Developing KPIs For Projects

Everyone talks about KPIs. Almost no one discusses how to design the right ones. Here’s one method that works: the KPI Tree (from Bernie Smith). Here’s how it works: 1. Start with your strategic objectives 2. Break them into themes and tactics 3. Link those directly to measurable KPIs What you get is a cause-and-effect hierarchy. Top of the tree = your strategy Bottom = the metrics that bring it to life Why it works: • Sharpens your strategy → Everyone knows what success looks like • Aligns teams → No more “strategy vs. ops” confusion • Generates your KPI list from the ground up • Filters out noise → Keeps focus on what drives results • Adapts fast when your business shifts • Tells a story → Makes strategy easier to communicate Done right? A KPI Tree doesn’t just organise data. It connects what you’re doing with why you’re doing it. Want smarter, more strategic metrics? Start with a KPI Tree. P.S. Like this kind of content? Hit follow

Over the last year, I’ve seen many people fall into the same trap: They launch an AI-powered agent (chatbot, assistant, support tool, etc.)… But only track surface-level KPIs — like response time or number of users. That’s not enough. To create AI systems that actually deliver value, we need 𝗵𝗼𝗹𝗶𝘀𝘁𝗶𝗰, 𝗵𝘂𝗺𝗮𝗻-𝗰𝗲𝗻𝘁𝗿𝗶𝗰 𝗺𝗲𝘁𝗿𝗶𝗰𝘀 that reflect: • User trust • Task success • Business impact • Experience quality    This infographic highlights 15 𝘦𝘴𝘴𝘦𝘯𝘵𝘪𝘢𝘭 dimensions to consider: ↳ 𝗥𝗲𝘀𝗽𝗼𝗻𝘀𝗲 𝗔𝗰𝗰𝘂𝗿𝗮𝗰𝘆 — Are your AI answers actually useful and correct? ↳ 𝗧𝗮𝘀𝗸 𝗖𝗼𝗺𝗽𝗹𝗲𝘁𝗶𝗼𝗻 𝗥𝗮𝘁𝗲 — Can the agent complete full workflows, not just answer trivia? ↳ 𝗟𝗮𝘁𝗲𝗻𝗰𝘆 — Response speed still matters, especially in production. ↳ 𝗨𝘀𝗲𝗿 𝗘𝗻𝗴𝗮𝗴𝗲𝗺𝗲𝗻𝘁 — How often are users returning or interacting meaningfully? ↳ 𝗦𝘂𝗰𝗰𝗲𝘀𝘀 𝗥𝗮𝘁𝗲 — Did the user achieve their goal? This is your north star. ↳ 𝗘𝗿𝗿𝗼𝗿 𝗥𝗮𝘁𝗲 — Irrelevant or wrong responses? That’s friction. ↳ 𝗦𝗲𝘀𝘀𝗶𝗼𝗻 𝗗𝘂𝗿𝗮𝘁𝗶𝗼𝗻 — Longer isn’t always better — it depends on the goal. ↳ 𝗨𝘀𝗲𝗿 𝗥𝗲𝘁𝗲𝗻𝘁𝗶𝗼𝗻 — Are users coming back 𝘢𝘧𝘵𝘦𝘳 the first experience? ↳ 𝗖𝗼𝘀𝘁 𝗽𝗲𝗿 𝗜𝗻𝘁𝗲𝗿𝗮𝗰𝘁𝗶𝗼𝗻 — Especially critical at scale. Budget-wise agents win. ↳ 𝗖𝗼𝗻𝘃𝗲𝗿𝘀𝗮𝘁𝗶𝗼𝗻 𝗗𝗲𝗽𝘁𝗵 — Can the agent handle follow-ups and multi-turn dialogue? ↳ 𝗨𝘀𝗲𝗿 𝗦𝗮𝘁𝗶𝘀𝗳𝗮𝗰𝘁𝗶𝗼𝗻 𝗦𝗰𝗼𝗿𝗲 — Feedback from actual users is gold. ↳ 𝗖𝗼𝗻𝘁𝗲𝘅𝘁𝘂𝗮𝗹 𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴 — Can your AI 𝘳𝘦𝘮𝘦𝘮𝘣𝘦𝘳 𝘢𝘯𝘥 𝘳𝘦𝘧𝘦𝘳 to earlier inputs? ↳ 𝗦𝗰𝗮𝗹𝗮𝗯𝗶𝗹𝗶𝘁𝘆 — Can it handle volume 𝘸𝘪𝘵𝘩𝘰𝘶𝘵 degrading performance? ↳ 𝗞𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹 𝗘𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆 — This is key for RAG-based agents. ↳ 𝗔𝗱𝗮𝗽𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗦𝗰𝗼𝗿𝗲 — Is your AI learning and improving over time? If you're building or managing AI agents — bookmark this. Whether it's a support bot, GenAI assistant, or a multi-agent system — these are the metrics that will shape real-world success. 𝗗𝗶𝗱 𝗜 𝗺𝗶𝘀𝘀 𝗮𝗻𝘆 𝗰𝗿𝗶𝘁𝗶𝗰𝗮𝗹 𝗼𝗻𝗲𝘀 𝘆𝗼𝘂 𝘂𝘀𝗲 𝗶𝗻 𝘆𝗼𝘂𝗿 𝗽𝗿𝗼𝗷𝗲𝗰𝘁𝘀? Let’s make this list even stronger — drop your thoughts 👇

Here are some realistic KPIs that project managers can actually track : 1. Schedule Management 🔹 Average Delay Per Milestone – Instead of just tracking whether a project is on time or not, measure how many days/weeks each milestone is getting delayed. 🔹 Number of Change Requests Affecting the Schedule – Count how many changes impacted the original timeline. If the number is high, the planning phase needs improvement. 🔹 Planned vs. Actual Work Hours – Compare how many hours were planned per task vs. actual hours logged. 2. Cost Management 🔹 Budget Creep Per Phase – Instead of just tracking overall budget variance, break it down per phase to catch overruns early. 🔹 Cost to Complete Remaining Work – Forecast how much more is needed to finish the project, based on real-time spending trends. 🔹 % of Work Completed vs. % of Budget Spent – If 50% of the budget is spent but only 30% of work is completed, there's a financial risk. 3. Quality & Delivery 🔹 Number of Rework Cycles – How many times did a deliverable go back for corrections? High numbers indicate poor initial quality. 🔹 Number of Late Defect Reports – If defects are found late in the project (e.g., during UAT instead of development), it increases risk. 🔹 First Pass Acceptance Rate – Measures how often stakeholders approve deliverables on the first submission. 4. Resource & Team Management 🔹 Average Workload per Team Member – Tracks who is overloaded vs. underloaded to ensure fair distribution. 🔹 Unplanned Leaves Per Month – A rise in unplanned leaves might indicate burnout or dissatisfaction. 🔹 Number of Internal Conflicts Logged – Measures how often team members escalate conflicts affecting productivity. 5. Risk & Issue Management 🔹 % of Risks That Turned into Actual Issues – Helps evaluate how well risks are being identified and mitigated. 🔹 Resolution Time for High-Priority Issues – Tracks how quickly critical issues get fixed. 🔹 Escalation Rate to Senior Management – If too many issues are getting escalated, it means the PM or team lacks decision-making authority. 6. Stakeholder & Client Satisfaction 🔹 Number of Unanswered Client Queries – If clients are waiting too long for responses, it could lead to dissatisfaction. 🔹 Client Revisions Per Deliverable – High revision cycles mean expectations were not aligned from the start. 🔹 Frequency of Executive Status Updates – If stakeholders are always asking for updates, the communication process might be weak. 7. Agile Scrum-Specific KPIs 🔹 Story Points Completed vs. Committed – If a team commits to 50 points per sprint but completes only 30, they are overestimating capacity. 🔹 Sprint Goal Success Rate – Tracks how many sprints successfully met their goal without major spillovers. 🔹 Number of Bugs Found in Production – Helps measure the effectiveness of testing. PS: Forget CPI and SPI - I just check time, budget, and happiness. Simple and effective! 😊

In today’s fast-paced business world, setting clear objectives is crucial to achieving success. 𝐊𝐞𝐲 𝐏𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞 𝐈𝐧𝐝𝐢𝐜𝐚𝐭𝐨𝐫𝐬 (𝐊𝐏𝐈𝐬) are one of the most effective tools for aligning your strategy with business goals. They help measure progress, spot trends, and ensure everyone in the organization is working towards the same vision. But simply having KPIs is not enough—they need to be defined, tracked, and analyzed in ways that make them actionable and meaningful. 𝐻𝑒𝑟𝑒’𝑠 ℎ𝑜𝑤 𝑦𝑜𝑢 𝑐𝑎𝑛 𝑒𝑓𝑓𝑒𝑐𝑡𝑖𝑣𝑒𝑙𝑦 𝑙𝑒𝑣𝑒𝑟𝑎𝑔𝑒 𝐾𝑃𝐼𝑠 𝑡𝑜 𝑑𝑟𝑖𝑣𝑒 𝑠𝑡𝑟𝑎𝑡𝑒𝑔𝑖𝑐 𝑎𝑙𝑖𝑔𝑛𝑚𝑒𝑛𝑡 𝑖𝑛 𝑦𝑜𝑢𝑟 𝑜𝑟𝑔𝑎𝑛𝑖𝑧𝑎𝑡𝑖𝑜𝑛: 1. Define Clear Goals: The first step is to ensure that your KPIs align with the company’s overall objectives. Ask yourself, “What is the organization trying to achieve this quarter, this year?” KPIs should serve as the roadmap to these goals, acting as a guiding light for teams to follow. 2. Measure What Matters: Not all data is created equal. Focus on the metrics that have the biggest impact on your business. This means prioritizing KPIs that directly affect performance, customer satisfaction, revenue, and growth. Identify what truly drives success and avoid getting caught up in vanity metrics. 3. Make KPIs Actionable: KPIs are only valuable if they drive decision-making. Ensure that they provide real-time insights that enable teams to take immediate action. If a metric shows a problem, your teams should be equipped to address it swiftly and strategically. 4. Consistency is Key: Tracking KPIs over time allows you to spot trends and patterns that could indicate underlying issues or opportunities. Regular reviews help keep everyone on track and allow for adjustments when necessary. Consistency also ensures that you're not blindsided by sudden changes. 5. Accountability: Every KPI should have a clear owner—someone responsible for tracking, analyzing, and reporting on that metric. Accountability ensures that the right actions are being taken and encourages continuous improvement. By consistently aligning KPIs with your strategic goals, you create a roadmap that drives measurable progress and keeps everyone in sync. KPIs not only help you measure success but also serve as a powerful tool for making data-driven decisions and achieving long-term objectives. What KPIs have you found most effective in driving strategic alignment within your business? Share your insights in the comments! #BusinessStrategy #KPIs #DataDrivenDecisionMakingg #KeyPerformanceIndicators #PerformanceTracking

📌 How to Select the Right Dashboard KPIs (What you need to know) In today’s digital age, data has become the lifeblood of business strategy. From SMBs to Fortune 500s, companies are rushing to capitalize on their collected data. Boards and investors are pushing for data-driven approaches to stay competitive in rapidly evolving markets. Business intelligence is no longer optional and dashboards are more critical than ever. We’re talking about tracking Key Performance Indicators (KPIs) to make better decisions. But the truth is… Most dashboards fail before they even get built. Why? Because they’re tracking the wrong KPIs. Let’s break this down: Anyone can Google “Top 10 KPIs for marketing” or “Sales dashboard metrics” But effective KPIs are not copied and pasted. They’re designed based on your business model, decision points, and goals. This is something closely tied to your business context. So how do you actually choose KPIs that drive impact? Here’s a 4-step framework: 1️⃣ 𝐒𝐭𝐚𝐫𝐭 𝐟𝐫𝐨𝐦 𝐭𝐡𝐞 𝐃𝐞𝐜𝐢𝐬𝐢𝐨𝐧, 𝐍𝐨𝐭 𝐭𝐡𝐞 𝐃𝐚𝐭𝐚 Before looking at any numbers, ask: → What decisions do we need to make faster? → What outcomes are we trying to improve? KPIs are not about monitoring everything. They’re about enabling better decisions. If you’re not clear on the decision, the KPI is just noise. 2️⃣ 𝐌𝐚𝐩 𝐊𝐏𝐈𝐬 𝐭𝐨 𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐎𝐛𝐣𝐞𝐜𝐭𝐢𝐯𝐞𝐬 Each KPI should directly tie to a strategic goal. Examples: → Sales conversion rate → revenue growth → Customer retention rate → long-term profitability → Cost per lead → marketing efficiency Ask yourself: If this metric improves, will the business benefit? If the answer is no, it’s not a key performance indicator. It’s just a metric. 3️⃣ 𝐁𝐚𝐥𝐚𝐧𝐜𝐞 𝐋𝐞𝐚𝐝𝐢𝐧𝐠 𝐯𝐬 𝐋𝐚𝐠𝐠𝐢𝐧𝐠 𝐈𝐧𝐝𝐢𝐜𝐚𝐭𝐨𝐫𝐬 Lagging KPIs show outcomes. (e.g. total revenue, churn rate) Leading KPIs show input signals. (e.g. pipeline volume, support tickets opened) You need both. Lagging tells you what happened. Leading helps you influence what will happen. Too many dashboards focus only on the past. 4️⃣ 𝐃𝐨𝐧’𝐭 𝐎𝐯𝐞𝐫𝐥𝐨𝐚𝐝 More KPIs ≠ more insight. It usually leads to analysis paralysis. Focus on the 5–7 metrics that truly matter. Kill vanity metrics. (Yes, that includes “likes” and “bounce rates” if they don’t drive decisions.) If you remember one thing today: A good KPI is… ☑ Actionable: You know what to do if it changes ☑ Owned: Someone is responsible for improving it ☑ Contextual: You can compare it (vs. target, vs. last month, etc.) -- 💡 I shared a few months ago a KPI Handbook to help you speed up your KPI selection. If you still haven’t checked it out, here’s the link: https://lnkd.in/e-TzyAkS #BusinessIntelligence #DataAnalytics #DecisionMaking

3 min read - What gets measured gets managed? Wrong. What gets measured gets manipulated. High sales, low profits. High engagement scores, toxic culture. If this sounds familiar, your performance metrics are lying to you. Your best employees are frustrated. Not because they can’t perform - but because they refuse to play the KPI game. You are a victim of the "Campbell's Law". We have all been there :) 1. What is Campbell’s Law? The more a quantitative measure is used for decision-making, the more it will be subject to corruption pressures and the more it will distort the processes it is intended to monitor. In simple terms: If you tie rewards, promotions, or consequences to a metric, people will game the system instead of improving actual performance. 2. How It Shows Up in Performance Management Most companies rely on KPIs, OKRs, and performance ratings to assess employees. But when these become the primary focus, employees: a. Optimize for the metric rather than real impact. b. Find loopholes to “win” the system. c. Engage in unintended negative behaviors to hit the numbers. Real-World Examples a. Sales reps push bad deals to hit targets, leading to high churn. b. Customer support agents avoid tough cases to maintain high ratings. c. Consultants inflate billable hours instead of delivering value. d. Managers pressure teams for high engagement scores rather than improving culture. 4. How to Escape the "Gaming the System" Trap? (Easy skimming of my quadrant work if you are busy) a. Pair Quantitative + Qualitative Metrics – Don’t just track numbers; add peer reviews, 360° feedback, and strategic assessments. b. Encourage Leading Indicators – Focus on behaviors that lead to results, not just the final output. c. Look for Unintended Consequences – If a KPI is improving but real impact is missing, investigate. d. Regularly Rotate Metrics – Keep performance measurement fresh to prevent system gaming. e. Reward Learning & Ethical Behavior – Recognize people who solve problems the right way, not just those who hit numbers. Metrics should serve as a compass, not a target and bad performance management doesn’t look like failure. It looks like everyone ‘hitting their targets’ while the company slowly falls apart. How would you tackle this?

Want better sprints? Start with better metrics. Agile success isn’t about guessing it’s about tracking the right data. ✓ Sprint Velocity & Story Points Gauge your team’s delivery capacity and fine-tune sprint planning with historical data. ✓ Sprint Progress Visualization Visual cues like burndown charts help monitor scope creep and pacing in real time. ✓ Cycle Time vs. Lead Time Understand time efficiency Cycle Time reflects execution, Lead Time reveals delivery performance. ✓ Task Management Efficiency Too many WIP (Work in Progress) items? That’s a signal to reduce multitasking and improve focus. ✓ Team Happiness Index Morale impacts productivity. Regular pulse checks lead to better engagement and retention. ✓ Defect Density Track bugs early. Low defect density means higher product quality and team effectiveness. ✓ Sprint Goal Success Rate Did the team meet the sprint goal? This shows alignment between planning and execution. ✓ Release Frequency Frequent releases mean faster feedback loops and better adaptability to change. ✓ Technical Debt Tracking Identify patterns in rushed work or rework. Addressing this early saves future costs. ✓ Team Collaboration Health Better collaboration leads to shared ownership and faster problem-solving. Common Myths Agile doesn’t believe in metrics. → Agile isn't anti-data it’s anti-waste. Good metrics inform, not control. Velocity is the only metric that matters. → Velocity without quality or context can be misleading. Focus on outcomes, not just speed. Metrics are for managers, not teams. → The best teams track their own metrics to inspect, adapt, and grow. All metrics should be quantitative. Why does this matter? ✓ These KPIs help teams improve sprint over sprint. ✓ Scrum Masters use them to remove blockers and coach teams. ✓ Stakeholders gain visibility into team performance and product health. What’s the toughest KPI to measure in your team? #BusinessAnalyst #ProjectManager #AgileLeadership #ScrumMaster #AgileMetrics

Most software development KPIs measure very short term output, NOT process efficiency or output stability. Velocity tells you how much work was completed. It doesn't tell you that 60% of cycle time was waiting or if the code sucks. If a space shuttle can get to outer space and fly 17,000 mph…but then it explodes, who cares how fast it was able to go? Leadership teams often optimize for metrics that measure output and which are easy to game while the most common underlying problems are process efficiency and progress sustainability. Low process efficiency means bottlenecks at handoffs between roles or teams and misalignment about quality standards and expectations. Code stability shows technical quality. Low code stability means insufficient testing, lack of alignment/clarity on requirements or technical debt. The velocity that matters should be how fast a team is moving forward with a stable product over months. Not how many feature tickets are done at the end of one sprint. That can be deceptive and lead to poor quality…and most dangerously, it can be like carbon monoxide…you don’t see it until too late. Here are 4 KPIs that measure project health instead of immediate output: 1) How often tickets are rejected from QA? 2) Quantity and quality of test coverage. Supporting logic that led to this choice. 3) The presence of instrumentation (user analytics, error monitoring, logging). 4) Quantity of new bugs in existing features after the deployment of new features

📊 𝗤𝘂𝗮𝗹𝗶𝘁𝗮𝘁𝗶𝘃𝗲 𝘃𝘀 𝗤𝘂𝗮𝗻𝘁𝗶𝘁𝗮𝘁𝗶𝘃𝗲 𝗗𝗮𝘁𝗮 — 𝗠𝗮𝗱𝗲 𝗥𝗶𝗱𝗶𝗰𝘂𝗹𝗼𝘂𝘀𝗹𝘆 𝗦𝗶𝗺𝗽𝗹𝗲 Most teams struggle to separate opinions from measurements. But if you want real process improvement, you MUST know the difference. Here are real examples from operations, customer experience, and manufacturing 👇 🔵 𝗤𝘂𝗮𝗹𝗶𝘁𝗮𝘁𝗶𝘃𝗲 (𝗪𝗵𝗮𝘁 𝗽𝗲𝗼𝗽𝗹𝗲 𝘀𝗮𝘆): – “The sofa feels too firm.” – “The box looks weak.” – “Delivery took too long.” – “Line is slower today.” – “Service was terrible.” – “It’s too loud in this area.” 🔴 𝗤𝘂𝗮𝗻𝘁𝗶𝘁𝗮𝘁𝗶𝘃𝗲 (𝗪𝗵𝗮𝘁 𝘁𝗵𝗲 𝗱𝗮𝘁𝗮 𝗽𝗿𝗼𝘃𝗲𝘀): – Firmness = 28 N – Compression strength = 17.5 kg – Lead time = 7.4 days – Cycle time = 51 sec/unit – Customer rating = 2.3 / 5.0 – Noise level = 89 dB 👉 𝗤𝘂𝗮𝗹𝗶𝘁𝗮𝘁𝗶𝘃𝗲 = feelings, opinions, categories 👉 𝗤𝘂𝗮𝗻𝘁𝗶𝘁𝗮𝘁𝗶𝘃𝗲 = numbers, units, limits You need BOTH to build VOC → CTQ, run DMAIC properly, and solve problems without bias. Follow Armando Flores 🔔 for more Quality, Six Sigma and CI insights.

Your dashboards are green but your problems keep getting worse. You're tracking revenue per employee, units produced, and efficiency percentages. All trending upward. But customers still complain about quality. Equipment still breaks down unexpectedly.   Operators still struggle with changeovers. Here's why most metrics miss the mark: They measure what happened yesterday. Not what will happen tomorrow. They focus on outputs. Not the inputs that create those outputs. These 8 KPIs actually predict and prevent problems: 1. OEE (Overall Equipment Effectiveness) Shows equipment reality, not just availability 2. First Pass Yield Reveals true process capability 3. Total Cost of Quality** Captures the real price of problems 4. Employee Suggestion Implementation Rate Measures engagement that drives improvement 5. Setup/Changeover Time Determines your flexibility advantage 6. Supplier Quality Performance Prevents problems at the source 7. Safety Leading Indicators Predicts incidents before they happen 8. Customer Complaint Resolution Time Shows responsiveness that builds loyalty Each metric drives specific behaviors. OEE pushes systematic waste elimination. First Pass Yield forces quality at the source. Cost of Quality makes prevention profitable. The best manufacturing teams measure fewer things. But they measure the right things. And they act on every single number. Stop measuring your past. Start predicting your future. Question for you: If you could only track one KPI for the next 90 days, which would drive the biggest change?