Effort

Effort measures how easy or difficult users find it to complete a task, using the Customer Effort Score (CES) formula. It reflects the amount of mental or physical strain users experience during an interaction.Use this metric when testing flows like setup, checkout, or navigation—especially when ease-of-use is critical to adoption or conversion. It’s valuable both in usability testing and in post-interaction surveys for live products.A high-effort score signals friction that could lead to frustration, drop-off, or support requests.This metric helps you identify and reduce unnecessary barriers so users can achieve their goals more smoothly.Interpreting the ResultsUse this key to understand what your Effort score means and how to interpret that for your product experience:How to Calculate EffortThe Effort metric measures how easy or difficult it was for users to complete a task, capturing perceived friction in the experience.Set up questionsTo measure Effort, use a prototype directive to guide participants through a task, followed by a Likert scale question asking how easy or difficult the task was to complete. The Likert scale should range from 1 (Very Difficult) to 7 (Very Easy).These questions are typically included in usability tests and distributed to your target audience using a remote research platform.Collect dataIn this example, participants were asked to complete a task on an e-commerce site involving navigation to a specific product category. After completing the task, they rated the level of effort required using a 7-point scale.A majority gave high ratings, indicating the task felt easy to complete.Plug data into the formulaThe Effort score is calculated using the following formula:Each rating from 1 to 7 is counted and multiplied by its corresponding value. The total is divided by the number of responses to get an average, then divided by 7 to convert the score into a percentage representing ease of use.Calculate the Effort scoreBased on the data:After plugging in the full distribution, the formula returns an Effort score of 87%, which is considered Good on a scale from Very Poor to Very Good. This indicates that most users found the navigation task to be smooth and low-effort.When to Use Effort MetricsEffort metrics measure how easy or difficult users find it to complete key tasks in your product, using a standardized Customer Effort Score (CES). These insights help you identify where your experience creates unnecessary friction—whether through confusing flows, inefficient interactions, or complex decision-making. Reducing user effort not only improves satisfaction but also drives retention, completion, and conversion. Use effort metrics to streamline experiences and make tasks feel as seamless as possible.Navigation and Flow DesignEffort metrics can show how intuitive your navigation is—whether users can move through flows like checkouts, form submissions, or dashboards with minimal confusion. High-effort scores in these areas often point to unclear structure or poor interaction design. Measuring effort helps you identify where to simplify interactions and reduce cognitive load, leading to smoother, more efficient experiences.Support and TroubleshootingIf users frequently rely on help documentation or contact support, effort metrics can reveal how difficult key tasks feel to complete independently. Use them to identify common friction points and evaluate whether instructions, tooltips, or UI patterns are actually making things easier. This is especially valuable for self-service flows and complex features that are meant to be user-friendly without external guidance.Post-Launch TestingAfter launching new features or updates, measuring effort gives you a clear picture of how users are experiencing the change. Even if a feature is functional, high effort may signal poor usability or lack of clarity. Tracking this metric post-launch helps you prioritize follow-up improvements that ease the burden on users and improve long-term adoption.How We Measured Effort to Navigate Getup’s E-Commerce SiteTo evaluate how intuitive and smooth Getup’s website experience was for shoppers, we measured the effort required to find a specific type of shirt. Since navigation is a key driver of user satisfaction and retention in e-commerce, we used the Effort metric to understand how easily users could complete this basic browsing task.The SetupEffort is calculated using the Customer Effort Score (CES) formula, which asks users:“How easy was it to complete this task?”After engaging with a product or digital experience, participants are asked to answer this question and rate their opinion on a 7-point scale, where 7 represents Very Easy and 1 represents Very Difficult. These ratings are then converted into a single Effort Score using the CES formula.The ResultsOur Effort question in the survey produced the following results:We plugged this data into our formula to reveal the Effort score:The task of finding a shirt on Getup’s site produced an Effort score of 87%, rated Good on the Glare scale55% of participants rated the task a 7 (Very Easy), while another 25% rated it a 6, reflecting a largely smooth experienceVery few users reported difficulty, with just 6% scoring it a 3 or below, indicating minimal frictionMany users mentioned that category headers, product visuals, and filters made it easy to explore the clothing catalogThe ImpactThe strong Effort score suggested that Getup’s navigation design was largely effective, enabling users to quickly find products with minimal confusion or delay. With only a few users encountering navigation pain points, the results support that the current design can scale, but offer a prompt to further refine filtering or menu options for edge cases.SourceHelio SurveyCSVHow to Use AI to Measure EffortUsing the 7-point 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 Effort score, and check out the type of output it would produce:Technicals for Measuring EffortWe’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 UseEffort Data ParserWith our framework, you can use theGlare::UxMetrics::Effort::Parsermodule to parse Effort data.Here are the steps:First we have to require the module in order for us to use it.Then, we need to use a valid data structure to pass into thenps_questionparameter as an argument. This is currently a Hash with a:choiceskey, where the value is an Array of survey responses.When callingGlare::UxMetrics::Effort::Parser.new, we return a Parser. This gives us access to aparseandvalid?method. We use parse here in order to give us a score result, also known asGlare::UxMetrics::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]Effort Data ValidationWith Glare, you can also validate whether or not data is valid for an Effort score.The requirements are:Thenps_questionparameter is aHashtype.Thenps_questionHash must contain a:choiceskey.The value of:choicesmust be anArray.Once all of those are confirmed, creating anEffort::Parserand runningvalid?should return true.data = {\n\thelpful: 0.1\n innovative: 0.4,\n simple: 0.2,\n joyful: 0.2,\n\tcomplicated: 0.2,\n confusing: 0.1,\n overwhelming: 0.3,\n annoying: 0,\n}\n\nparser = Glare::UxMetrics::Sentiment::Parser.new(\n\tchoices: data\n)\n\nparser.valid? # ✅Effort ResultWe return three important values from anEffort::Resultinstance:Result- a singular integer or float that resembles the score. It is typically used as translation across Label and Threshold.Label- A descriptive string: "Excellent", "Average", or "Low".Threshold- Whether or not the result passes our test. Determined by our UX Metric’s team. Can be “positive”, “neutral”, or “negative”.data = {\n\thelpful: 0.1\n innovative: 0.4,\n simple: 0.2,\n joyful: 0.2,\n\tcomplicated: 0.2,\n confusing: 0.1,\n overwhelming: 0.3,\n annoying: 0,\n}\n\nparser = Glare::UxMetrics::Sentiment::Parser.new(\n\tchoices: data\n)\n\nresult = parser.parse # Sentiment::Result\n\nresult.result # 1.2\nresult.label # \u0022positive\u0022\nresult.threshold # \u0022positive\u0022Threshold ScoringWe also have a data translation step based on thresholds our team has defined:If the Effort score's label is "Excellent", we return a good sentiment result string, "positive".However, if the score did not meet that threshold, we now go to level 2. If the label is "Average", smack dab in the middle, we return a score that means that there’s room for improvement, "neutral".Lastly, we have a catch-all, that takes the scores that didn’t reach any of the requirements get "negative" slapped onto it, meaning that we gotta improve this!def threshold\n@threshold ||= if result \u003e 1.5\n \u0022positive\u0022\n elsif result \u003e 1.0\n \u0022neutral\u0022\n else\n \u0022negative\u0022\n end\n endTemplates & 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.ArticlesWhat is the Value vs Effort Matrix? Explanation, Guide, and How to Avoid its Pitfalls,byKareem MayanHelps product teams prioritize features efficiently by balancing potential impact with required resources. It allows teams to focus on features that will deliver the most value to users and the business with the least effort, avoiding resource-draining initiatives that provide minimal benefit.CES: New Ways SaaS Companies are Using Customer Effort Scoreby,InMovementThis article highlights how product development teams—particularly in SaaS—are leveraging CES beyond customer support to assess onboarding flows and feature adoption, helping pinpoint UX friction and improve usability.The Role of Customer Effort Score (CES) in Improving SaaS CXbyJohnny PageThis piece makes the case that measuring CES immediately after user tasks delivers clear insights into UX pain points—even when a feature appears to function correctly, subtle annoyances can drive frustration.Helio MethodsInteraction Matrixby HelioThe Goal-Gradient Effect: Harnessing Progress to Increase Effortby 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 SkillsEffort measures how hard users have to work to get something done in your product. TheUX Metrics AI Skillsis a package you load into your LLM so you can ask questions and get expert answers anytime.Find out where your product creates unnecessary frictionSet effort benchmarks and track them over timeCompare effort scores across different user flowsUse effort data to simplify the hardest parts of your productDrop it into your LLM and start asking questions right away.