Performance Goal Benchmarking

Benchmarking is one of the most direct ways to answer a question every UX team faces at some point: is the design meeting expectations or just looking good by chance? A benchmark might be an industry standard like a System Usability Scale score of 68 or higher, an internal performance target such as a 90 percent task completion rate, or the performance of a previous product version that you are trying to improve upon. The way you compare your data to that benchmark depends on the type of metric you have and the size of your sample. Getting that match right matters because the wrong method can give you either false confidence or unwarranted doubt. If your metric is binary such as pass or fail, yes or no, completed or not completed, and your sample size is small, you should be using an exact binomial test. This calculates the exact probability of seeing your result if the true rate was exactly equal to your benchmark, without relying on large-sample assumptions. For example, if seven out of eight users succeed at a task and your benchmark is 70 percent, the exact binomial test will tell you if that observed 87.5 percent is statistically above your target. When you have binary data with a large sample, you can switch to a z-test for proportions. This uses the normal distribution to compare your observed proportion to the benchmark, and it works well when you expect at least five successes and five failures. In practice, you might have 820 completions out of 1000 attempts and want to know if that 82 percent is higher than an 80 percent target. For continuous measures such as task times, SUS scores, or satisfaction ratings, the right approach is a one-sample t-test. This compares your sample mean to the benchmark mean while taking into account the variation in your data. For example, you might have a SUS score of 75 and want to see if it is significantly higher than the benchmark of 68. Some continuous measures, like task times, come with their own challenge. Time data are often right-skewed: most people finish quickly but a few take much longer, pulling the average up. If you run a t-test on the raw times, these extreme values can distort your conclusion. One fix is to log-transform the times, run the t-test on the transformed data, and then exponentiate the mean to get the geometric mean. This gives a more realistic “typical” time. Another fix is to use the median instead of the mean and compare it to the benchmark using a confidence interval for the median, which is robust to extreme outliers. There are also cases where you start with continuous data but really want to compare proportions. For example, you might collect ratings on a 5-point scale but your reporting goal is to know whether at least 75 percent of users agreed or strongly agreed with a statement. In this case, you set a cut-off score, recode the ratings into agree versus not agree, and then use an exact binomial or z-test for proportions.

More information on [GA4] Benchmarking Overview Benchmarks are key metrics that enable you to compare your business's performance against other businesses in your industry. Google Analytics provides these benchmarks through peer groups—cohorts of similar businesses determined by factors like industry vertical and other relevant details. Key Features Daily Updates: Benchmarks are refreshed every 24 hours to provide the most current data. Eligibility Requirements: To access benchmarking data, your Google Analytics property must have the "Modeling contributions & business insights" setting enabled. Additionally, your property must generate sufficient user data to be included in a peer group. Data Protection Your benchmarking data is encrypted and protected, ensuring privacy and aggregation. There are also thresholds to guarantee that a minimum number of properties are included before benchmarks are available to a peer group. Accessing Benchmarking Metrics To view benchmarking data: Select the desired metric in the overview card on the Home page. Expand the Benchmarking category. Choose from a variety of metrics, such as Acquisition, Engagement, Retention, and Monetization. Using Benchmarking Data When benchmarking data is activated, you'll see: Your property's trendline The median of your peer group The range within your peer group (shaded area) Benchmarking comparisons are available within the 25th to 75th percentile to help you make informed decisions based on your performance relative to your peers. Changing Your Peer Group You can change your peer group to ensure more accurate comparisons. Peer groups are categorized based on industry characteristics, such as Shopping > Apparel or Travel & Transportation. Example Scenarios Acquisition: If your 'New User Rate' is below the 25th percentile, consider boosting user acquisition strategies. Engagement: A high 'Average Engagement Time per Session' could be leveraged by enhancing conversion strategies. Retention: A high 'Bounce Rate' may indicate a need for better user experience and content accessibility. Monetization: Low 'ARPU' suggests exploring strategies like upselling or personalized offers. Conclusion Benchmarking data in GA4 offers actionable insights by comparing your performance with industry peers, helping you identify strengths and areas for improvement to achieve your business goals.

You can’t improve manager performance if you don’t know what “good” is. Benchmarks fix that. Most companies use surveys to measure manager performance. But surveys capture sentiment, not behavior. Benchmarks reveal what actually drives team outcomes. Here’s what leading organizations are tracking: 1. Focus time. Top quartile managers create 90+ minute blocks daily. Below median managers lose 3+ hours to interruptions. Every 30-minute block lost means slower problem solving and execution. 2. Collaboration patterns. Effective managers work with 15–25 strong collaborators weekly. Too many collaborators = shallow alignment. Too few = risk of isolation or bottlenecks. 3. Meetings and 1:1s. High-performing teams meet in smaller, faster cycles. Fewer meetings with 10+ attendees improves ownership. Weekly 1:1s boost engagement and growth metrics by over 20%. 4. Workload and Slack activity. Managers above the 75th percentile in Slack messages show higher burnout. Excess messages correlate with fewer focus hours and less strategic time. Longer workdays don’t lead to higher performance, just higher churn. Behavioral benchmarks make manager effectiveness measurable. And give teams a way to improve, not just evaluate. How does your manager data compare?

Drive design impact by comparing UX metrics. UX metrics turn raw user data into useful signals. Benchmarketing is what turns these signals into actionable design decisions. Once you know what you're measuring and how you're collecting data, a benchmark helps you measure the differences in user behaviors. Benchmarking helps you answer two questions. • What does this data mean? • What should we do next? Using benchmarks, like a goal, past result, or industry standard, you can see if your design works and what to change. We use Helio with iterative design to create these signals before development begins. Example: 80% of users completed the task after a design iteration—up from 60%. Shifting the call to action and rewriting the copy had an impact. That 20% jump shows that the design change worked. Benchmarking made it clear. Measuring your work is good. Comparing the performance makes it great. #productdesign #productdiscovery #userresearch #uxresearch

How important is it to align company-level metrics goals to departmental, team, and individual "metrics-centric" objectives? Does your company highlight how department/team/individual goals that are metrics-centric directly impact the company's metrics??? 👆 I am so passionate about the opportunity and benefits in using metrics to align departments/teams/individuals that it was the motivating force to create RevOps Squared - now Benchmarkit...some ideas 👇 1️⃣ Conduct "lunch and learn" sessions where all employees are invited to learn about the metrics that the CEO and CFO track and present to the Board - Do this with the EXECUTIVE TEAM first - it will be very ENLIGHTENING - Highlight the metrics used (what and why) - Explain how the metrics are calculated (how) - Share the company goals for each metric and the industry benchmarks - Share company trends over the last 2/4/6/8 quarters 2️⃣ Conduct "departmental" sessions where each department learns about how their objectives and measurements (metrics) impact the company-level metrics - Create and share the leading indicators that each department is responsible for and how they impact the company-level metrics 👉 Example: Customer Success     - Customer Retention/Renewal Goals: How they impact GRR and NRR     - Customer Expansion Goals: How they impact NRR, Growth and        Enterprise Value:Revenue multiples     - Customer Product utilization goals: How they impact retention (GRR)     - CSQLs: How they impact Expansion ARR and thus NRR 👉 Example: Sales     - New ARR Goals: How they impact Growth and EV:Rev multiples     - Win Rate: How it impacts CAC, CAC Ratio, and CAC Payback Period     - Average ACV: How it impacts CAC, CAC Ratio and CPP     - Quota Productivity: How it impacts growth efficiency metrics 3️⃣ Conduct a metrics and benchmarking assessment against your internal company metrics and then external industry benchmarks to ensure: - The calculation methods are using industry standards and the calculation model your investors use for "comparison" purposes - Identify the most highly correlated input variables that directly impact the outcome metric(s) you are focused upon - Understand the industry benchmarks not just for your like company cohort today...but also how those benchmarks and thus goals will change at the next stage of evolution - Identify the top 2-3 metrics that provide opportunities for improvement and identify then focus on the top 1-2 input variables (leading indicators) that will have the most impact on the outcome metrics (lagging indicators) - SHARE the findings with the executive team, get their buy-in on the need to improve and their specific areas of focus with "goals" that will improve the company-level metric 4️⃣ Create and share a company "Metrics Framework" If you are interested in discussing the what, why, or how of doing any of the above comment or DM me 😱 Many of the above ideas seem basic & obvious- but < 20% do them #b2bsaas #metrics #benchmarks