Shaping A Team's Shared Vision

How metrics help you align your teams across the organization. Did this happen to your team before: You worked on improving a metric, were successful doing so, but got backlash from other functions, stakeholders, or product teams? Because something went south on their end? This happened because you where not aligned on your goals and the mechanisms how your goals and metrics influence each other. Ladies & gentlemen, let's hug trees 🤗🌲 More concrete: Metrics trees. 3 Trees to help you align business goal with team goals as well as align goals of different (functional or cross-functional) teams: 1️⃣ KPI-Trees in combination with the User Journey: When you break down how your product with its business model generates revenue, you understand how different KPIs drive revenue growth. On this level, you can find business goals for setting the strategic direction. When you map your user's journey and add conversion and engagement metrics along the tree (not funnel!!!), you can find different leading indicators to improve your product. Now when you find all those connection points between user journey and KPI-tree, you can create a big tree that shows how you can drive business growth through product improvement, and which leading indicator on the product side will help you move which business KPI. It will also help all teams understand the effect of moving their metric on the other metrics. 2️⃣ North Star Metric (NSM): Because high-level business KPIs are not actionable for product teams, we need something more tangible. A NSM is a proxy for business success. It tells us "improving our NSM will lead to improving our business target" which typically is revenue. The NSM is a leading indicator for revenue, but it's a lagging indicator for product success. Therefore, we break it down into its input metrics which are the leading indicators for the NSM. Here's an article by Itamar Gilad that explains this connection very well: https://lnkd.in/eU8g2ziz 3️⃣ Driver Trees: As a general term it describes how different input metrics (drivers) influence your goal. Or how to break down a goal/ outcome 🤷🏻♀️ Some forms: . Cascade outcomes/ metrics through WHYs or HOWs . use helpful frameworks like Goals-Questions-Metrics . or Critical-to-Quality type of trees . or any other method that helps you break down your higher level measurable goal into its input metrics in a structured way. 🔗 Connecting the dots: Technically, each of them serves a different purpose. But all of them create visibility on how metrics and goals influence each other so that teams can visually see the effects. And this helps aligning cross-team efforts towards the same goal for a specific time period. Are you using metrics trees already? Share your experience from PRACTICE. What works for you, what doesn't, how did you make it work? 👇

Computer Vision Accuracy Metrics: Are You Measuring the Right Things? When evaluating a Computer Vision model, accuracy alone rarely tells the full story. A model with 95% accuracy can still fail in production if it misses critical defects, struggles with object localization, or cannot meet real-time performance requirements. Different Computer Vision tasks require different evaluation metrics: 🎯 Classification • Accuracy • Precision • Recall • F1 Score 📦 Object Detection • IoU (Intersection over Union) • AP50 • AP50:95 • mAP 🖼️ Segmentation • Pixel Accuracy • Dice Coefficient • mIoU • Boundary F1 Score 🎥 Tracking • MOTA • MOTP • IDF1 • Track Fragmentation 🔤 OCR & Document AI • Character Error Rate (CER) • Word Error Rate (WER) • Text Detection F1 🏃 Pose Estimation • PCK • OKS • MPJPE 🏭 Industrial Inspection • Defect Detection Rate • False Reject Rate • False Accept Rate • Escape Rate ⚡ Deployment Metrics • Latency • FPS • Throughput • Model Size • Edge Accuracy The most successful Computer Vision systems balance algorithm performance, business objectives, and deployment constraints. The real goal is not just building an accurate model. The goal is building a system that delivers measurable business impact through reliable decisions, operational efficiency, automation, and scalability. Which metric do you consider the most important when evaluating a Computer Vision solution in production? #ComputerVision #ArtificialIntelligence #MachineLearning #DeepLearning #MLOps #EdgeAI #DataScience #IndustrialAI #VisionAI #AIEngineering #VisualAnalytics #ComputerVisionEngineering #VisualGrab #visualgrab

Optimizing the SimpleITK Pipeline for Precision Rigid CT/MRI Registration 🧠💻 When aligning modalities as different as CT (Hounsfield units representing electron density) and MRI (proton density/relaxation times), a naive spatial approach fails. Achieving clinical-grade Rigid Registration requires a robust, mathematically configured similarity optimization stack. This new poster visualizes our refined engineering pipeline using SimpleITK, focusing on the specific architectural components required to compute the optimal transform T (MRI → CT). 🔍 Technical Deep Dive into the Registration Method: To move from the misaligned state ('Before') to the perfect fusion ('After'), we configure a comprehensive ImageRegistrationMethod in SimpleITK: Transform: A 6-degree-of-freedom Euler3DTransform (3D rotation matrix + translation vector). Metric: Given the multimodal, non-linear intensity relationship, we utilize Mattes Mutual Information (an entropy-based metric) as the cost function. Optimizer: A variation of gradient descent (e.g., RegularStepGradientDescentOptimizerv4) iteratively maximizes the Mutual Information. Multi-Resolution Framework: An image pyramid is employed to improve robustness, capture large initial displacements, and avoid local minima. Interpolator: Final resampling uses linear interpolation to map the moving MRI into the fixed CT space. The result is visually flawless structural alignment, crucial for radiotherapy treatment planning and surgical navigation. 📺 See here: https://lnkd.in/eXJaenGY What similarity metric and optimizer combination have you found most robust for multimodal rigid registration in SimpleITK? Let’s discuss below. 👇 #MedicalImaging #SimpleITK #ImageRegistration #ComputerVisionEngineering #BiomedicalEngineering #OpenSourceSDK #Python #MedTechArchitecture #MultimodalAI #RadiologyTech #DataScience #OptimizationAlgorithms #3DVision

Early in my career, I saw brilliant strategies fail, not for lack of ambition, But for lack of alignment. Everyone was running. No one was running in the same direction. That’s when I discovered the real bridge between vision and execution: KPI–OKR alignment. Most leaders confuse the two. But here’s the truth: 1/. OKRs define the destination. 2/. KPIs track the discipline. When you align them, ambition stops being a poster: and becomes a performance system. The KPI–OKR Alignment Framework I’ve used across 75+ growth-stage companies: 🟦 Start with Purpose, Not Metrics Before you set numbers, define the why. If your OKR doesn’t emotionally connect with your people, it won’t operationally connect with your KPIs. ____________________________ 🟦 Anchor Every OKR to Measurable KPIs Example: 1/. OKR → “Improve Customer Experience.” 2/. KPIs → NPS, CSAT, resolution time. Ambition without accountability is optimism. Accountability without ambition is stagnation. ____________________________ 🟦 Design Cadence, Not Chaos Quarterly OKRs. Monthly KPI check-ins. Weekly tactical reviews. Clarity → Cadence → Consistency. That rhythm builds execution energy. ____________________________ 🟦 Make Managers the Multipliers Leaders cascade OKRs. Managers connect them to daily priorities. That’s how culture operationalizes strategy, one task at a time. ____________________________ 🟦 Tell the Story Behind the Score Numbers tell what happened. Stories reveal why it happened. Every KPI review is a chance to teach, not just track. ____________________________ At HumanAlpha, we’ve seen that the most scalable cultures are the ones where ambition is inspiring and accountability is empowering. Because strategy isn’t what you set at the start of the year. It’s what you sustain every single week. The alignment formula is simple: OKRs give direction. KPIs give traction. Together, they create momentum. When was the last time your team felt their KPIs were connected to something bigger than numbers?

Does your Product and CS team share the same source of truth? Without aligned metrics, teams will continue speaking different languages—building, supporting, and engaging customers based on inconsistent definitions. One shared definition for key metrics is essential: ✅ Active users ✅ Feature usage ✅ Feature abandonment ✅ Inactive customers ✅ Ideal customer In my previous roles, we built a methodology with clear definitions, dashboards, and insights to measure product usage, feature adoption, and retention across segments. The impact was huge: 🔥 Consistency – No matter what room we were in, we referenced the same numbers and insights. 🔥 Stronger collaboration – Teams and executives felt the synergy, leading to more cross-functional alignment. 🔥 Better decision-making – A data-driven approach gave us the insights needed to influence roadmaps and engagement programs. Now, ask yourself: If you asked 5 people in your company how they define “feature usage,” would you get 5 different answers? If the answer is yes, it’s time to align. How are you tackling this challenge in your org? #customersuccess Sara Bochino

Is alignment between the GTM departments of Marketing, Sales, and Customer Success possible? Alignment across marketing, sales, professional services, and customer success, has been a topic of discussion since I started in B2B software… Shared objectives based upon the below top 5 metrics that measure the results of cross-functional processes are a good place to start: #𝟏 𝐆𝐓𝐌 𝐀𝐥𝐢𝐠𝐧𝐦𝐞𝐧𝐭 𝐌𝐞𝐭𝐫𝐢𝐜: 𝐍𝐞𝐭 𝐍𝐞𝐰 𝐀𝐑𝐑 Nothing says cross-functional alignment like a metric that includes four different inputs across acquisition, retention, and expansion motions: Net New ARR = New ARR + Expansion ARR + Churn ARR + Down-sell ARR #𝟐 𝐆𝐓𝐌 𝐀𝐥𝐢𝐠𝐧𝐦𝐞𝐧𝐭 𝐌𝐞𝐭𝐫𝐢𝐜: 𝐂𝐀𝐂 𝐑𝐚𝐭𝐢𝐨 The CAC Ratio has three versions and all are materially impacted by each of the three primary GTM departments: Blended CAC Ratio = Marketing + Sales Expenses / New + Expansion ARR New CAC Ratio = Marketing +Sales Expenses / New Logo ARR Expansion CAC Ratio = Marketing + Sales +CS Expenses / Expansion ARR #𝟑 𝐆𝐓𝐌 𝐀𝐥𝐢𝐠𝐧𝐦𝐞𝐧𝐭 𝐌𝐞𝐭𝐫𝐢𝐜: 𝐍𝐞𝐭 𝐑𝐞𝐯𝐞𝐧𝐮𝐞 𝐑𝐞𝐭𝐞𝐧𝐭𝐢𝐨𝐧 (𝐍𝐑𝐑) NRR requires input from all three GTM departments, including: Customer Success is well-positioned to identify up-sell and cross-sell opportunities and create Customer Success Qualified Leads (CSQLs) CEs or AMs work on the CSQLs that CS identified, resulting in expansion ARR Marketing often underinvests in “customer marketing,” which is key to increasing customer awareness of new use cases and/or new products #𝟒 𝐆𝐓𝐌 𝐀𝐥𝐢𝐠𝐧𝐦𝐞𝐧𝐭 𝐌𝐞𝐭𝐫𝐢𝐜: 𝐂𝐮𝐬𝐭𝐨𝐦𝐞𝐫 𝐋𝐢𝐟𝐞𝐭𝐢𝐦𝐞 𝐕𝐚𝐥𝐮𝐞 (𝐋𝐓𝐕) Customer Lifetime Value is an example of a “compound metric” that requires cross-functional alignment and is calculated using the below formula:                    LTV = (Average Revenue per Account*Gross Margin)/ ARR Churn Rate #𝟓 𝐆𝐓𝐌 𝐀𝐥𝐢𝐠𝐧𝐦𝐞𝐧𝐭 𝐌𝐞𝐭𝐫𝐢𝐜𝐬: 𝐈𝐧𝐭𝐞𝐫-𝐃𝐞𝐩𝐚𝐫𝐭𝐦𝐞𝐧𝐭𝐚𝐥 𝐒𝐚𝐭𝐢𝐬𝐟𝐚𝐜𝐭𝐢𝐨𝐧 This might be the most underused, yet most important measurement to facilitate alignment between Marketing, Sales, and Customer Success Some potential satisfaction criteria to measure quarterly via a survey include: Marketing Sales Satisfaction Criteria - Qualified Leads that become Qualified Opportunities - Marketing content that engages middle-of-funnel opportunities - Percent of New ARR generated from Inbound Handraisers Sales Marketing Satisfaction Criteria - Qualified Lead response time - Inbound Lead response time - Win/Loss Feedback Customer Success Sales - Customer Success Qualified Leads Generated - CSQL win rate - Customer Validated Outcomes shared with Sales Marketing Customer Success - Customer Use Cases Published - Customer Events Conducted - Product Content for Education and Engagement If this topic resonates with you and you would like to dive deeper into the details based upon the above Top 5 alignment metrics - this weekend's SaaS Barometer Newsletter might be an interesting read!

Marketing says leads exploded. Sales can’t find them. Product insists churn is the real crisis. Sound familiar? Alignment isn’t a meeting problem, it’s a measurement problem. Different teams slice data until the story flatters them. My remedy is a One‑Page Truth: a single Looker Studio canvas designed for cross‑department decisions. Framework: 1️⃣ North‑Star Line: ↳ Top row shows the one business outcome everyone signs off on (ARR, Net Profit, User LTV). ↳ If it’s not on this line, it’s not strategic. 2️⃣ Department Tiles: ↳ Each team gets one tile: ↳ The KPI that proves their contribution to the North Star (Marketing → PQQLs Sales → Win Rate Product → Net Retention). ↳ Zero secondary metrics, clarity beats completeness. 3️⃣ Variance Flags: ↳ Color‑code any 7‑day variance > 10 % from target. ↳ Flags spark conversation before blame. The magic isn’t the chart design. It’s the forced discipline: ↳ One metric. ↳ One definition. ↳ One shared fate. After implementing this with a SaaS firm, weekly exec calls dropped from 90 minutes to 45 and action items doubled. The data decided, egos didn’t. Which color flag do you dread seeing most? A) Lead quantity drop B) Win rate slide C) Net retention dip

A team shipped 100 features. What measurably changed for customers? AI drops the cost of output, so the risk is bigger: you can ship more while customer outcomes stay flat. 𝗢𝗞𝗥𝘀 are excellent for alignment. They help teams commit, focus, and measure progress. But in practice, OKRs often stop at 𝘄𝗵𝗮𝘁 𝘄𝗲 𝘄𝗮𝗻𝘁 𝘁𝗼 𝗺𝗼𝘃𝗲… without making explicit 𝗵𝗼𝘄 𝘁𝗵𝗲 𝘀𝘆𝘀𝘁𝗲𝗺 𝘄𝗶𝗹𝗹 𝗿𝗲𝗹𝗶𝗮𝗯𝗹𝘆 𝗺𝗼𝘃𝗲 it under real constraints, delays, and dependencies. That’s where Outcome System Result (OSR) comes in. (OSR = outcomes + the system mechanics that produce them.) 🎯 𝗢𝗞𝗥𝘀 measure progress toward a target. 🎯 𝗢𝗦𝗥 forces you to design the mechanism that makes the target repeatable. Because “more activity” is not the same thing as “more value.” 📦 𝗢𝗨𝗧𝗣𝗨𝗧 𝗠𝗘𝗧𝗥𝗜𝗖𝗦 (easy to count): • Features shipped • Tickets closed • Calls made • Leads generated • Content published Useful signals. Not outcome proof. 📊 𝗧𝗥𝗔𝗡𝗦𝗙𝗢𝗥𝗠𝗔𝗧𝗜𝗢𝗡 𝗠𝗘𝗧𝗥𝗜𝗖𝗦 (BACKS proof value was created): • Did customer behavior change (adopt, retain, expand)? • Did customer attitude improve (confidence/trust)? • Did customer condition improve (less risk, less effort, fewer issues)? • Did customer knowledge increase (they learned what they need to succeed)? • Did customer status shift (trial → committed, at-risk → stable)? 𝗥𝗲𝗮𝗹𝗶𝘁𝘆 𝗰𝗵𝗲𝗰𝗸: • You can ship 100 features and change nothing for customers. • You can close 1,000 tickets and still drive satisfaction down if root causes stay. • You can deploy AI everywhere and still miss ROI if workflows, incentives, and adoption don’t change. Output is motion. Transformation is change. If you can’t point to a measurable shift in 𝗯𝗲𝗵𝗮𝘃𝗶𝗼𝗿, 𝗮𝘁𝘁𝗶𝘁𝘂𝗱𝗲, 𝗰𝗼𝗻𝗱𝗶𝘁𝗶𝗼𝗻, 𝗸𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲, 𝗼𝗿 𝘀𝘁𝗮𝘁𝘂𝘀 (𝗕𝗔𝗖𝗞𝗦), you don’t have an outcome. You have activity.