Performance Measurement Baselines

Your project started without a baseline? Welcome to 90% of real-world Monitoring and Evaluation. Most programmes launch with urgency, political pressure, or donor timelines, not perfect data systems. That doesn’t mean you can’t measure change. It just means you need to reconstruct the “before” using the tools seasoned evaluators rely on: 🔹 Start with what already exists Intake forms, early reports, planning documents, grant proposals, even if they weren’t created for MEL, they often contain reference points you can extract. 🔹 Use recall methods strategically Ask participants and staff to describe conditions before the intervention, but anchor their memory to major events: ↳ “Before the school opened…” ↳“Before the water point was installed…” This reduces bias and increases accuracy. 🔹 Pull secondary data to fill the gaps Census tables, ministry surveys, NGO assessments, anything close in geography and timeframe can provide a credible reference. 🔹 Triangulate relentlessly Never rely on one source. Cross-check community recall with government data, staff insights, and documentation. Retrospective baselines aren’t shortcuts. They’re structured, defensible methods for rebuilding the past and they’re what experienced evaluators use when perfection isn’t possible (which is most of the time). 🔥 If you want more practical MEL techniques like this with no jargon, no theory-only talk, join my mailing list for weekly insights that will sharpen your practice. #Baseline

Are You Truly Measuring Energy Savings Scientifically? In any ISO 50001-compliant Energy Management System (EnMS), Establishing an Energy Baseline (EnB) and selecting Energy Performance Indicators (EnPIs) are the absolute foundation. Without them, you cannot reliably prove energy savings or demonstrate continuous improvement. Let us see clear breakdown of these critical steps: 🔹 1. Establishing the Energy Baseline (EnB) The EnB is your quantitative reference point: "How much energy would we have used today if no improvements had been made?" Data Collection: Gather at least 12 months of historical data (energy consumption + relevant variables like production volume, degree days) to capture seasonality. Normalization: Avoid simple static baselines (e.g., last year’s total). Identify and account for key drivers (weather, output levels) that significantly affect consumption. Regression Analysis (Best Practice): Use linear or multivariable regression to build a model (e.g., y = mx + c). This lets you calculate expected vs. actual energy use under current conditions. 🔹 2. Selecting Energy Performance Indicators (EnPIs) EnPIs should be hierarchical — from facility-wide down to specific equipment ,and focus on efficiency, not just total consumption. A. High-Level (Facility-Wide) Energy Use Intensity (EUI): Total energy ÷ floor area (kWh/m²/yr) — ideal for buildings. Energy Intensity (EI): Total energy ÷ production output (e.g., kWh/unit) , standard in manufacturing. B. System & Equipment Level (Significant Energy Users) Chillers: kW/ton or COP Boilers: Combustion efficiency (%) or steam intensity Compressed Air: Specific power (kW/100 cfm) C. Productivity Metrics Link energy to value: kWh/kg of product or energy cost per unit sold. The Process in a Nutshell Identify Significant Energy Users (SEUs) Determine key driving variables Build the EnB using regression on historical data Choose EnPIs that track true efficiency Getting these steps right turns energy management from guesswork into data-driven success. And a final question for energy managers, sustainability leaders, and facility engineers: what has your experience been with baselines and EnPIs? Have you encountered common pitfalls, or found go‑to tools, for regression analysis? If you have a question, insight, or story to share, feel free to comment. #EnergyManagement #ISO50001 #EnergyEfficiency #Sustainability #EnMS #EnergyPerformance #NetZero

Does employee performance at your company rely on a single, a once-a-year rating? Are you optimizing for storytelling or outcomes? How we measure performance directly impacts the results we get. In order to leverage performance systems to compound employee impact, we need to look holistically, at data captured over time to understand trajectory and velocity of employee outcomes. Here are 5 ways to evaluate performance beyond the 5-point scale: ➤ Level Progression: Progression measured by performance across competencies for their role at their level (which can go up or down over time, but generally show the directionality of someone's progress). ➤ Growth Rate: How are employees performing in their level, over time? Growth rate measured by % of change over time (up or down). Rather than subjective "potential" see potential through growth velocity of individuals or teams. ➤ Skill Density: Measure of performance across specific skill dimension. Enables you to benchmark strengths or weaknesses (e.g., IC4s light on "Execution") by function, level, geo and other factors. ➤ Alignment Rate: Are managers and employees aligned on performance expectations? If yes, performance improves, if no, it goes down. Alignment rate is measured by how employees rate themselves vs, their manager. The more dimensions, the greater the alignment potential. ➤ Distribution: Not looking for a bell curve here, but understanding talent density. How do you get the most employees performing their best, and are folks evaluated properly? Why this is different: • Transparent performance expectations and observable behaviors • Focus on nuances of individual performance and growth trends • Alignment as an improvable metric to achieve greater outcomes • Fosters proactive performance improvement (vs. corrective PIPs) 👉 Want the 60-min crash course on building a modern performance program (levels, frameworks, feedback, goals, assessments)? Comment “crash course.” Tagging a few folks here that I know are focused on performance transformation (give them a follow!): Russ Laraway, Lissa Minkin, Shelby Wolpa, Kim Minnick, Jessica Z.

I recently spoke with an iCIMS System Administrator who thought their time-to-fill was 58 days. After just 20 minutes of consultation, we discovered it was actually 29 days - almost meeting their goal of 28 days! What happened? We removed statistical outliers, applied proper filters, and most importantly, customized time-based metrics to match what leadership actually wanted to measure. This isn't about prettier reports - it's about establishing accurate baselines that reveal your true performance as a baseling for quantifying ROI. This article explains exactly what we did together, step-by-step.

The Right Way to Evaluate a Machine Learning Model👇 (You Need a Baseline) When building a machine learning model, it's easy to focus solely on improving metrics like accuracy, precision, or recall. However, without a baseline for comparison, it's impossible determine if your model's performance is truly impressive, or just average. A baseline provides a point of reference, helping you gauge whether your model adds value beyond simpler or existing solutions. Let’s explore some common types of baselines and how they can help you evaluate your machine learning model: --- 1. Random Prediction Baseline A random baseline involves generating predictions entirely by chance. For example, in a binary classification task, a random predictor might classify 50% of the data as one class and the other 50% as the second class. You then compute the evaluation metrics (e.g., accuracy, F1 score) for these random predictions. While the results may be poor, they provide a simple benchmark that a good model should outperform. --- 2. Heuristic Baseline A heuristic baseline uses simple rules or domain knowledge to make predictions. For instance, in a spam detection task, a heuristic could classify any email with the word "free" as spam. Although simplistic, heuristic baselines can be surprisingly effective. --- 3. Zero Rule Baseline The zero rule baseline (or “most frequent class baseline”) predicts the most common class in your dataset for every observation. For example, in a dataset where 70% of samples belong to class A, the zero rule predictor would always predict class A. This baseline works well in imbalanced datasets. --- 4. Human Baseline A human baseline involves comparing your model’s performance to that of human experts performing the same task. This baseline is especially useful in areas like medical diagnostics or language translation, where human performance sets the standard. --- 5. Existing Solution Baseline An existing solution baseline compares your model’s performance to a pre-existing method. This could include traditional if-else code, a competitor’s product, or another ML model. --- WHY BASELINES MATTER Baselines aren't just academic exercises ... they’re essential for understanding whether your model solves the problem effectively. A good model should significantly outperform trivial baselines like random predictions and zero rule. It should also demonstrate measurable improvements over heuristics, human benchmarks, or existing solutions, depending on the task. By setting clear baselines at the start, you build a measurement framework that ensures your model actually solves the problem and drives meaningful impactful in deployment. #AI #datascience #machinelearning -------------------------- Hi! I’m Joshua Ebner I do AI and data science consulting. If you're thinking about using AI or machine learning in your business, the click the link in my bio and reach out for a free initial consult.

𝐇𝐨𝐰 𝐭𝐨 𝐄𝐯𝐚𝐥𝐮𝐚𝐭𝐞 𝐋𝐋𝐌𝐬: 𝐇𝐮𝐦𝐚𝐧𝐄𝐯𝐚𝐥, 𝐌𝐌𝐋𝐔, 𝐓𝐫𝐮𝐭𝐡𝐟𝐮𝐥𝐐𝐀 High benchmark scores don’t guarantee real-world reliability. A model scoring 90% may excel at the test but fail in production: hallucinations, brittle reasoning, silent failures, and performance drops under distribution shifts are common. 𝐊𝐞𝐲 𝐁𝐞𝐧𝐜𝐡𝐦𝐚𝐫𝐤𝐬 1. 𝐇𝐮𝐦𝐚𝐧𝐄𝐯𝐚𝐥 – 𝐂𝐨𝐝𝐞 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐨𝐧 -Tests small, self-contained functions. Measures syntax, instruction following, pattern completion. -Does not test multi-file reasoning, debugging, system-level engineering, or long-term consistency. 2. 𝐌𝐌𝐋𝐔 – 𝐊𝐧𝐨𝐰𝐥𝐞𝐝𝐠𝐞 𝐁𝐫𝐞𝐚𝐝𝐭𝐡 -Covers 50+ academic domains. Tests recall and structured reasoning. -Fails to capture messy real-world prompts, multi-step tool usage, self-correction, and adaptive intelligence. 3. 𝐓𝐫𝐮𝐭𝐡𝐟𝐮𝐥𝐐𝐀 – 𝐇𝐚𝐥𝐥𝐮𝐜𝐢𝐧𝐚𝐭𝐢𝐨𝐧 𝐑𝐞𝐬𝐢𝐬𝐭𝐚𝐧𝐜𝐞 -Checks if models avoid repeating common misconceptions. Surfaces factual alignment issues. -Still misses dynamic hallucinations like fake citations, invented APIs, and subtle errors in reasoning. 𝐓𝐡𝐞 𝐁𝐢𝐠𝐠𝐞𝐫 𝐏𝐫𝐨𝐛𝐥𝐞𝐦 Benchmarks assume IID data. Production is non-IID: user prompts drift, context length increases, domains mix, tools fail. Leaderboard leaders often fail in real pipelines. 𝐒𝐞𝐧𝐢𝐨𝐫 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫 𝐀𝐩𝐩𝐫𝐨𝐚𝐜𝐡 𝐄𝐯𝐚𝐥𝐮𝐚𝐭𝐞 𝐚𝐠𝐚𝐢𝐧𝐬𝐭 𝐲𝐨𝐮𝐫 𝐟𝐚𝐢𝐥𝐮𝐫𝐞 𝐦𝐨𝐝𝐞𝐬, 𝐧𝐨𝐭 𝐥𝐞𝐚𝐝𝐞𝐫𝐛𝐨𝐚𝐫𝐝 𝐬𝐜𝐨𝐫𝐞𝐬: -Offline benchmarks for baseline ability -Domain-specific data tests -Adversarial and ambiguous prompt tests -Long-context and multi-step workflows -Tool integration and consistency checks -Latency-cost tradeoffs 𝐓𝐚𝐤𝐞𝐚𝐰𝐚𝐲: Benchmarks are useful signals. Real intelligence in production is measured by reliability, adaptability, and context-aware performance, not scores. Follow Karthik Chakravarthy for more insights

Most of us forced to optimise campaigns like intraday traders. Open Ads Manager. Look at last 7 days. React. That’s how you go nuts. But, multi-baseline performance is sanity. Today vs last 7 days. vs last 14. vs last 28. One view. You instantly see: Seasonality Day of week swings Whether today is actually bad, or just worse than yesterday but fine vs last month That’s the difference between an intraday trader and a portfolio manager. The trader panics on every red candle. The portfolio manager looks at trend, not noise. Multi-baseline gives you that portfolio view for your ad account. But here’s the catch: Even a beautiful multi-baseline screen is useless if it doesn’t connect to business objectives. I still need to know: How is new customer acquisition doing vs baselines What about retention / repeat Are my high AOV products holding up Which campaigns are aligned to actual business goals, not just cheap clicks So yes, multi-baseline is non-negotiable. It stops emotional decisions and exposes seasonality clearly. But the real power is: Multi-baseline view granular breakdown by business objective. That’s where media buying starts to look like real portfolio management, not just fancy refresh of last 7 days.

How to Measure AI ROI: A Step-by-Step Guide That Actually Works Most companies waste millions on AI without knowing if it works. Looking to maximize your AI investments? Here's your roadmap to success: Step 1: Define Clear Success Metrics • Revenue impact • Cost savings • Time saved • Customer satisfaction scores • Employee productivity gains Step 2: Implement the AI Decision Scorecard • Compliance checks • Quality assessment • Employee experience • Business impact measurement Step 3: Set Baseline Measurements • Current performance metrics • Cost of operations • Time per task • Error rates • Customer feedback Step 4: Track Progress • Weekly data collection • Monthly progress reviews • Quarterly ROI calculations • Stakeholder feedback • Performance adjustments Step 5: Scale What Works • Document successful use cases • Share wins across teams • Replicate winning patterns • Train more users • Expand implementation The Truth: Only 22% of companies measure AI ROI effectively. Don't be part of that statistic. Remember: If you can't measure it, you can't improve it. Ready to transform your AI investments into real results? Share your biggest AI measurement challenge below 👇