Analytics & Metrics Articles & Videos

  • Handling Insignificance in UX Data

    After collecting KPI numbers for two versions of a design, the difference between the two metrics is not statistically significant. Now which version should you launch?

  • Why You Cannot Trust Numbers from Qualitative Usability Studies

    Qualitative usability studies have few users and variable protocol; numbers obtained from such studies are likely to poorly reflect the true behavior of your population due to large measurement errors.

  • How Useful Is the System Usability Scale (SUS) in UX Projects?

    SUS is a 35-years old and thus well-established way to measure user satisfaction, but it is not the most recommended way of doing so in user research.

  • Net Promoter Score in User Experience

    Net Promoter Score (NPS) is a simple satisfaction metric that's collected in a single question. While easy to understand, it's insufficiently nuanced to help with detailed UX design decisions.

  • Benchmark Usability Testing

    Benchmark studies measure one or more KPIs (key performance indicators) of a user interface so that you can tell whether a redesign has measurably better (or worse) usability.

  • Calculating ROI for Design Projects

    Demonstrating the value of design improvements and other UX work can be done by calculating the return-on-investment (ROI). Usually you compare before/after measures of relevant metrics, but sometimes you have to convert a user metrics into a business-oriented KPI (key performance indicator).

  • Triangulation: Get Better Research Results by Using Multiple UX Methods

    Diversifying user research methods ensures more reliable, valid results by considering multiple ways of collecting and interpreting data.

  • How to Interpret User Time Spent and Page Views

    Users’ “productivity” tasks differ from “engagement” tasks, in whether more or less is better for metrics like time on tasks, interactions, and page views. Such KPIs are important, but they must be evaluated relative to users' tasks.

  • Don't A/B Test Yourself Off a Cliff

    A/B testing often focuses on incremental improvements to isolated parts of the user experience, leading to the risk of cumulatively poor experience that's worse than the sum of its parts.

  • Rating Scales in UX Research: Likert or Semantic Differential?

    Likert and semantic differential are instruments used to determine attitudes to products, services, and experiences, but depending on your situation, one may work better than the other.

  • The Benefits of Benchmarking Your Product's UX

    Collect UX metrics to show how well your design is performing over time or relative to competitors. If numbers are down, you know what needs improvement. If up, ROI data is a key management tool.

  • Bounces vs Exits in Web Analytics

    It's important to study why users leave websites. Analytics tools give you two metrics for web pages: exit rate and bounce rate. Understanding the difference between these two numbers is essential for better UX design.

  • Vanity Metrics in Analytics

    Analytics for websites or other UX design projects should drive the project forward to better business success. Metrics that make you feel good may not achieve this goal.

  • Vanity Metrics: Add Context to Add Meaning

    Tracked analytics metrics should be actionable: variations in a meaningful, relatively stable metric reflect change in the user experience. In contrast, vanity metrics appear impressive, but their fluctuations are not operational.

  • What Is a Conversion Rate, and What Does It Mean for UX?

    Conversions measure whether users take a desired action on your website, so they are a great metric for tracking design improvements (or lack of same). But non-UX factors can impact conversion rates, so beware.

  • Treemaps: Data Visualization of Complex Hierarchies

    A treemap is a complex, area-based data visualization for hierarchical data that can be hard to interpret precisely. In many cases, simpler visualizations such as bar charts are preferable.

  • A/B Testing 101

    What is A/B testing, and why should you consider this method for measuring the business value of design changes?

  • Why Confidence Intervals Matter for UX

    To make valid design decisions from quantitative user research data, you should be familiar with the concept of a confidence interval.

  • Macro & Microconversions as Metrics in Analytics

    The most desired user actions (macroconversions) may be too rare to generate enough analytics data for fast design iteration, so we can also analyze smaller user actions (microconversions) that are more frequent and are connected to bigger goals.

  • A/B Testing vs. Multivariate Testing for Design Optimization

    Like A/B testing, multivariate testing is a design optimization method that involves experimenting with live traffic to find the best impact on conversions.

  • Handling Insignificance in UX Data

    After collecting KPI numbers for two versions of a design, the difference between the two metrics is not statistically significant. Now which version should you launch?

  • How Useful Is the System Usability Scale (SUS) in UX Projects?

    SUS is a 35-years old and thus well-established way to measure user satisfaction, but it is not the most recommended way of doing so in user research.

  • Net Promoter Score in User Experience

    Net Promoter Score (NPS) is a simple satisfaction metric that's collected in a single question. While easy to understand, it's insufficiently nuanced to help with detailed UX design decisions.

  • Benchmark Usability Testing

    Benchmark studies measure one or more KPIs (key performance indicators) of a user interface so that you can tell whether a redesign has measurably better (or worse) usability.

  • Calculating ROI for Design Projects

    Demonstrating the value of design improvements and other UX work can be done by calculating the return-on-investment (ROI). Usually you compare before/after measures of relevant metrics, but sometimes you have to convert a user metrics into a business-oriented KPI (key performance indicator).

  • How to Interpret User Time Spent and Page Views

    Users’ “productivity” tasks differ from “engagement” tasks, in whether more or less is better for metrics like time on tasks, interactions, and page views. Such KPIs are important, but they must be evaluated relative to users' tasks.

  • Don't A/B Test Yourself Off a Cliff

    A/B testing often focuses on incremental improvements to isolated parts of the user experience, leading to the risk of cumulatively poor experience that's worse than the sum of its parts.

  • The Benefits of Benchmarking Your Product's UX

    Collect UX metrics to show how well your design is performing over time or relative to competitors. If numbers are down, you know what needs improvement. If up, ROI data is a key management tool.

  • Bounces vs Exits in Web Analytics

    It's important to study why users leave websites. Analytics tools give you two metrics for web pages: exit rate and bounce rate. Understanding the difference between these two numbers is essential for better UX design.

  • Vanity Metrics in Analytics

    Analytics for websites or other UX design projects should drive the project forward to better business success. Metrics that make you feel good may not achieve this goal.

  • What Is a Conversion Rate, and What Does It Mean for UX?

    Conversions measure whether users take a desired action on your website, so they are a great metric for tracking design improvements (or lack of same). But non-UX factors can impact conversion rates, so beware.

  • A/B Testing 101

    What is A/B testing, and why should you consider this method for measuring the business value of design changes?

  • Why Confidence Intervals Matter for UX

    To make valid design decisions from quantitative user research data, you should be familiar with the concept of a confidence interval.

  • Macro & Microconversions as Metrics in Analytics

    The most desired user actions (macroconversions) may be too rare to generate enough analytics data for fast design iteration, so we can also analyze smaller user actions (microconversions) that are more frequent and are connected to bigger goals.

  • A/B Testing vs. Multivariate Testing for Design Optimization

    Like A/B testing, multivariate testing is a design optimization method that involves experimenting with live traffic to find the best impact on conversions.

  • Statistical Significance in UX

    If you’re working on digital products, you should be familiar with what statistical significance means in the context of UX research. Otherwise your decisions may be based on meaningless numbers that could be due to pure chance and not a reliable difference between design options.

  • In Analytics, What do the Numbers Really Mean?

    Analytics data are only as valuable as the insights derived from them. Some figures can stand on their own while others need further research to be interpreted. To use analytics data confidently and accurately, teams must understand the difference.

  • Pitfalls of Conversion-Rate-Only Concern

    Numbers don't paint the full UX picture, so in the quest for conversion rate optimization, don’t lose sight of the fact that we’re designing for humans.

  • Check Analytics Data Before You Wreck UX Priorities

    Analytics data can help supplement observations made during usability studies by providing evidence on the severity and generalizability of the issues observed.

  • Turning Analytics Findings Into Usability Studies

    Tips for translating UX issues found in analytics into user research. Analytics tell you what customers are doing, but not why they are doing it. Pairing analytics and user research will provide you with clearer answers.

  • Why You Cannot Trust Numbers from Qualitative Usability Studies

    Qualitative usability studies have few users and variable protocol; numbers obtained from such studies are likely to poorly reflect the true behavior of your population due to large measurement errors.

  • Triangulation: Get Better Research Results by Using Multiple UX Methods

    Diversifying user research methods ensures more reliable, valid results by considering multiple ways of collecting and interpreting data.

  • Rating Scales in UX Research: Likert or Semantic Differential?

    Likert and semantic differential are instruments used to determine attitudes to products, services, and experiences, but depending on your situation, one may work better than the other.

  • Vanity Metrics: Add Context to Add Meaning

    Tracked analytics metrics should be actionable: variations in a meaningful, relatively stable metric reflect change in the user experience. In contrast, vanity metrics appear impressive, but their fluctuations are not operational.

  • Treemaps: Data Visualization of Complex Hierarchies

    A treemap is a complex, area-based data visualization for hierarchical data that can be hard to interpret precisely. In many cases, simpler visualizations such as bar charts are preferable.

  • Annoying Online Ads Do Cost Business

    Increased advertising caused a 2.8% drop in use of an Internet service. The full magnitude of the lost business was only clear after a full year.

  • Multivariate vs. A/B Testing: Incremental vs. Radical Changes

    Radical redesigns are best tested using an A/B experiment, while multivariate tests indicate how various UI elements interact with each other and support incremental improvements to a design.

  • Beyond the NPS: Measuring Perceived Usability with the SUS, NASA-TLX, and the Single Ease Question After Tasks and Usability Tests

    Post-test questionnaires like the SUS measure perceived usability of an entire system; post-task scales suggest problematic parts of a design.

  • Search-Log Analysis: The Most Overlooked Opportunity in Web UX Research

    Your website’s search engine can tell you what your web visitors want, how they look for it, and how well your content strategy meets their needs.

  • Translating UX Goals into Analytics Measurement Plans

    Focus on UX goals to drive analytics measurement plans, rather than tracking superficial metrics. Identify the core goal of a design to meaningfully measure it.

  • Optimize for Return Visits, not Bounce Rate

    Use bounce rate as a red flag for possible issues lurking on your site, but don’t make design decisions aimed solely at chasing that second click. Optimize for long-term engagement through return visits and track deeper conversion goals.

  • Frequency & Recency of Site Visits: 2 Metrics for User Engagement

    How often people visit your site and how long they wait between two visits can help to gauge visitor loyalty and to uncover the behavioral trends distinguishing frequent users from occasional ones.

  • Net Promoter Score: What a Customer-Relations Metric Can Tell You About Your User Experience

    NPS is a loyalty metric that correlates well with perception of usability, is easy to understand and administer, but has limitations for understanding and evaluating UX when used in isolation.

  • 5 Information Architecture Warning Signs in Your Analytics Reports

    Analytics metrics such as pageviews, conversions, entrances, bounce rates, and search query frequency can help identify problems in your category structure.

  • Games User Research: What’s Different?

    Game testing researches the notion of fun. Compared with mainstream UX studies, it involves many more users and relies more on biometrics and custom software. The most striking findings from the Games User Research Summit were the drastic age and gender differences in motivation research.

  • No More Pogo Sticking: Protect Users from Wasted Clicks

    Misleading links and omitted information force users to bounce back and forth in a hub-and-spoke pattern between a routing page and subpages linked from it, increasing the interaction cost and decreasing engagement over time. Use web analytics tools to identify and monitor pogo-stick behavior on your site.

  • Segment Analytics Data Using Personas

    Persona-inspired segments can be used in website analytics to uncover trends in data and derive UX insights. Better than (a) lumping everybody together or (b) segmenting on demographics that don't relate to user behavior.

  • Define Stronger A/B Test Variations Through UX Research

    Complement A/B split tests with user research to identify true causes and develop well informed design variations.

  • Define Micro Conversions to Measure Incremental UX Improvements

    Not every design and content change generates immediate or significant increases in conversion rates, but they may affect conversion rates in the long run.

  • Five Essential Analytics Reports for UX Strategists

    Google Analytics is filled with very useful information for UX Strategists defining a baseline and tracking trends in order to define goals, strategies, and concepts for a brighter tomorrow.