Resource Allocation Improvements

Let me walk you through the math that should make every CFO question their resource allocation. Using the latest 2025 industry benchmarks from SaaS Capital, here's the stark reality for a typical $200M ARR company: Revenue Responsibility: • Sales team: Manages $40M in new ARR (20% of total revenue) • CS team: Manages $160M in existing/expansion ARR (80% of total revenue) Budget Allocation Reality: • Sales: 13% of ARR ($26M) - up from 10.5% in previous years • Customer Success: 8% of ARR ($16M) - down from 8.5% in previous years Enablement Investment (based on industry benchmarks): • Sales enablement: ~$780K annually (3% of sales budget) • CS enablement: ~$160K annually (64% of CS teams spend <$200K on all programs, tools, and training combined) Investment per revenue dollar managed: • Sales: $780K ÷ $40M = $19.50 per $1M managed • CS: $160K ÷ $160M = $1.00 per $1M managed They're spending 19.5X more per revenue dollar on the team managing 20% versus the team managing 80%. In what other business context would this allocation be considered rational? Imagine if manufacturing allocated 19.5X more maintenance budget to machines producing 20% of output versus those producing 80%. Or if airlines invested 19.5X more in routes generating 20% of revenue versus those generating 80%. The CFO would be fired. Yet this exact irrational allocation persists in SaaS because of tradition, not logic. The Efficiency Data only makes this more baffling: • CS Efficiency: 1 CSM manages $2-5M in ARR • Sales Efficiency: 1 rep manages $600K-$1M in quota • CS is 2-5X more capital efficient, yet receives proportionally less investment The Revenue Economics defy conventional business wisdom: • According to BCG, "Over 25X more value is generated over a customer's lifetime than in the year when the customer is acquired" • TSIA data shows companies with dedicated CS teams achieve 17% base revenue growth vs. just 5% with a sales-only approach • Forrester Research found dedicated CS teams deliver 107% ROI within 3 years Remember the 120-day challenge from my earlier post? For this company, achieving a 1% churn reduction and 3% expansion increase would be worth millions, yet they're investing $1 per $1M in revenue for the team responsible for making that happen. The reality: McKinsey explicitly states that "slower-growing SaaS companies underinvest in customer success." This investment imbalance explains why many companies struggle to achieve the critical 3-5% improvements that transform business fundamentals. Next week, I'll explain why training is the most obvious investment decision in CS and why it's the most overlooked. What's the enablement investment ratio in your organization? Does it match your revenue responsibility ratio? Calculations based on industry benchmarks from SaaS Capital's 2025 Private SaaS Company Spending Benchmarks #CustomerSuccess #Enablement #Investment #ARR #ROI Previous Post: https://lnkd.in/g_bpYGzr Next Post: https://lnkd.in/g76FYFMf

🏨 HOTEL MANAGERS: Stop leaving money on the table! Linear Programming can revolutionize your resource allocation and boost profits by 15-25%. Here’s how using a simple bakery example 🧁 THE REAL CHALLENGE: Your hotel bakery produces 2 cakes with limited resources: • Chocolate Cake: $15 profit each (requires 3 cups flour, 2hrs baker time, 1hr oven) • Vanilla Cake: $12 profit each (requires 2 cups flour, 1.5hrs baker time, 0.8hrs oven) DAILY CONSTRAINTS: • 60 cups flour available • 30 hours baker time maximum • 16 hours oven capacity • Must produce at least 1 of each type (customer variety expectation) THE LINEAR PROGRAMMING FORMULA: 🎯 OBJECTIVE: Maximize 15X + 12Y (Where X = chocolate cakes, Y = vanilla cakes) 📊 CONSTRAINTS THAT LIMIT YOU: • Flour limitation: 3X + 2Y ≤ 60 • Baker capacity: 2X + 1.5Y ≤ 30 • Oven capacity: 1X + 0.8Y ≤ 16 • Minimum variety: X ≥ 1, Y ≥ 1 STEP-BY-STEP SOLUTION: We test corner point solutions (where constraints intersect): • Option 1 (1,1): Profit = $27 • Option 2 (1,18): Profit = $231 ✅ OPTIMAL • Option 3 (14,1): Profit = $222 SURPRISING RESULT: 1 chocolate + 18 vanilla = $231 maximum daily profit COUNTERINTUITIVE INSIGHT: Even though chocolate generates higher profit per unit ($15 vs $12), producing mostly vanilla cakes maximizes total profit! Why? Vanilla uses fewer resources per cake, allowing higher volume production within your constraints. EXCEL IMPLEMENTATION (5 SIMPLE STEPS): 1. Data → Solver (install add-in if needed) 1. Set Objective: Total profit cell (select “Max”) 1. Variable Cells: Number of each cake type 1. Add Constraints: Resource limits + minimum production rules 1. Choose “Simplex LP” method → Solve IMMEDIATE HOTEL APPLICATIONS: 🏨 Room Mix Optimization: Standard vs suite allocation based on housekeeping capacity 👥 Staff Scheduling: Full-time vs part-time ratios within budget constraints 🍽️ Menu Engineering: High-margin vs quick-prep dishes given kitchen limitations 🛏️ Housekeeping Routes: Maximize rooms cleaned within time constraints 💰 Revenue Management: Rate strategies considering demand and capacity limits 🚗 Parking Allocation: Guest vs valet spaces for maximum revenue RESOURCE UTILIZATION ANALYSIS: With optimal solution (1 chocolate, 18 vanilla): • Flour usage: 39/60 cups (65% - room for growth) • Baker time: 29/30 hours (97% - bottleneck identified!) • Oven time: 15.4/16 hours (96% - near capacity) This analysis reveals your baker time is the constraint limiting further profit growth - focus improvement efforts here! KEY BUSINESS TAKEAWAY: Linear Programming reveals non-obvious solutions. Your intuition might say “focus on high-profit items,” but math shows resource efficiency often trumps unit profitability. The “lower profit” option frequently maximizes total returns when resources are scarce.

𝐀𝐮𝐭𝐨𝐦𝐚𝐭𝐞, 𝐀𝐮𝐠𝐦𝐞𝐧𝐭, 𝐨𝐫 𝐂𝐨𝐥𝐥𝐚𝐛𝐨𝐫𝐚𝐭𝐞? 𝐑𝐞𝐭𝐡𝐢𝐧𝐤𝐢𝐧𝐠 𝐇𝐮𝐦𝐚𝐧-𝐀𝐈 𝐖𝐨𝐫𝐤 𝐀𝐥𝐥𝐨𝐜𝐚𝐭𝐢𝐨𝐧 As (Gen)AI tools have become increasingly capable of fulfilling a variety of tasks, the future of work is being reshaped not just by automation—but by collaboration. In their Management Science paper, Andreas Fügener, Dominik Walzner, and Alok Gupta present to determine when AI should (not) replace or augment human workers in judgment tasks: it depends on human-AI complementarity. 𝐊𝐞𝐲 𝐟𝐢𝐧𝐝𝐢𝐧𝐠𝐬 ➡️ 𝐀𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐨𝐧 works best when AI and humans have different strengths across tasks (high between-task complementarity). ➡️ 𝐀𝐮𝐠𝐦𝐞𝐧𝐭𝐚𝐭𝐢𝐨𝐧 is ideal when AI and humans perform similarly on a task, and humans can tell good from bad AI advice (high within-task complementarity). ➡️ 𝐎𝐩𝐭𝐢𝐦𝐚𝐥 𝐬𝐞𝐭𝐮𝐩: AI takes over easy tasks, humans and AI team up on moderately difficult tasks, and humans work alone (or in groups) on complex, uncertain tasks. ➡️ Empirical validation using image classification shows their framework achieves 88% 𝐚𝐜𝐜𝐮𝐫𝐚𝐜𝐲, outperforming both full automation (77%), full augmentation (80%), and humans alone (68%). 𝐈𝐦𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬 ➡️ 𝐉𝐨𝐛 𝐃𝐞𝐬𝐢𝐠𝐧: Leaders should move beyond the “AI vs. human” debate and design work systems that strategically mix automation, augmentation, and human-only tasks. ➡️ 𝐑𝐞𝐬𝐨𝐮𝐫𝐜𝐞 𝐀𝐥𝐥𝐨𝐜𝐚𝐭𝐢𝐨𝐧: Freed-up human capacity from automation can be reallocated to more complex problems, amplifying productivity gains. ➡️ 𝐅𝐮𝐭𝐮𝐫𝐞 𝐑𝐞𝐚𝐝𝐢𝐧𝐞𝐬𝐬: As AI improves, automation might dominate—but human value will persist in tacit knowledge, judgment under uncertainty, and novel problem-solving. In sum, AI should not be blindly implemented. It should be deliberately integrated into workflows to maximize joint value creation. #artificialintelligence #generative #work #futureofwork #productivity Stefano Puntoni Carey Morewedge Ethan Mollick Fabrizio Dell'Acqua Karim Lakhani Dante Donati Miklos Sarvary Carl Benedikt Frey

High-leverage decisions outperform hard work Most performance gaps in business are not caused by effort. They are caused by where decisions are applied. Research in organizational economics and management consistently shows that a small number of decisions drive a disproportionate share of outcomes. Leaders who focus on effort and hours worked often miss the few decisions that actually change trajectory. Working harder on the wrong decisions produces minimal return. What research shows Studies on decision impact demonstrate that outcomes in complex organizations follow a power-law distribution. A minority of decisions account for the majority of long-term performance differences. These are typically decisions related to pricing, hiring standards, capital allocation, incentive design, and distribution channels. Additional research on executive effectiveness shows that leaders who spend more time on high-impact decisions outperform peers who spend more time on operational involvement, even when total hours worked are lower. Study-based situations Situation 1: Pricing decisions Research across multiple industries shows that small pricing changes often have a larger impact on profit than large increases in sales volume. Teams that focused on pricing structure outperformed teams that focused on increasing activity levels. Situation 2: Hiring standards Studies on talent density found that raising hiring standards reduced total headcount needs while increasing output. Organizations that focused on one or two critical hires achieved better results than those that tried to compensate with volume hiring. Situation 3: Resource allocation Research on capital allocation shows that reallocating resources from low-return initiatives to high-return ones consistently outperformed cost-cutting or efficiency programs. The decision of where to allocate resources mattered more than how efficiently teams worked. How effective leaders think about leverage They identify decisions with irreversible or compounding impact They protect time for judgment rather than activity They avoid confusing busyness with value creation They revisit high-leverage decisions regularly instead of optimizing minor ones Effort scales linearly. Leverage scales outcomes. Leadership question Which decision in your role would still matter twelve months from now, even if everything else changed?

Understanding PRB Utilization What is PRB utilization? ✅ PRB utilization measures the percentage of physical resource blocks being used at any given time within a cell. ✅ PRBs are the smallest units of frequency and time resources that can be assigned to users in an LTE network. ✔️ Factors Affecting PRB Utilization Several factors can impact PRB utilization in LTE networks: ✅ User Density: More users in a cell generally require more PRBs for data transmission. ✅ Traffic Demand: Data-intensive applications like streaming, online gaming, or large file downloads increase PRB usage. ✅ Signal Quality: Poor signal conditions (e.g., low SINR) may require more PRBs to maintain quality ✔️ deal PRB Utilization Ideally, PRB utilization should be kept below 80% to 85% to ensure there is sufficient capacity for peak traffic and to maintain a good user experience. Below 60% 📉 Between 60% and 80% 📊 Above 85% 🚨 ✔️ Impact of High PRB Utilization High PRB utilization can lead to: ✅ Congestion: Increasing latency, reducing data rates, and causing dropped packets. ✅ Poor User Experience: Leading to reduced quality in applications like video streaming or VoLTE calls. ✅ Reduced Throughput: More users competing for limited resources can reduce the average throughput per user. ✔️ To mitigate the impact of high DL PRB utilization, network operators can employ various strategies: ✅ Network Optimization: Optimize network parameters and configurations for better resource allocation and efficiency. ✅ Load Balancing: Distribute traffic evenly across available cells and sectors to prevent overloading specific areas. ✅ Capacity Expansion: Add more physical resources, such as additional cell sites or spectrum, to increase the network’s capacity.  ✅ Advanced Technologies: Implement technologies like Massive MIMO and carrier aggregation to enhance capacity and coverage. ✅ Traffic Management: Prioritize critical services and manage congestion to ensure optimal network performance. What’s your take on PRB utilization Let’s discuss.

Kubernetes just got smarter about hardware — and that’s a big deal for AI. Dynamic Resource Allocation (DRA) that went GA in k8s 1.34 unlocks a new way to manage GPUs, FPGAs, and other specialized devices in Kubernetes. Instead of static allocation, DRA lets you define device classes and claims, so workloads get the exact resources they need — no more underutilization or rigid scheduling. Why it matters: 1. For GPU-intensive AI/ML workloads, DRA ensures fair sharing or dedicated allocation, improving performance and efficiency. 2. It simplifies scaling AI pipelines where multiple teams or models need controlled access to accelerators. 3. It future-proofs Kubernetes clusters for emerging workloads in generative AI, HPC, and data analytics. In our first two blog posts on the k8s DRA series, we break down: - Why DRA matters? - What DRA is and how it works - Roles of Cluster Admins and Workload Admins If you’re building or scaling AI workloads on Kubernetes, DRA is a must-know capability. 👉 https://lnkd.in/gEn5uwnS and https://lnkd.in/gVHKbjrx

𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗶𝗻𝗴 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 𝗶𝗻 𝗜𝗧 𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀: 𝗔 𝗣𝗮𝘁𝗵 𝘁𝗼 𝗙𝗶𝗻𝗮𝗻𝗰𝗶𝗮𝗹 𝗘𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆 Effective resource management is essential for IT project success and financial stability. As IT portfolios expand, managing resources across multiple projects becomes complex. It involves aligning the right skills and resources to projects at the right time, enhancing efficiency and minimizing costs. 𝗧𝗵𝗲 𝗜𝗺𝗽𝗼𝗿𝘁𝗮𝗻𝗰𝗲 𝗼𝗳 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 𝗶𝗻 𝗜𝗧 𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀 Resource management ensures effective allocation of personnel, technology, and financial assets to meet project goals. Poor management leads to delays and budget overruns, while effective management enables timely delivery and better cost control. 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗰 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲 𝗔𝗹𝗹𝗼𝗰𝗮𝘁𝗶𝗼𝗻 Strategic allocation equips projects with necessary skills and tools. Management tools help forecast needs and distribute resources based on priorities, minimizing bottlenecks and enhancing productivity 𝗦𝗸𝗶𝗹𝗹-𝗕𝗮𝘀𝗲𝗱 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲 𝗔𝘀𝘀𝗶𝗴𝗻𝗺𝗲𝗻𝘁 IT projects often require specialized skills. A robust strategy identifies skill gaps, enabling businesses to upskill or hire strategically. Assigning resources based on expertise reduces rework and improves quality. 𝗙𝗶𝗻𝗮𝗻𝗰𝗶𝗮𝗹 𝗜𝗺𝗽𝗮𝗰𝘁 𝗼𝗳 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 Financial efficiency in IT projects is linked to resource management. Effective management minimizes waste and aligns costs with timelines, ensuring better outcomes. 𝗥𝗲𝗱𝘂𝗰𝗶𝗻𝗴 𝗖𝗼𝘀𝘁 𝗢𝘃𝗲𝗿𝗿𝘂𝗻𝘀 Accurate forecasting prevents budget overruns from unexpected needs. Continuous monitoring and adjusting resource allocation help projects stay within budget while maintaining quality standards. 𝗠𝗮𝘅𝗶𝗺𝗶𝘇𝗶𝗻𝗴 𝗥𝗢𝗜 𝘁𝗵𝗿𝗼𝘂𝗴𝗵 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 Optimizing resource management increases ROI by ensuring critical tasks are handled by the right personnel. This allows businesses to take on more projects without increasing costs. 𝗟𝗲𝘃𝗲𝗿𝗮𝗴𝗶𝗻𝗴 𝗧𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝘆 𝗳𝗼𝗿 𝗕𝗲𝘁𝘁𝗲𝗿 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 Modern tools are vital for IT projects, providing real-time monitoring and enabling adjustments. Automated scheduling and AI insights help managers predict constraints and make reallocations. 𝗔𝗜 𝗮𝗻𝗱 𝗣𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝘃𝗲 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 AI tools forecast resource needs and anticipate bottlenecks. These insights help businesses make cost-effective decisions, preventing delays and cost overruns. Effective resource management is crucial to the success and financial performance of IT projects. Aligning resources with project needs reduces waste and optimizes the workforce. A strategic approach ensures IT portfolios operate efficiently and profitably. #finance #cfo #transformation