to use this feature?” (Likert scale question)The data from the two questions above was plugged into the Desirability formula to reveal Getup's score:Getup’s new feature earned a Desirability Score of 86%, a Good rating on the Glare framework scale89% of participants gave a positive first impression of the tool82% said they’d be likely to use it for event preparationParticipants described the interface as “sleek,” “clear,” and “fun to explore”Some users called out how the outfit filters aligned with real-world scenarios, like formal outdoor weddings or business conferencesThe ImpactEncouraged by the high desirability score, Getup used this data to greenlight additional investment in the feature—adding calendar integrations and expanding outfit categories. Because users showed strong intent to use the feature and saw it as immediately useful, the team prioritized visibility by placing it higher in the homepage module and including it in post-purchase email flows.SourceHelio SurveyCSVHow to Use AI to Measure DesirabilityUsing the multiple choice and Likert scale questions 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 survey data and upload it into an AI platform along with the prompt below.Technicals for Measuring DesirabilityWe’ve built out a UX Metric framework that we’re using in our Helio platform. Here, we’ve laid out what we’ve done, and how other developers can use this. You can also become a Glare Code Contributor on ourUX Metric framework.How to UseDesirability Data ParserWith our framework, you can use theGlare::UxMetric::Desirability::Parsermodule to parse Desirability data.Here are the steps:First, we have to require the module in order to use it.Then, we need to use a validdatastructure to pass into the choices parameter as an argument.When callingGlare::UxMetric::Desirability::Parser.new, we return aParser. This gives us access to a parse andvalid?method.We useparsehere in order to give us a score result, also known asGlare::UxMetric::Result.[\n\t{\n\t\t\u0022choices\u0022: {\n\t\t\t\u0022helpful\u0022: 0.1\n\t\t\t\u0022innovative\u0022: 0.2,\n\t\t\t\u0022simple\u0022: 0.49,\n\t\t\t\u0022joyful\u0022: 0.01,\n\t\t\t\u0022complicated\u0022: 0.4,\n\t\t\t\u0022confusing\u0022: 0.3,\n\t\t\t\u0022overwhelming\u0022: 0.12,\n\t\t\t\u0022annoying\u0022: 0.28\n\t\t}\n\t}\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 TemplatesResourcesThe Resources section provides a collection of articles, case studies, methods, and blog posts to support your work within the UX metrics framework. These materials offer insights into best practices, research methodologies, and practical applications for improving design comprehension and usability. Whether you're refining your design process or conducting user research, these resources will help guide you towards data-informed, user-centered decisions.ArticlesOutcome metrics for product desirability,byGrant BakerThis highlights how desirability elevates a product beyond mere usability. It connects to brand experience and influences users' emotional connection to the product, shaping first impressions and adoption…Rapid Desirability Testing: A Case StudybyMichael HawleyMichael explores desirability testing, which measures users' emotional responses to design aesthetics. This method helps designers gauge visual appeal and first impressions, which can impact usability perceptions…What is Desirability?by,Interaction Design Foundationexplains how outcome metrics like problem-value and success metrics assess desirability by analyzing emotional engagement and product-market fit…Helio MethodsDesirability Testing is Essential for Successful Product Developmentby HelioInteraction Matrixby HelioHelio Case studiesAction Maps for Banking Consumersby HelioValidated Banking Site Landing Page Concepts, by HelioHelio blog postsThe Helio Data-informed Design Process, by Bryan ZmijewskiProduct Development Research Guide to Slow and Fast Researchby Bryan ZmijewskiAgile Design Drives Continuous Improvement and User Satisfactionby Bryan ZmijewskiTake This Further with the UX Metrics AI SkillsDesirability measures whether users actually want to use your product 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 desirability clearlyFind out what makes users want or avoid a productCompare desirability across different designsUse desirability scores to guide product directionDrop it into your LLM and start asking questions right away.