Harrison Finch’s Post

Elena Verna is a well-known advocate of the Product-Led Growth approach. In her blog she formulated the ultimate goal of User Activation like this: "Activation is taking a user from signing up to establishing a habit around your core value prop". To get user activation properly you need to set up data and analyze user behavior via Product Analytics. Today I will take Elena's idea further and show you "Why does Product Analytics go beyond product feature analysis?" https://lnkd.in/dVi94pHw #plg #activation #productanalytics

𝗟𝗲𝘃𝗲𝗿𝗮𝗴𝗶𝗻𝗴 𝗗𝗮𝘁𝗮 𝗶𝗻 𝗣𝗿𝗼𝗱𝘂𝗰𝘁 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 In today's competitive landscape, leveraging data in product management is essential for making informed decisions that drive success. Data-driven decision-making involves using data to guide product choices, improving accuracy and outcomes. Here’s why and how you should incorporate data into your product management process: 📊 𝗪𝗵𝘆 𝗗𝗮𝘁𝗮-𝗗𝗿𝗶𝘃𝗲𝗻 𝗗𝗲𝗰𝗶𝘀𝗶𝗼𝗻-𝗠𝗮𝗸𝗶𝗻𝗴 𝗠𝗮𝘁𝘁𝗲𝗿𝘀: 𝙄𝙣𝙘𝙧𝙚𝙖𝙨𝙚𝙨 𝘼𝙘𝙘𝙪𝙧𝙖𝙘𝙮: Basing decisions on data reduces guesswork and subjective biases, leading to more accurate outcomes. 𝙀𝙣𝙝𝙖𝙣𝙘𝙚𝙨 𝙐𝙨𝙚𝙧 𝙐𝙣𝙙𝙚𝙧𝙨𝙩𝙖𝙣𝙙𝙞𝙣𝙜: Data provides insights into user behavior, preferences, and pain points, allowing you to tailor your product to meet their needs better. 𝙊𝙥𝙩𝙞𝙢𝙞𝙯𝙚𝙨 𝙍𝙚𝙨𝙤𝙪𝙧𝙘𝙚𝙨: Data helps prioritize features and initiatives that offer the highest value, ensuring efficient use of resources. 𝙏𝙧𝙖𝙘𝙠𝙨 𝙋𝙚𝙧𝙛𝙤𝙧𝙢𝙖𝙣𝙘𝙚: Continuously monitoring data allows you to measure changes' impact and make necessary real-time adjustments. How to Incorporate Data into Your Product Decisions: 𝘿𝙚𝙛𝙞𝙣𝙚 𝙆𝙚𝙮 𝙈𝙚𝙩𝙧𝙞𝙘𝙨: Identify the most important metrics for your product, such as user engagement, conversion rates, and retention. 𝘾𝙤𝙡𝙡𝙚𝙘𝙩 𝘿𝙖𝙩𝙖: Use tools like Google Analytics, Mixpanel, or custom dashboards to gather data from various sources, including user interactions, surveys, and feedback forms. 𝘼𝙣𝙖𝙡𝙮𝙯𝙚 𝙏𝙧𝙚𝙣𝙙𝙨: Look for patterns and trends in the data to understand user behavior and identify areas for improvement. 𝙈𝙖𝙠𝙚 𝘿𝙖𝙩𝙖-𝘿𝙧𝙞𝙫𝙚𝙣 𝙃𝙮𝙥𝙤𝙩𝙝𝙚𝙨𝙚𝙨: Formulate hypotheses based on data insights and design experiments to test these hypotheses. 𝙄𝙩𝙚𝙧𝙖𝙩𝙚 𝘽𝙖𝙨𝙚𝙙 𝙤𝙣 𝙄𝙣𝙨𝙞𝙜𝙝𝙩𝙨: Use the results from your experiments to make informed decisions and continuously improve your product. 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗮𝗹 𝗘𝘅𝗮𝗺𝗽𝗹𝗲𝘀 𝗼𝗳 𝗗𝗮𝘁𝗮-𝗗𝗿𝗶𝘃𝗲𝗻 𝗗𝗲𝗰𝗶𝘀𝗶𝗼𝗻-𝗠𝗮𝗸𝗶𝗻𝗴: 𝘍𝘦𝘢𝘵𝘶𝘳𝘦 𝘗𝘳𝘪𝘰𝘳𝘪𝘵𝘪𝘻𝘢𝘵𝘪𝘰𝘯: Use data on user engagement and feedback to determine which features to develop next. 𝘈/𝘉 𝘛𝘦𝘴𝘵𝘪𝘯𝘨: Test different versions of a feature or design to see which performs better with users. 𝘜𝘴𝘦𝘳 𝘚𝘦𝘨𝘮𝘦𝘯𝘵𝘢𝘵𝘪𝘰𝘯: Analyze user data to create segments and tailor experiences for different user groups. 🔎 How do you incorporate data into your product decisions? Share your methods and experiences in the comments below! 👇 #ProductManagement #DataDriven #Analytics #UserInsights #DecisionMaking [D15]

Ever wondered how users really interact with your digital products? Product analytics can reveal invaluable insights to help you optimise and personalise their journey. Check out the latest blog from Umair A. where he dives into how these insights can transform your digital products. 👉 Read the full article https://lnkd.in/eYS6ZSex

Data-Driven Development: Using Analytics to Guide Product Decisions In today’s competitive landscape, making informed product decisions is more crucial than ever. At [Your Company Name], we’ve embraced data-driven development to ensure our products not only meet but exceed user expectations. 🔍 Feature Prioritization By leveraging analytics, we gain valuable insights into which features are most important to our users. This helps us prioritize development efforts, ensuring we focus on what’s truly impactful. Data helps us move beyond assumptions, enabling us to make strategic choices backed by real user behavior. 🧔♂️ User Experience Improvements User experience is at the heart of any successful product. With analytics, we can track how users interact with our product, identify pain points, and understand their journey. This data allows us to refine and enhance the user experience, making our product more intuitive and user-friendly. 📊 Informed Decision-Making Data-driven development transforms our approach from reactive to proactive. Instead of guessing what users want, we have concrete data to guide our decisions. This leads to more efficient use of resources, faster development cycles, and ultimately, a product that resonates with our audience. At [Your Company Name], we believe that every data point is a piece of the puzzle, helping us create products that not only solve problems but delight our users. Are you using data to drive your product decisions? Share your experiences and insights below! #ProductDevelopment #DataDriven #Analytics #UserExperience #Innovation #TechTrends

Let’s talk about analytics, but from a product perspective. Ever added an item to your cart but never checked out? Or signed up for a service only to cancel shortly after? We’ve all done it—whether it was finding a better deal, dealing with a frustrating payment process, or getting discouraged by long delivery times. Whatever our reason, the company knew when and how we left. And they’ve likely made product decisions based on similar patterns. Every day, businesses strive to understand user behavior and improve their products. Two important approach that help with this are cohort analysis and funnel analysis—both essential for shaping product success either by driving better retention or higher conversions. Cohort analysis groups users based on shared traits—like when they signed up or which features they interacted with. This helps uncover trends over time. Curious why retention is dropping for last month’s users? Or how new features are affecting different groups? Cohort analysis provides those insights, helping you create more focused, user-centered improvements. Funnel analysis, on the other hand, shows you every step of the user journey and highlights where people are dropping off. Are users abandoning their carts? Not completing sign-ups? Funnel analysis identifies those pain points and helps you streamline the experience, improving conversions along the way. Now, imagine combining both approaches: cohort analysis for tracking trends and funnel analysis for understanding user flow. Together, they provide the full picture of how users engage with a product and where the key improvements lie. For product analysts enthusiasts, understanding both analysis approach is important. Your ability to extract insights from cohort and funnel analysis can impact a product’s success, refine the product experience, increase retention, and drive overall growth. #productanalysis #datatips #analytics

Product Analytics: The act of capturing and analyzing how users interact with a digital product. Ways You Can Use Product Analytics: - Trends: Graph engagement with certain features or pages and compare it against other parts of the product over time. - Funnels: This allows you to track drop-off at each step across a specific a subset of features and pages. - Paths: View all the product journeys users take either leading up to or following a specific interaction, plus a measure of how common or uncommon the next step being taken is. How Product Analytics Can Impact Your Business: - User insights and ROI - Growth and Experimentation: Product analytics unlocks metrics by hypotheses are made and meaningful engagement is measured. - Successful Digital Transformation: It allows teams to analyse existing workflow and employee behaviour within and across software to help decisions about future app purchases and recommend best practices. Product Analytics tracks user interactions in different ways: 1. Events: Any user actions in software applications e.g clicks 2. Event Properties: Specific attributes of the tracked interactions Types of Product Analytics: Codeless analytics: Automatically collects every feature click, screen, and page load inside a product, all without any tracking code. Instrumented Analytics: It requires a tracking code to be installed for almost every interaction for data to be collected about a given feature or product area. #productanalytics

Brian T. O'Neill's recent newsletter points out a basic flaw in how we measure success as product teams: "Adoption = usage. Usage itself is not indicative of benefits. Beyond being able to say "at least somebody is using it," analytics on analytics does not give you insight into whether any value has been created for your users. It doesn't tell you why they are using it, their attitudes, or what success means (or meant) to them in that moment. It also doesn't guide you as to what needs to change." https://lnkd.in/ghsY-PSj Brian's newsletter is another that I have enjoyed following the past 9 months, in addition the Michele Ronsen's newsletter.

𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗶𝗻𝗴 𝗣𝗿𝗼𝗱𝘂𝗰𝘁 𝗜𝗻𝘀𝗶𝗴𝗵𝘁𝘀 𝘄𝗶𝘁𝗵 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 In today’s competitive landscape, understanding users is not a luxury it’s a necessity. We recently implemented a robust product analytics framework at Aster DM Healthcare, and the results have been transformative. The power of product analytics lies in its ability to go beyond surface-level metrics and dig deep into how users interact with our platform. By leveraging tools like GA4 and Google Tag Manager, we’ve tracked every touchpoint in real time revealing insights that have significantly improved our product. Here are a few key benefits we've seen since implementing product analytics: 1️⃣ 𝗘𝗻𝗵𝗮𝗻𝗰𝗲𝗱 𝗨𝘀𝗲𝗿 𝗘𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲: We identified key friction points in the user journey and addressed them quickly, leading to a smoother, more intuitive experience. 2️⃣ 𝗗𝗮𝘁𝗮-𝗗𝗿𝗶𝘃𝗲𝗻 𝗣𝗿𝗼𝗱𝘂𝗰𝘁 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁: Instead of relying on assumptions, we now use real data to prioritize feature releases and updates, ensuring we deliver what our users truly need. 3️⃣ 𝗖𝗼𝗻𝘁𝗶𝗻𝘂𝗼𝘂𝘀 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻: With real-time insights, I can experiment with various product features, measure user engagement, and make adjustments on the fly—allowing for faster and more informed iterations. 4️⃣ 𝗕𝗲𝘁𝘁𝗲𝗿 𝗗𝗲𝗰𝗶𝘀𝗶𝗼𝗻-𝗠𝗮𝗸𝗶𝗻𝗴: The granularity of data helps teams across departments from product development to marketing make informed decisions that directly impact user satisfaction and product growth. Analytics has truly become the foundation for how we approach product improvement and innovation. If you’re not already leveraging product analytics, you’re missing out on invaluable insights that can shape the future of your product. Excited to see where these data-driven insights will take us next! #ProductAnalytics #DataDrivenDecisions #DigitalHealth #GA4 #GTM #ProductDevelopment #UserExperience"

Data is all-important for gauging product performance and delivering great customer experiences! Our latest blog details the key metrics and tools you need for effective product analytics, helping you make data-driven decisions and drive growth. Learn how to track user behavior and improve product performance:  https://bit.ly/4eTCEH2 #ProductAnalytics #DataDriven #TechTrends #BusinessGrowth #xcubelabs #techblog

unitQ Adds Product Analytics To Its User Feedback Platform For Real-Time Actionable Insights Into What Users Are Doing And Saying https://lnkd.in/dNxAgwmR #martech360 #newsmedia #news #marketingtechnology #artificialintelligence #analytics #customerexperience #QualityAutomation