Performance Metric Analysis Techniques

A "sampled success metric" is a performance measure or evaluation criterion calculated from a sample or subset of data rather than the entire population. Its calculation often involves higher costs per sample, such as manual review, leading to a trade-off between sample size and metric accuracy/sensitivity. In this tech blog, written by the data science team from Shopify, the discussion revolves around how the team leverages Monte Carlo simulation to understand metric variability under various scenarios to help the team make the right trade-offs. Initially, the team defines simulation metrics to describe the variability of the sampled success metric. For instance, if the actual success metric is decreasing over time, the metric could indicate how many months of sampled success metric would show a decrease, termed as "1-month decreases observed". Then, the team defines the distribution to run the Monte Carlo simulation. Monte Carlo simulation, a computational technique using random sampling to estimate outcomes of complex systems or processes with uncertain inputs, draws samples from a dedicated distribution that matches business needs. Based on past observations, the team’s application follows a Poisson distribution. Next comes the massive simulation phase, where the team runs multiple simulations for one parameter and then changes various parameters to simulate different scenarios. The goal is to quantify how much the sample mean will differ from the underlying population mean given realistic assumptions. The final result provides a clear statistical distribution of how much extra sample size could lead to metrics variability decrease and increased accuracy. This case study demonstrates that Monte Carlo simulation could be a valuable toolkit to add to your decision-making and data science knowledge. #datascience #analytics #metrics #algorithms #simulation #montecarlo #decisionmaking – – –  Check out the "Snacks Weekly on Data Science" podcast and subscribe, where I explain in more detail the concepts discussed in this and future posts:    -- Spotify: https://lnkd.in/gKgaMvbh   -- Apple Podcast: https://lnkd.in/gj6aPBBY    -- Youtube: https://lnkd.in/gcwPeBmR https://lnkd.in/dKnrZzzV 

A critical part of journey management in any large organisation is measuring how your journeys perform. 📊 By setting clear goals, monitoring performance, identifying gaps, and measuring improvement impact, you create a continuous cycle of management and enhancement. Measurement surfaces opportunities and kickstarts improvements. 🚀 Yet many organisations struggle: data sits in silos, teams measure inconsistently, and dashboards report numbers without a coherent story. Product, marketing, sales, service, and digital teams collect valuable insights, but without a common language, they never combine into a unified performance view. The result? Plenty of activity, little clarity on what actually improves customer experience and business performance. Measuring performance along specific journeys—rather than isolated KPIs—provides the right context: the journey itself. 🗺️ This approach transforms your journey framework into an engine for improving both customer experience and business performance holistically, creating a shared structure and language where different KPIs unite. 🧭 Inspired by the Balanced Scorecard, this pragmatic 3x3 Matrix structures performance measurement across two dimensions: 👉 First, it distinguishes 3 performance metric categories: - Customer performance (behavior and sentiment) - Commercial performance (conversion, customer base, revenue) - Operational performance (cost, efficiency, reliability) 👉 Second, it distinct three journey hierachy levels: - Overall customer lifecycle - End-to-end product or service journey - Individual customer tasks These intersecting dimensions ensure each metric sits logically within a complete, coherent view. The visual below shows example metrics for all nine sections, helping you build a balanced measurement framework for journeys. This matrix delivers three immediate benefits: ✨ 1. It aligns siloed KPIs and contextualizes them into a shared journey 2. It enables drill-down and aggregation through connected KPIs across journey levels 3. It surfaces trade-offs and synergies between performance metrics A few quick tips to take into account when drafting or structuring your own journey-driven measurement framework 👇👇👇 🐌 Consider both leading and lagging indicators for a robust measurement approach that balances early warning signs with outcome metrics.  🤲 Don’t collect everything. Start with a North Star KPI for each journey, and add a small set of supporting metrics. Less is more. 💬 Always mix performance metrics with more qualitative feedback and insights that will help you determine why performance is down and how to fix it. Happy measuring! 🎉

𝗜𝗱𝗲𝗮 #𝟭𝟲: 𝗠𝗲𝘁𝗿𝗶𝗰𝘀 𝘁𝗵𝗮𝘁 𝗺𝗮𝘁𝘁𝗲𝗿: 𝘁𝗵𝗲 𝗯𝗲𝗮𝘂𝘁𝘆 𝗼𝗳 𝘀𝗽𝗶𝗹𝗹 𝗮𝗻𝗱 𝘀𝗽𝗼𝗶𝗹 I worked with a hotel chain that was focused on two high-level KPIs: 𝗮𝘃𝗲𝗿𝗮𝗴𝗲 𝗿𝗼𝗼𝗺 𝗿𝗮𝘁𝗲 (𝗔𝗥𝗥) and 𝗼𝗰𝗰𝘂𝗽𝗮𝗻𝗰𝘆 (%).  Occupancy was around 80% and had increased year on year but this aggregate average was hiding significant opportunities. When we de-averaged the overall occupancy by hotel and night, we discovered that very few hotels were 80% full: most were either completely full or only half full.  We reframed performance using two “failure metrics” (see illustration): • 𝗦𝗽𝗼𝗶𝗹: measured empty rooms (by hotel, by night). • 𝗦𝗽𝗶𝗹𝗹: measured “lost trading days” when a hotel reached full occupancy too early. By analysing 𝘀𝗽𝗶𝗹𝗹 𝗮𝗻𝗱 𝘀𝗽𝗼𝗶𝗹 𝗮𝘁 𝗮 𝘀𝗶𝘁𝗲-𝗻𝗶𝗴𝗵𝘁 𝗹𝗲𝘃𝗲𝗹, we uncovered significant value: • Spoil caused by pricing too high or insufficient marketing.   • Spill caused by pricing too low or overmarketing.   𝗦𝗽𝗼𝗶𝗹 𝗶𝘀 𝗮 𝗳𝗮𝗰𝘁. 𝗦𝗽𝗶𝗹𝗹 𝗶𝘀 𝗮 𝗺𝗼𝗱𝗲𝗹. One measures what you wasted; the other estimates what you missed.   The principle applies to almost any decision made under uncertainty: where there’s finite capacity and variable demand, there’s always a 𝘀𝗽𝗶𝗹𝗹-𝘀𝗽𝗼𝗶𝗹 𝘁𝗿𝗮𝗱𝗲-𝗼𝗳𝗳.  I’ve applied this framework across a diverse range of businesses: • 𝗖𝗮𝗹𝗹 𝗰𝗲𝗻𝘁𝗿𝗲𝘀: spill = calls with no agents (missed sales); spoil = agents with no calls (wasted labour). • 𝗥𝗲𝘀𝘁𝗮𝘂𝗿𝗮𝗻𝘁𝘀: spill = understaffed hours (poor service); spoil = overstaffed hours (low productivity). • 𝗦𝘂𝗽𝗲𝗿𝗺𝗮𝗿𝗸𝗲𝘁𝘀: spill = missed sales (poor availability); spoil = waste (over-stocking). Every business wrestles with these two-sided costs – the 𝗰𝗼𝘀𝘁 𝗼𝗳 𝗲𝘅𝗰𝗲𝘀𝘀 and the 𝗰𝗼𝘀𝘁 𝗼𝗳 𝗺𝗶𝘀𝘀𝗲𝗱 𝗼𝗽𝗽𝗼𝗿𝘁𝘂𝗻𝗶𝘁𝘆.  Once you measure both, you can manage the balance intelligently.  The best metrics don’t just describe performance – they expose 𝘧𝘢𝘪𝘭𝘶𝘳𝘦 𝘮𝘰𝘥𝘦𝘴 that can actually be fixed. Key takeaways: • Analyse at the most atomic level that could be actionable (hour, site-night, SKU-store, agent, keyword etc.) • Define the acceptable 𝗴𝘂𝗮𝗿𝗱𝗿𝗮𝗶𝗹𝘀 for that atomic outcome.  • Systematically analyse the distribution of performance outside guardrails. • Recognise that averages hide opportunities where good and bad performance offset each other There’s a fascinating 140-year history of optimising these decisions which are commonly referred to as Newsvendor problems – but that story deserves its own post.

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.

One of the most common mistakes I see is when organizations rely on totals instead of rates when defining annual operating plan metrics. Many companies set goals like: “Deliver $461M in total cost improvement YoY.” At first glance, this sounds reasonable, but totals often obscure what’s really happening operationally. Absolute metrics are heavily influenced by volume, mix, and scale. If demand increases or decreases, the number moves—even if the underlying process hasn’t improved. For most operational metrics, a rate-based approach is far more useful. Rates isolate process performance from external factors. They normalize for scale, making it easier to determine whether the operation is actually improving. Instead of focusing on totals, define controllable input metrics such as: → Cost per unit shipped → Cost per delivery → Defects per thousand units → Delivery speed per hour Rates force clarity about what managers actually control. For example, a regional manager cannot fully control total delivery volume. But they can influence the cost per delivery, the defects per thousand units, or the delivery speed per hour. When metrics reflect what leaders actually control, ownership and accountability improve dramatically. During my time at Amazon, we would start each year with top-down output targets, such as revenue and fixed costs (especially headcount). But operational performance was largely managed through rate-based input metrics, including: a) Cost per unit shipped b) tp90 click-to-deliver time c) Demand-weighted in-stock rate d) Percent clicks in the top three search results e) tp90 page load time These metrics helped teams focus on sustainable improvements in operational excellence, regardless of demand fluctuations. Totals measure outcomes. Rates measure process performance. If you focus on process performance, desirable outcomes will follow.

✋Before rushing into training models, do not skip the part that actually determines whether the model is useful: Measuring performance. Without the right metrics you are not evaluating a model, you are just validating your assumptions. Check out theses nine metrics every ML practitioner should understand and use with intention 👇 1. Accuracy Good for balanced datasets. Misleading when classes are skewed. 2. Precision Of the samples you predicted as positive, how many were correct. Important when false positives are costly. 3. Recall Of the samples that were actually positive, how many you caught. Critical when false negatives are dangerous. 4. F1 Score Balances precision and recall. Reliable when you need a single metric that reflects both types of error. 5. ROC AUC Measures how well a model separates classes across thresholds. Useful for model comparison independent of cutoffs. 6. Confusion Matrix Exposes the exact distribution of true positives, false positives, true negatives, and false negatives. Great for diagnosing failure modes. 7. Log Loss Penalizes confident wrong predictions. Important for probabilistic models where calibration matters. 8. MAE (Mean Absolute Error) Average of absolute errors. Simple, interpretable, and robust for many regression problems. 9. RMSE (Root Mean Squared Error) Heavily penalizes large errors. Best when you care about avoiding big misses. Strong ML systems are built by measuring the right things. These metrics show you how your model behaves, where it fails, and whether it is ready for production. What else would you add? #AI #ML

Performance metrics commonly used for predictive AI in medicine can mislead clinical decisions unless they are properly chosen and interpreted. 1️⃣ Model evaluation should prioritise discrimination, calibration, and clinical utility. 2️⃣ Many popular classification metrics are statistically improper at clinical thresholds. 3️⃣ Proper measures reward correct probability estimates on average. 4️⃣ Improper measures can rank incorrect models above correct ones. 5️⃣ AUROC remains a valid discrimination measure despite class imbalance. 6️⃣ AUPRC and F1 mix statistics with decision-making and lack clinical grounding. 7️⃣ Calibration plots are more informative than single calibration numbers. 8️⃣ Overall performance metrics add little beyond separate discrimination and calibration. 9️⃣ Clinical utility must explicitly account for misclassification costs. 🔟 Net benefit with decision curve analysis best links models to real decisions. ✍🏻 Ben Van Calster, Gary Collins, Andrew Vickers, Laure Wynants, Kathleen Kerr, Lasai Barreñada, Gael Varoquaux, Karandeep Singh, Karel GM Moons, Tina Hernandez-Boussard, Dirk Timmerman, David McLernon, Maarten van Smeden, Ewout Steyerberg. Evaluation of performance measures in predictive artificial intelligence models to support medical decisions: overview and guidance. The Lancet Group Digital Health. 2025. DOI: 10.1016/j.landig.2025.100916 ❗ Healthcare AI in 2025 - a reflection: https://lnkd.in/dgFCgBAC

Teaching sequential decision analytics IV – Risk   Evaluating performance requires handling the different types of variations in metrics (covered in the previous post).    We typically handle variations in the performance metrics in two ways: -       Base performance – Calculated by averaging performance over time. -       Risk metrics which capture extreme deviations.   Risk has always been treated as a “know it when we see it,” which has produced an array of books with lists of different types of risk (see below), but without any formal definition. Risk can be defined in a very specific way:   “Risk covers metrics that are not captured in the base performance.”   For example, the base performance will capture routine variations in sales which are averaged over an accounting period. But a disruption in the supply chain that produces a major shortage may push customers to find alternative suppliers, which would produce a longer term reduction in demand. This is not captured in averages of sales and profits, so we have to include it in risk.   Risk may be included in the objective function as a term to be minimized, or as a constraint.   The definition of events that are not captured in the base performance has to be defined and quantified, but once it is turned into a formal metric, algorithms can take over.  

One of the emerging trends I am most excited about in the education ecosystem is the increase of educators jumping into the startup world to solve a problem at scale via a new product. This new batch of founders and "teacherpreneurs" have decades of earned insights into specific challenges and opportunities in K12. As new technology has lowered the cost of experimentation and reduced the time to build a new solution, it has opened the door to many who may have previously been shut out of the innovation ecosystem. As these amazing educators are making their way up the learning curve of launching a product and building a non-school-based org, I have been on a mission to share as much information as possible to accelerate that learning. I recently discovered this excellent resource by Abhi Sivasailam on building metric trees. Abhi describes the goal for company data can be reduced to answering four core questions: ▶ What happened? -> Descriptive Analytics ▶ Why did it happen? -> Root Cause Analysis ▶ What's going to happen? -> Predictive Analytics ▶ What should happen? -> Prescriptive Analytics  To do this, he recommends using a metric tree model 📊 . A metric tree is an analytical model that takes metrics and their weighted relationships to represent the flow of inputs into a business and how they are transformed into outputs. The rough steps for this framework are to: ✏ Defining the North Star metric: The North Star metric is the primary measure of success for the company or unit. This metric should align with the long-term value creation for the company. This metric provides direction and focus for the entire organization. ⚙ Decomposing components: The next step involves breaking down the North Star metric into its component parts, identifying the input metrics that directly contribute to the calculation of the North Star Metric and other key output metrics. 🔍 Identifying influences: The final step involves identifying influence metrics, the metrics that have correlative, causal, or qualitative relationship with output metrics – in order to understand how changes in certain metrics can influence the performance of other metrics. Once the tree is set up for your organization, you can walk down the tree to do root cause analysis or walk up the tree for forecasting. The link below is a great video presentation of Abhi introducing the concept. Also, this Miro board link has a built-out metric tree for any B2B SaaS offering. https://lnkd.in/gDN5M2mn #EdTech #K12 #aiineducation #genai #k12innovation https://lnkd.in/guwpMBe3

In Performance Engineering 📊, our bread and butter is drawing the right conclusions from the data we capture. Are you familiar with the 4 types of Data Analytics every Performance Engineer should know? #1 Descriptive to understand what is happening We use live data captured from observability or other monitoring systems to understand how our systems work. This is the most critical feedback for operational teams, and it decides the course of their actions. A lack of understanding of what is happening leads to system outages because teams will miss critical signals and fail to implement corrective actions. #2 Diagnostic to identify the root cause Manual troubleshooting of performance problems is time-consuming, which is why automated root cause analysis systems are so popular today. RCA (Root cause analysis) systems go one level deeper by identifying why things are happening. One typical use case that shows how descriptive and diagnostic data analysis are related is detecting slowness in a checking process (explanatory) and highlighting the responsible database query (diagnostic). #3 Predictive to forecast the future Ideally, we identify the root cause of performance issues and predict how this would impact our business. Imagine the banking system serves 1 million logins every day. If you identify a bottleneck in the login process, you could explain how it would affect your organization. When product teams prioritize issues, they rely on such predictions to solve the most critical problems immediately. Missing this crucial information could set you up for failure. #4 Prescriptive to recommend the best course of action There are many ways to fix performance bottlenecks. When deciding how to solve these problems, we must balance plans, goals, and objectives. An advanced algorithm could test potential outcomes and recommend the best action. This domain has enormous potential because it could enable further automated problem remediation and reduce manual work. Happy Performance Engineering 😊 #Performance #DataAnalytics