Developing KPIs For Projects

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 👇

Curious about how top SaaS companies consistently hit their revenue targets? The answer lies in driver-based budgeting. 1. Identify Key Drivers First, find the measures that directly impact your ARR. These might include: ➡️Customer Acquisition Cost (CAC) ➡️Customer Lifetime Value (CLTV) ➡️Churn Rate ➡️Average Revenue Per User (ARPU) ➡️Sales Conversion Rates 2. Set Realistic Targets Set achievable goals for each driver: - New ARR: Lower CAC by optimizing marketing spend and improving lead quality. Boost sales conversion rates with better training and tools. - Expansion ARR: Increase CLTV by offering premium features and upselling. - Contraction ARR: Reduce contraction by addressing customer pain points. - Churn ARR: Lower churn by improving customer support and satisfaction. 3. Build a Driver-Based Model Create a budgeting model that integrates these drivers. Use different scenarios to see how changes in each driver impact your ARR. Validate and adjust your model with historical data. 4. Monitor, Adjust, and Align Track the performance of each driver against your targets. Use real-time data to spot trends and make adjustments. Ensure your teams are aligned with the budget and have clear KPIs tied to these drivers.

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! 😊

🚀How to choose the right KPIs for your tech scale-up I've noticed a consistent challenge Many businesses collect extensive data but struggle to identify which metrics actually matter. Here's my top tips on choosing KPIs that will genuinely drive your business forward. 1️⃣ Start with strategy, not metrics: Your KPIs should reflect your strategy through numbers. Before opening any spreadsheets, ask yourself: 🎯where are you aiming to get to? 🥅what specific goals have you set for your team? 🥸how do you differentiate from competitors? Your answers should guide your choice of metrics, not the other way around. 2️⃣Balance leading and lagging indicators: Here's a practical example. If your goal is to increase premium tier adoption from 15% to 25%, that percentage is your lagging indicator. But you need leading indicators to drive progress. For your sales team, this might mean tracking: ✅number of upgrade conversations with existing customers ✅weekly demos of premium features ✅customer feature usage patterns These leading indicators help predict whether you'll hit your target and allow for adjustments while there's still time to impact the outcome. 3️⃣The essential metrics: Some metrics need consistent monitoring regardless of your strategy. In my experience, these include: ☑️MRR ☑️EBITDA ☑️Cash runway ☑️Customer LTV ☑️Customer churn Consider these your fundamental business health indicators. 4️⃣Make data collection seamless: Even the best-designed KPI framework fails if data collection is manual and inconsistent. Two key principles: 🖥️automate wherever possible 🐣capture data at its earliest possible point For example, don't wait for finance to categorize sales by department at month-end. Build it into your invoicing process. 5️⃣Consider the human element: Numbers need context to drive action. For KPIs to create change: 🗣️share them with the people who can impact them 🤔explain the reasoning behind each metric 🔎make them visible and accessible 🫧create clear accountability I've consistently seen that teams who understand why they're tracking certain metrics perform better than those who are simply told what to track. What separates effective KPI frameworks from ineffective ones? Keep your regular reporting focused on metrics that are: 🔗directly linked to strategy 😕simple to understand ✔️actionable by your team ❤️🩹critical to business health But maintain other data points in your systems. They become valuable when investigating problems or identifying opportunities. If you're working on refining your KPI framework, what's the one metric that's transformed how you view your business performance? Want to dive deeper into building effective reporting structures for your scale-up? DM me for a copy of our KPI framework template. #techscaleup #startupmetrics #businessgrowth #datadrivendecisions

Meetings cut in half. Escalations down 75%. No new tools required. A cross-functional marketing team at a major global retailer was drowning: only 22% thought their meetings were a good use of time, and just 39% understood the metrics they were being evaluated against. No calendar audit fixed it. What did? Getting their team working norms aligned, starting with cross-functional goals. With help from Sacha Connor at Virtual Work Insider, the team worked through five intensive 90-minute sessions over two months. Three focus areas made the difference: 🔹 Align goals before anything else. They mapped KPIs side by side and found one function's top priority barely registered for the other. They worked to get aligned, and shared understanding of team metrics went from 39% to 83%. 🔹 Clarify decision rights first. Designated points of contact absorbed a brutal 15:1 staffing ratio, without adding headcount. It also cut down on meetings ("where are we on X") and reduced escalations by 75%! 🔹 Create norms for communication. One rule on Teams: drop an eyeball emoji to acknowledge you've seen a message. Information-flow effectiveness jumped from 41% to 83%. As Sacha put it about Team Working Agreements: most companies put a toolkit on the intranet, maybe a couple teams download it, work through the logistics and call it done. It's not. Three-quarters of teams have never established formal norms. If you're about to layer AI on top of that foundation, you're building on sand. 👉 Full case study in today's newsletter, linked in comments What's actually standing in the way of your team doing this work? #Meetings #Management #AI

Over the years, I've discovered the truth: Game-changing products won't succeed unless they have a unified vision across sales, marketing, and product teams. When these key functions pull in different directions, it's a death knell for go-to-market execution. Without alignment on positioning and buyer messaging, we fail to communicate value and create disjointed experiences. So, how do I foster collaboration across these functions? 1) Set shared goals and incentivize unity towards that North Star metric, be it revenue, activations, or retention. 2) Encourage team members to work closely together, building empathy rather than skepticism of other groups' intentions and contributions. 3) Regularly conduct cross-functional roadmapping sessions to cascade priorities across departments and highlight dependencies. 4) Create an environment where teams can constructively debate assumptions and strategies without politics or blame. 5) Provide clarity for sales on target personas and value propositions to equip them for deal conversations. 6) Involve all functions early in establishing positioning and messaging frameworks. Co-create when possible. By rallying together around customers’ needs, we block and tackle as one team towards product-market fit. The magic truly happens when teams unite towards a shared mission to delight users!

Most KPI systems fail for a simple reason: They measure activity, not impact. ⚙️❌🎯 Over the years and across different companies, I have learned that KPIs only work when they are designed as a decision-making tool, not as a reporting artifact. 📊➡️🧭 Along the way, I developed a personal twist on the SMART framework, specifically on the “A”, to add real operational granularity and make KPIs truly usable in practice. Why SMART still matters (when used properly) SMART is widely known, yet often applied mechanically. When used with intent, it becomes a powerful alignment tool between vision, strategy, and execution. Here is how we apply it at TDK Ventures: S – Specific 🎯 A KPI must clearly articulate what we are trying to achieve. Precision eliminates ambiguity and prevents teams from optimizing around interpretations rather than outcomes. M – Measurable 📏 If progress cannot be observed, tracked, and discussed, it cannot be managed. Measurement is not about control, it is about learning and course correction. A – Accountable | Achievable | Ambitious This is my personal twist, and where I have seen the biggest difference in practice. 🧱 Accountable (Threshold) The minimum acceptable level, aligned with mission and vision. Missing it means we did not meet our collective expectations. ✅ Achievable (Target) What good execution looks like with the resources, time, and capabilities available. Realistic, credible, and strategically aligned. 🚀 Ambitious (Stretch) The goal that stretches the team beyond its comfort zone. Challenging, aspirational, and motivating. At TDK Ventures, stretch goals push us to do things others have never done before, yet remain achievable through strong teamwork and discipline. This three-level “A” transforms KPIs from static scorecards into living management tools. R – Relevant 🧩 A KPI must matter. If it is not tightly connected to mission and strategy, it becomes noise rather than focus. T – Time-bound ⏳ Deadlines create momentum. Time-boxing is what turns intent into execution. Why this framing works 🔄 Focus on outcomes rather than outputs 👂 Enables honest conversations when reality diverges from plan 🌱 Encourages ambition without sandbagging or reckless heroics I have used this framework before joining TDK, and applying it at TDK Ventures reinforced a simple belief: 👉 Great KPIs do not constrain teams, they liberate them 🔓 When goals are clear, meaningful, and well-calibrated, teams spend less time justifying activity and more time creating impact. 🌍✨ Curious how others have evolved SMART to make it truly work in practice. Always keen to exchange perspectives.

Early morning (my time...) long form musings. 💵 Revenue stalls when teams stay in their lanes. Collaboration is the only way to win. Too many companies treat Product Marketing like a content factory, looking to them to crank out decks, one-pagers, and flashy campaigns - without asking a very important question -> Will this actually help sales convert? We don't want to be talking about features, or just running campaigns randomly to create buzz. Or - leave our Sales team to battle buyer objections without support. We all win when these these groups (and others) collaborate early and often, making sure we align around *outcomes* buyers are interested in. That means creating messaging that isn't about us, it's about our prospects and talks to their pain, their needs, their goals. We can't afford to leave Sales guessing how to translate features into business outcomes. We don't want our partners in Product Management frustrated because their vision gets watered down. We gotta talk, people! If we collaborate, sales enablement becomes a growth engine, not an afterthought and conversion rates increase instead of pipelines stalling. Cross-functional engagement isn’t just “nice to have.” It’s how we help our companies turn messaging into revenue. That means: Building messaging that connects outcomes to buyer pain, not specs to features. Partnering with sales before a launch to arm them with tools, stories, and training that shorten the sales cycle instead of slowing it. Making enablement a culture, not an afterthought. If Product Marketing is doing its job, sellers don’t just get collateral. They get clarity, confidence, and conversations that convert. Alignment isn’t optional. It’s revenue. #productmarketing #outcomefocus #salesenablement

A common partnership snafu is that companies want partnership success, but don’t provide the resources to get there. I heard of a case where a whole marketing team quit, the partnerships team was given no marketing support, and they didn't yet have an integration with product -- and yet, the CEO expected the partnership strategy to deliver instant revenue. Wild. But not uncommon. Partnerships can't thrive in a vacuum. They need cross-functional support—marketing, product integration, sales enablement—all aligned to succeed. Before you set revenue targets for your partnerships, ask yourself: Do we have the resources to support them? If the answer is no, you have to help your leadership teams to reconsider their expectations. To help create the cross-functional support needed for partnerships to thrive, here are four strategies: 1. Involve Cross-Functional Leaders from the Very Beginning Bring key leaders from marketing, sales, and product into the partnership planning phase. Early involvement gives them a sense of ownership and ensures they understand how partnerships align with their own goals. Strategy: Schedule a kick-off meeting with stakeholders from each relevant department. Create a shared roadmap that outlines how partnerships will impact each team and their specific contributions. 2. Tie Partnership Success to Department KPIs To gain buy-in, tie partnership goals directly to the KPIs of each department. Aligning partnership outcomes with what each team is measured on ensures they have skin in the game. Strategy: During planning sessions, ask each department head how partnerships can contribute to their targets. Build specific KPIs for each function into the overall partnership strategy. 3. Create a Resource Exchange Agreement Formalize the support needed from each department with a resource exchange agreement. This sets clear expectations on what each function will contribute—whether it's a dedicated product team member for integrations or marketing resources for co-branded campaigns. It turns vague promises into commitments. Strategy: Draft a simple document that outlines the roles, responsibilities, and deliverables each team will provide, then get sign-off from department heads and the executive team. 4. Demonstrate Early Wins for Buy-In Quick wins go a long way toward securing ongoing resources. Identify a small pilot project with an internal team that shows immediate impact. Whether it's a small co-marketing campaign or a limited integration, these early successes build momentum and demonstrate the value of supporting partnerships. Strategy: Select one or two partners to run a pilot with, focused on delivering measurable outcomes like leads generated or product adoption. Use this success story to demonstrate value to other departments and secure further commitment. Partnership success requires cross-functional alignment. Because partnerships don’t happen in a silo.

Using Data to Drive Strategy: To lead with confidence and achieve sustainable growth, businesses must lean into data-driven decision-making. When harnessed correctly, data illuminates what’s working, uncovers untapped opportunities, and de-risks strategic choices. But using data to drive strategy isn’t about collecting every data point — it’s about asking the right questions and translating insights into action. Here’s how to make informed decisions using data as your strategic compass. 1. Start with Strategic Questions, Not Just Data: Too many teams gather data without a clear purpose. Flip the script. Begin with your business goals: What are we trying to achieve? What’s blocking growth? What do we need to understand to move forward? Align your data efforts around key decisions, not the other way around. 2. Define the Right KPIs: Key Performance Indicators (KPIs) should reflect both your objectives and your customer's journey. Well-defined KPIs serve as the dashboard for strategic navigation, ensuring you're not just busy but moving in the right direction. 3. Bring Together the Right Data Sources Strategic insights often live at the intersection of multiple data sets: Website analytics reveal user behavior. CRM data shows pipeline health and customer trends. Social listening exposes brand sentiment. Financial data validates profitability and ROI. Connecting these sources creates a full-funnel view that supports smarter, cross-functional decision-making. 4. Use Data to Pressure-Test Assumptions Even seasoned leaders can fall into the trap of confirmation bias. Let data challenge your assumptions. Think a campaign is performing? Dive into attribution metrics. Believe one channel drives more qualified leads? A/B test it. Feel your product positioning is clear? Review bounce rates and session times. Letting data “speak truth to power” leads to more objective, resilient strategies. 5. Visualize and Socialize Insights Data only becomes powerful when it drives alignment. Use dashboards, heatmaps, and story-driven visuals to communicate insights clearly and inspire action. Make data accessible across departments so strategy becomes a shared mission, not a siloed exercise. 6. Balance Data with Human Judgment Data informs. Leaders decide. While metrics provide clarity, real-world experience, context, and intuition still matter. Use data to sharpen instincts, not replace them. The best strategic decisions blend insight with empathy, analytics with agility. 7. Build a Culture of Curiosity Making data-driven decisions isn’t a one-time event — it’s a mindset. Encourage teams to ask questions, test hypotheses, and treat failure as learning. When curiosity is rewarded and insight is valued, strategy becomes dynamic and future-forward. Informed decisions aren't just more accurate — they’re more powerful. By embedding data into the fabric of your strategy, you empower your organization to move faster, think smarter, and grow with greater confidence.