# Frequency

Frequency measures how often users return to engage with your product. It reflects habits, stickiness, and whether the experience stays relevant over time.Use this metric to assess retention, track feature adoption, or evaluate long-term usage patterns. It’s especially useful for products that rely on recurring engagement, like subscription services or productivity tools.Frequency helps you understand if your product delivers enough ongoing value to keep people coming back. If users don’t return, it’s a sign the experience may not be meaningful or essential enough in their routine.Interpreting the ResultsUse this key to understand what your Frequency score means and how to interpret that for your product experience:How to Calculate FrequencyThe Frequency metric measures how often users say they would interact with a product or feature, helping you understand habitual or recurring value.Set up questionsUse a Likert scale question that asks participants how often they would use or return to your product:Answer options should range from Very Rarely to Very Frequently, and the question should appear after participants have experienced the feature in context.Collect dataIn this example, participants were asked how often they would use a digital tool to check their credit score:Responses ranged from Once per day to Never, with most participants selecting Once per month.Plug data into formulaTo calculate your Frequency score from the survey responses you collected, first translate the Likert scale answers into numerical ratings.The Likert scale answers are translated into the follow numerical ratings:Very Frequently = 5Frequently = 4Occasionally = 3Rarely = 2Very Rarely = 1Once your Likert scale answers have been transferred into numerical ratings, you can start plugging the values into the formula below:After converting each response to its numeric value, divide the total score by the number of responses, then divide again by 5 to express it as a percentage.Calculate the Frequency scoreIn this case, the calculated Frequency score is 51%, which falls in the Average range on a scale from Very Poor to Very Good.This suggests that while some users would check their credit score regularly, the feature may not drive frequent repeat usage for the majority of participants.When to Use Frequency MetricsFrequency UX metrics measure how often users interact with your product or its features, providing insights into engagement and habitual use. By tracking usage patterns, you can identify high-performing features, pinpoint areas for improvement, and create designs that encourage consistent interaction. Frequency metrics are critical for assessing user engagement, feature relevance, and product stickiness.Account ManagementFrequency metrics help evaluate how often users access account management features, such as updating profiles, managing subscriptions, or reviewing account details. High usage indicates that these features are easily accessible and provide necessary value to users.Core FeaturesTracking frequency metrics for core features helps determine how essential and valuable these features are to your users. High frequency of use often indicates that a feature solves a significant problem or fulfills a critical need.Notifications and AlertsFrequency metrics are useful in evaluating the effectiveness of notifications and alerts in bringing users back to the product. Alerts that provide timely and relevant information encourage regular use without overwhelming or frustrating users.How We Measured Frequency of Credit Score Checks in Banko’s Mobile AppTo evaluate how often users return to specific features within the Banko mobile banking app, we ran a Frequency test on its credit score check tool. This helped us assess how consistently users engage with the feature and what factors drive (or limit) repeat usage.The SetupFrequency is measured by asking users to self-report how often they perform a particular action using a 5-point Likert scale:Once per dayOnce per weekOnce per monthOnce per yearNeverAfter selecting their frequency, users are asked to explain why they take that action at that rate. This helps surface behavioral patterns and underlying motivations.The ResultsOur Likert scale question produced the following results:We plugged the data into the Frequency formula to reveal the score:The Credit Score feature received a Frequency score of 51%, rated Average on the Glare scaleThe most common response was “Once per month” (43%), often tied to monthly financial check-ins or bill payments19% check weekly, largely driven by active financial planning or concern about credit activityA smaller group (13%) checked only once per year, while 7% reported never using the feature, citing lack of need or awarenessOpen-ended responses revealed that while the tool was seen as helpful, many users weren’t clear on the value of checking more frequentlyThe ImpactThis test revealed that Banko’s credit score tool was seen as trustworthy and accessible, but not yet essential to daily or weekly banking habits. The Glare team recommended surfacing the tool more prominently in key financial workflows, and providing light education about how regular checks can help users catch fraud, track goals, and improve their financial health over time.SourceHelio SurveyCSVHow to Use AI to MeasureFrequencyUsing 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 Frequency score, and check out the type of output it would produce:Technicals for Measuring FrequencyData StructureHere’s a breakdown of all of our properties within our Frequency data model:ChoicesBased on the user choices in the Likert question, selection ratios are converted to percentages that total 100%.[\n\t{\n\t\t\u0022choices\u0022: {\n\t\t\t\u0022very_frequently\u0022: 0.1\n\t\t\t\u0022frequently\u0022: 0.2,\n\t\t\t\u0022occasionally\u0022: 0.49,\n\t\t\t\u0022rarely\u0022: 0.01\n\t\t}\n\t}\n]Parsing DataThequestionsparameter should contain the following:What we’re trying to get out of this is a single score:With this calculated out, we simply return a float that becomes the Frequency score to be used in our thresholds.class UxMetric\n\tdef frequency_score(questions)\n\t\tlikert = questions.first\n\t\t\n\t\tscore = likert.choices['very_frequently'] +\n\t\t  likert.choices['frequently'] -\n\t\t  likert.choices['occasionally'] -\n\t\t  likert.choices['rarely']\n\t\t  \n\t\tscore\n\tend\nendValidationWe have a lax structure to follow for Feeling in order to grab the correct data you need for correct calculation.If you notice, we’re ending the function early and returningfalseif these parameters are not met, indicating an invalid comprehension question set.We first do a smell check, is the question of typeLikertand if the question is of likert typeOccurrence(ps: spelling!).The next step is to actually confirm that the choices align to what is required.We extract each key that should align with ourchoicesobject. Then, we run through each, confirming that they have to equate to a number, otherwise if one doesn’t, we cancel the loop early and returnfalse.If the function doesn’t meet any early returns, the last line of code returnstrueto indicate a valid frequency metric.function isValidFrequencyQuestion(question) {\n\tif (question.type !== 'Likert') {\n\t\treturn false;\n\t}\n\t\n\tif (question.likert_type !== 'Occurrence') {\n\t\treturn false;\n\t}\n\t\n\tconst choices = question.choices;\n\t\n\tconst veryFrequently = choices['very_frequently'];\n\tconst frequently = choices['frequently'];\n\tconst occasionally = choices['occasionally'];\n\tconst rarely = choices['rarely'];\n\t\n\tfor (const item of [\n\t\tveryFrequently,\n\t\tfrequently,\n\t\toccasionally,\n\t\trarely\n\t]) {\n\t\tif (typeof item !== 'number') {\n\t\t\treturn false;\n\t\t}\n\t}\n\n  return true;\n}Score TranslationWe also have a data translation step based on thresholds our team has defined, which you can see to the right.If the Frequency score is above 30% we return a very successful comprehension string"High".However, if the score did not meet those thresholds, we now go to level 2. If the score is above 10%, we return a decent, but not great score,"Avg".Anythingelse(ha, get it?) that did not reach any of the requirements get"Low"slapped onto it.function translateFrequencyScore(score) {\n\tif (score \u003e 0.3) return 'High';\n\telse if (score \u003e 0.1) return 'Avg';\n\telse return 'Low';\n}function calculateEngagementScore(promoters, detractors) {\n  const nps_score = (promoters - detractors) * 100;\n  return nps_score;\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 SkillsFrequency measures how often users come back to your product or use a specific feature. TheUX Metrics AI Skillsis a package you load into your LLM so you can ask questions and get expert answers anytime.Find out what is driving users to return or stay awaySet frequency benchmarks for your product typeCompare frequency across features or user segmentsUse frequency data to improve habits and retentionDrop it into your LLM and start asking questions right away.