Product Performance Metrics

Quotations 📚 "Velocity doesn’t measure value. It only tells you how fast you're shipping—never why." 📚 "North Star metrics are not about volume—they’re about meaning." 📚 "You can’t improve what you don’t track, but tracking everything will sink you." 📚 "The best products don’t win by guessing—they win by learning faster than anyone else." 📚 "Funnels, cohorts, and replays aren’t features—they’re the language of product success." Key Points 📚 Covers 57 core metrics across AARRR: Acquisition, Activation, Retention, Revenue, Referral—plus Engagement and Agile/Lean metrics. 📚 Emphasizes the importance of focus over volume—choose the right 2–3 metrics, not all of them. 📚 Introduces Time to Learn (TTL)—a meta-metric that combines TTM + data analysis + iteration speed. 📚 Clarifies the use of Cohort vs Funnel analysis—cohorts show "who & when," funnels show "where & why." 📚 Highlights tools like Amplitude Session Replay to bridge qualitative and quantitative gaps. 📚 Breaks down essential frameworks: North Star, AARRR, and Google HEART, each with different product-stage applications. 📚 Shows how metrics like TTV, CSAT, CES, NPS, and Stickiness directly shape product-market fit and user loyalty. 📚 Lean metrics like Cycle Time, Work in Progress (WIP), and Throughput help reduce feature bloat and optimize delivery cadence. 📚 Metrics are only meaningful when tied to product strategy, stage, and user journey. Headlines 📚 "The 57 Product Metrics That Actually Matter—And When to Use Them" 📚 "Why Most PMs Track Too Much, Too Early" 📚 "TTL > TTM: The New North Star for Product Learning" Action Items 📚 Define your North Star Metric—one that reflects sustainable user value and growth. 📚 Pair quantitative tracking (e.g., cohort drop-offs) with qualitative insights (e.g., session replays) to find the "why." 📚 Use Cohort Analysis to reveal retention patterns by behavior, not just signup date. 📚 Adopt Google HEART to align UX performance with broader product outcomes. 📚 Track Time to Learn (TTL) as a key feedback loop—how fast you ship, observe, and adapt. 📚 Limit Work In Progress (WIP) to increase speed and reduce cognitive overload. 📚 Segment Feature Adoption and Task Success Rate by persona or cohort for UX tuning. 📚 Use CES and CSAT surveys at key interaction points—not just post-purchase. Risks 📚 Tracking too many metrics leads to noise and diluted insight—focus is strategic. 📚 Misinterpreting NPS as behavior rather than sentiment can lead to faulty decisions. 📚 Failing to use Cohort Analysis masks behavioral trends behind averages. 📚 Relying on velocity or story points to measure impact misaligns effort with value. 📚 Poor TTL management delays learning and increases feature risk and delivery debt. #ProductLeadership #MetricsThatMatter #NorthStarThinking #ExecutiveProductStrategy

Every PM wants to measure the success of their product. But most struggle to do it correctly. As a product management hiring manager, leader, and coach, I've seen that many product managers struggle with defining the right success metrics They focus on generic metrics like acquisition, engagement,  retention These are insufficient. My recommendation is to ask concrete questions when thinking of metrics Here's a list of questions I ask: 𝗧𝗵𝗶𝗻𝗸 𝗮𝗯𝗼𝘂𝘁 𝘁𝗵𝗲 𝘂𝘀𝗲𝗿 𝗳𝗶𝗿𝘀𝘁 1. What is the user’s goal? 2. What human need do they want to fulfill? 3. What action signifies that their need is met? 4. Is that action enough to know user’s job is done? 5. How can I measure that action? 𝗧𝗵𝗶𝗻𝗸 𝗮𝗯𝗼𝘂𝘁 𝘂𝘀𝗮𝗴𝗲 𝗮𝗻𝗱 𝗮𝗱𝗼𝗽𝘁𝗶𝗼𝗻 1. How many users are using the product? 2. How many users should be using it? 3. Which users aren't using it but should be using it? 𝗧𝗵𝗶𝗻𝗸 𝗮𝗯𝗼𝘂𝘁 𝗵𝗼𝘄 𝗺𝘂𝗰𝗵 𝘂𝘀𝗲𝗿𝘀 𝗲𝗻𝗷𝗼𝘆 𝘆𝗼𝘂𝗿 𝗽𝗿𝗼𝗱𝘂𝗰𝘁 1. How many users like the product? 2. How much do they like it? 3. What action(s) show they “like” it? 4. How can I measure those actions 5. Do they like it enough to keep coming back? 6. If yes, how often should they come back? 𝗧𝗵𝗶𝗻𝗸 𝗮𝗯𝗼𝘂𝘁 𝘁𝗵𝗲 𝗾𝘂𝗮𝗹𝗶𝘁𝘆 𝗼𝗳 𝗲𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲 𝘁𝗵𝗲𝘆 𝗮𝗿𝗲 𝗴𝗲𝘁𝘁𝗶𝗻𝗴 𝘄𝗵𝗶𝗹𝗲 𝘂𝘀𝗶𝗻𝗴 𝘁𝗵𝗲 𝗽𝗿𝗼𝗱𝘂𝗰𝘁 1. Are users finding it hard to complete certain actions? 2. Are there things that users dislike? 3. Are there enough options for users to choose from? 4. Are there things that users want to do, but the product doesn’t allow them to? 5. Can we measure all the above? 𝗧𝗵𝗶𝗻𝗸 𝗮𝗯𝗼𝘂𝘁 𝘁𝗵𝗲 𝗾𝘂𝗮𝗹𝗶𝘁𝘆 𝗼𝗳 𝗺𝗲𝘁𝗿𝗶𝗰𝘀 1. Can I cheat on any of the above metrics? 2. Do above metrics give the most accurate answer? 3. Are all metrics simple enough for everyone to understand? 𝗧𝗵𝗶𝗻𝗸 𝗮𝗯𝗼𝘂𝘁 𝘁𝗵𝗲 𝗻𝗲𝘁 𝗶𝗺𝗽𝗮𝗰𝘁 𝗼𝗻 𝘁𝗵𝗲 𝗼𝘃𝗲𝗿𝗮𝗹𝗹 𝗽𝗿𝗼𝗱𝘂𝗰𝘁/𝗰𝗼𝗺𝗽𝗮𝗻𝘆 1. Are  above metrics a true representation of success? 2. Any other parts of user journey I should measure? 3. Will a positive impact on above metrics lead to a negative impact on other critical metrics? 4. Is the tradeoff acceptable? -- How easy or tough do you find creating success metrics? What is your process?

Unveiling the Secrets of Product Metrics: A PM's Guide to Unlocking Product Success In todays landscape product management, data reigns supreme. Product metrics, the quantitative heartbeat of product performance, provide PMs with the invaluable insights they need to decipher user behavior, optimize features, and propel their products to success. By mastering the art of tracking and analyzing these metrics, PMs can transform their products into growth engines. Why Product Metrics Matter for PMs Product metrics are not just numbers; they're the language of product success. They provide PMs with a crystal-clear understanding of how their products are performing, enabling them to set realistic goals, identify areas for improvement, and prioritize product development efforts with precision. In this handy guide, Paweł Huryn 🇺🇦, provides a comprehensive list that outlines the essential product metrics that PMs should track. Essential Product Metrics: The AARRR Framework The AARRR framework, provides a structured way to categorize and analyze product metrics. The framework consists of five key stages: 1/ Acquisition: This stage focuses on how users discover and learn about the product. Key metrics include website traffic, marketing campaign performance, and social media engagement. 2/ Activation: This stage measures the percentage of users who experience value from the product. Key metrics include signup completion rates, feature adoption rates, and time to first use. 3/ Retention: This stage evaluates how well the product retains users over time. Key metrics include daily active users (DAU), monthly active users (MAU), churn rate, and customer lifetime value (CLTV). 4/ Revenue: This stage monitors the financial performance of the product. Key metrics include monthly recurring revenue (MRR), average revenue per user (ARPU), and revenue growth rate. 5/ Referral: This stage assesses how enthusiastic users are about the product. Key metrics include net promoter score (NPS), referral rates, and social media mentions. Prioritizing Product Metrics While tracking all metrics can be tempting, PMs should consider the following factors when prioritizing product metrics include: 1/ Product Goals: Align metrics with the specific goals of the product, such as increasing user engagement, improving retention, or boosting revenue. 2/ Business Objectives: Select metrics that are relevant to the overall business objectives, such as increasing customer lifetime value or reducing customer acquisition costs. 3/ Data Availability: Ensure that the data required for the metrics is readily available and reliable. 4/ Impact on Decision-Making: Prioritize metrics that will directly inform product decisions and drive meaningful change. Are you a data-driven PM? Share what are the most impactful product metrics you track? How have these shaped your product's success journey #productmanagement #productmetrics #userbehavior #productsuccess #growthhacking #PM101

Measuring the right metrics is the difference between winning products & struggling products. 🌟 As Product Managers, we’re constantly tasked with aligning stakeholders, optimizing user journeys, and driving impact—and it all starts with identifying the right metrics. That’s where the *AARM Method* comes in. AARM = Acquisition, Activation, Retention, Monetization A tried-and-tested framework to break down your product’s health and success into actionable, measurable parts. 👉 Acquisition: Are we bringing the right users to the door? 👉 Activation: Are they finding value in their first experience? 👉 Retention: Are they coming back? 👉 Monetization: Are we converting users into paying customers or driving revenue effectively? In my latest guide, I dive deep into: ✅ How to set the right metrics for each stage of AARM ✅ Common mistakes PMs make when defining metrics ✅ Real-world examples from products that nailed it (and those that didn’t) ✅ Step-by-step instructions to implement the AARM framework If you’ve ever found yourself asking, “What should I measure to prove my product’s success?” this guide is for you. 🛠 Bonus: Includes examples tailored for Indian startups and global B2B products to give you a full perspective. Let’s discuss in the comments: What’s the biggest challenge you’ve faced in setting metrics for your product? PS: I run a program that helps folks get better at product management and crack product roles, both in India and abroad. If you want to apply for the program, check out the links in comments. 🌶️ #ProductManagement #MetricsMatter #AARMMethod #GrowthFrameworks #PMTools

Many companies think they're set if they have product usage metrics and can track user engagement. But unfortunately, that's only part of the picture. The real value comes from connecting that usage data to actual business impact. The best product ops teams create the vision and ability to connect those data points. They help relate user behavior metrics to critical business outcomes like revenue, churn, and more. Imagine seeing a feature with rising usage month-over-month. Seems great, right? But what if you found that the usage spike was mainly from a customer segment you're looking to phase out... while adoption from your strategic focus segment had dropped 20%? Yikes. Having that analytical power to map product metrics to business metrics is the secret sauce. With product ops, you can scale those capabilities across the entire product org and executive team, guiding decision-making in the right direction. As Aniel Sud, CTO of Optimizely, puts it: "Product ops becomes data-driven over time, turning data into actual value." And according to Joe Peake of Featurespace, the goal is analyzing each product's revenue opportunity and ROI - not just relying on gut feelings about the market. True product insight means bringing all data together - from product usage to customer feedback to financial impacts. As Shira Bauman of Zapier notes, "Learning about the data that people care about, and partnering across data teams, is so important." With product ops connecting those dots, we get out of the "build trap" and can optimize for real outcomes. The path to successful products lies in combining engagement metrics with business performance. What's your experience been in tying product usage data to business metrics? Share your insights and lessons learned in the comments!