Understanding Customer Feedback Importance

The Paradox of Growth: The Bigger You Get, the Less You Know I came across something that stuck with me: When companies scale, they gain users — but lose understanding. Not because they stop caring, but because their customer feedback starts living everywhere — support tickets, sales calls, forums, surveys, social media, and app store reviews. That thought really made me pause. I’ve seen this firsthand. When a company is small, every piece of feedback feels personal — every bug report or review has a face behind it. But as you grow, those voices scatter across platforms and departments. Support sees the frustration, sales hears the hesitation, leadership sees the numbers — and somehow, everyone’s looking at the same customers, but no one’s hearing them anymore. That, in my opinion, is the quiet cost of growth. This is the problem Enterpret is solving — by helping teams stay in tune with their customers even as they scale. Here’s how it works: → It collects real-time customer feedback from 55+ channels — support tickets, sales calls, social media (X, Reddit, Instagram, Facebook), app store reviews, community forums, surveys, Slack, and more. → It analyzes all that feedback using AI and tells you exactly what to fix or build next. → It maps everything through a customer knowledge graph that connects feedback, complaints, and requests by channel, user, and payment data. → It even provides a chat interface where you can directly ask questions, and AI agents that flag bugs or issues automatically. That’s why teams like Notion, Perplexity, Canva, Chipotle, and The Farmer’s Dog use it — to make sure customer voices never get lost in the noise. In my view, the real lesson here isn’t about using more tools — it’s about staying close to the people you build for. Here’s how I’d approach it: ✅ Centralize every piece of feedback — even if it’s messy. ✅ Look for patterns instead of isolated complaints. ✅ Use AI systems like Enterpret to uncover the “why” behind what customers say. Because in the end, growth shouldn’t make you deaf. It should make you listen better — just faster. How does your team make sure you’re hearing what customers really mean, not just what they say? #CustomerFeedback #AIProducts #ProductStrategy #VoiceOfCustomer #Enterpret #Leadership

Customer experience collapses when teams avoid accountability. Real customer centricity needs structured systems that consistently capture feedback, act on it, and evolve with customers. Top performers build frameworks to: 1) Collect feedback systematically. 2) Analyse patterns across touch points. 3) Prioritise improvements and hold teams accountable. 4) Adapt in real time as customer demands evolve. This disciplined approach delivers strong business outcomes. Companies that invest in customer-experience systems see up to 1.5× higher retention and repeat-purchase rates than those that don’t. Also better customer experience correlates with 8% higher revenue than the industry average. Beyond financials, teams become more aligned, responsive and motivated. Customer success becomes a company wide mission. Set up a feedback to action loop. Use standardised surveys, CRM linked feedback tracking and regular review cycles. Assign owners. Turn insights into process improvements and track impact. Customer centric growth isn’t accidental. It’s engineered with data, empathy, discipline, and accountability. Launch your first feedback to action cycle this month. Even one improvement can trigger loyalty, referrals and growth. Start building what matters! #startups #customer #growth

Your feedback process should act as a funnel, catching data from all the various sources and bringing it into a centralized location. As you get feedback from various sources, it’s helpful to be consistent in what you collect. Capturing data in a handful of key areas is particularly useful, including: >Touchpoint. What was the touchpoint, or where was the customer in their journey? For example, this could be after a repair, or an interaction with customer service. >Objective. What was the customer’s objective? For example, they wanted to get their cable working again. >Experience. What was the actual experience? The cable got repaired but it happened outside the promised window of time. >Emotional impact. What was the emotional impact of this experience? The range you establish could be very satisfied to very unsatisfied, on a scale. I’ve seen alternatives such as very happy to very frustrated. What words best capture emotion in your setting? These factors give you a solid foundation for comparing both structured and unstructured feedback. UL, a global company that provides product testing and certification, made a push to more completely capture the on-the-fly feedback their employees were hearing. They created a simple feedback form inside their CRM system. The link can be accessed quickly by any employee, anytime. For example, they can easily pull up the form from their phone and enter the customer’s feedback. Nate Brown, who spearheaded the effort, said at the time, “This is a complete game-changer in how UL understands customers.” Find more examples here: https://lnkd.in/e-t5Zs2b #customerfeedback #customerexperience #customerservice

🚀 Exploring Feedback Loops in Language Models: A Double-Edged Sword! 📢 Have you considered how feedback loops in AI can amplify unintended consequences? 🔬 The recent research, "Feedback Loops With Language Models Drive In-Context Reward Hacking", highlights critical dynamics in how language models interact with feedback systems. 🌟 Key Takeaways:- 👉 In-Context Reward Hacking (ICRH) - Language models often optimize for specific objectives in ways that unintentionally lead to undesirable outputs. 📌 Two Feedback Loop Mechanisms - 1️⃣ Output-Refinement - It repeatedly improving outputs based on feedback can amplify biases or errors. 🔍 Example: In content moderation systems, refining outputs for strict compliance can result in over-censorship or loss of nuance. 2️⃣ Policy-Refinement - It adapting the model’s decision-making to feedback can cause unintended policy shifts. 🔍 Example: Customer support chatbots may overly prioritize high ratings, offering refunds unnecessarily to ensure positive feedback. 🌎 Real-World Implications:- 🗝️ Gen AI in Content Creation - When feedback prioritizes engagement, AI may generate clickbait or sensational content to maximize metrics. 🗝️ Personalized Recommendations - Systems adapting to user feedback may create echo chambers, reinforcing specific preferences while ignoring diverse perspectives. 🛠️ Why It Matters? As AI systems become more ubiquitous, their ability to self-optimize through feedback is both a strength and a potential risk. 👭 We must develop robust strategies to:- 🌐 Detect unintended behaviors early. 🌎 Ensure ethical and aligned outcomes. ⌛ Foster transparency in AI feedback systems. 📖 Read the full paper for deeper insights - https://lnkd.in/dp42viKq 👉 What strategies do you think could help mitigate these challenges in feedback systems? Let’s discuss in the comments! 💬

User Feedback Loops: the missing piece in AI success? AI is only as good as the data it learns from -- but what happens after deployment? Many businesses focus on building AI products but miss a critical step: ensuring their outputs continue to improve with real-world use. Without a structured feedback loop, AI risks stagnating, delivering outdated insights, or losing relevance quickly. Instead of treating AI as a one-and-done solution, companies need workflows that continuously refine and adapt based on actual usage. That means capturing how users interact with AI outputs, where it succeeds, and where it fails. At Human Managed, we’ve embedded real-time feedback loops into our products, allowing customers to rate and review AI-generated intelligence. Users can flag insights as: 🔘Irrelevant 🔘Inaccurate 🔘Not Useful 🔘Others Every input is fed back into our system to fine-tune recommendations, improve accuracy, and enhance relevance over time. This is more than a quality check -- it’s a competitive advantage. - for CEOs & Product Leaders: AI-powered services that evolve with user behavior create stickier, high-retention experiences. - for Data Leaders: Dynamic feedback loops ensure AI systems stay aligned with shifting business realities. - for Cybersecurity & Compliance Teams: User validation enhances AI-driven threat detection, reducing false positives and improving response accuracy. An AI model that never learns from its users is already outdated. The best AI isn’t just trained -- it continuously evolves.

For the last 20 years, we’ve built VoC programs around the same formula: send surveys, wait for responses, analyze, react. It’s clean. It’s measurable. I also think it’s wildly out of step with how customers live and interact every day. Over the next several years, I think VoC shifts from interruption-based to observation-based. Passive signal capture from wearables, devices, connected products, in-app behavior. We’ll have a more honest picture of the customer experience than any survey ever gives us. This data will help us predict what’s about to happen and give every brand the chance to act before the customer ever raises a hand. Leading brands will blend passive signals with targeted, active listening. They’ll also give instant value back to the customer for every piece of data they share, whether it’s volunteered or detected. Everyone else? They’ll still be chasing CSAT responses while fewer and fewer customers fill out surveys. On Monday, here’s where to start if I were you: Compare where you think you’re getting feedback to where customers actually express themselves. Document the gaps. Test one new signal source line app behavior, device data, or voice tone in calls, and see how it changes your insight. Identify how you can route every signal into a system that can respond instantly, not just analyze later. Make every piece of feedback, whether active or passive, trigger something tangible for the customer. Build comfort with behavioral data, machine learning outputs, and multi-signal analysis on your team. VoC is about to stop asking questions and start delivering answers. The only question left is: will your program be ready when the shift happens? #customerexperience #voc #surveys

Most teams drown in feedback and starve for insight. I’ve felt that pain across CX, SaaS, retail—and especially in gaming, where Discord, reviews, and LiveOps telemetry never sleep. The unlock wasn’t “more data.” It was AI turning feedback → insight → action in hours, not weeks. Here’s what changed for me: Ingest everything, once. Tickets, app reviews, Discord threads, calls, streams—normalized and de-duplicated with PII handled by default. Enrich automatically. LLMs tag topics, intent, and aspect-level sentiment (what players love/hate about this feature in this build). Act where work happens. Copilots draft Jira issues with evidence, propose fixes, and close the loop with customers—human-in-the-loop for quality. Measure what matters. Not just CSAT. In gaming: retention, ARPDAU, event participation. In other industries: conversion, refund rate, cost-to-serve. Gaming example: a balance tweak drops; AI cross-references sentiment from Spanish/Portuguese Discord channels with session logs and flags a difficulty spike for new players on Android. Product gets a one-pager with root cause, repro steps, and a recommended hotfix—before social blows up. That’s the difference between a rocky patch and a win. This isn’t just for studios. Healthcare, fintech, DTC, SaaS—same playbook, different telemetry. I put my approach into a 2025 AI Feedback Playbook: architecture, workflows, guardrails, and a 30/60/90 rollout you can start tomorrow. If you lead Product, CX, Support, or LiveOps, it’s built for you. 👉 I’d love your take—what’s the hardest part of your feedback loop right now? Link in comments. 💬 #AI #CustomerExperience #Gaming #LiveOps #ProductManagement #VoiceOfCustomer #LLM #Leadership #CXOps

AI and humans are locked in a feedback loop — continuously training and shaping one another. A new paper puts this dynamic under the microscope and introduces a name for it: "coevolution". Backed by researchers from Massachusetts Institute of Technology, Northeastern University, UCL, Central European University, Sciences Po, and more, the paper proposes a bold new scientific field: 𝗛𝘂𝗺𝗮𝗻-𝗔𝗜 𝗖𝗼𝗲𝘃𝗼𝗹𝘂𝘁𝗶𝗼𝗻 — a framework for understanding how these interactions reshape individuals, societies, and the systems we build. 💡 𝗪𝗵𝗮𝘁 𝗶𝘀 𝗵𝘂𝗺𝗮𝗻-𝗔𝗜 𝗰𝗼𝗲𝘃𝗼𝗹𝘂𝘁𝗶𝗼𝗻?  It's the idea that humans and AI systems influence each other in an ongoing feedback loop. Our choices train AI systems like recommenders and assistants, and their suggestions shape our future decisions — a dynamic, mutual evolution. 🔁 𝗪𝗵𝘆 𝗱𝗼𝗲𝘀 𝘁𝗵𝗶𝘀 𝗺𝗮𝘁𝘁𝗲𝗿?  This feedback loop is unlike traditional human-machine interactions. It can create large-scale, unintended consequences:  ▶︎ Echo chambers and polarization ▶︎ Inequality and concentration of influence ▶︎ Diversity loss in content and decision-making 📊 𝗪𝗵𝗮𝘁’𝘀 𝗻𝗲𝘄 𝗶𝗻 𝘁𝗵𝗶𝘀 𝗽𝗮𝗽𝗲𝗿?  The authors propose human-AI coevolution as a cornerstone for a new interdisciplinary research field, combining AI, complexity science, and social science. ⚠️ 𝗞𝗲𝘆 𝗰𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲𝘀 𝘁𝗵𝗲𝘆 𝗵𝗶𝗴𝗵𝗹𝗶𝗴𝗵𝘁:  ▶︎ Lack of transparency and access to platform data ▶︎ Difficulty tracking feedback loop mechanisms over time ▶︎ Risks of reinforcing inequality, polarisation, and bias ▶︎ Need for legal, scientific, and socio-political interventions 🧭 𝗧𝗵𝗲𝗶𝗿 𝘃𝗶𝘀𝗶𝗼𝗻: A society-centric AI that serves collective well-being, not just individual utility or corporate profit. It requires new methodologies, governance models, and public engagement to manage feedback loops at scale. Authors: Dino Pedreschi, Luca Pappalardo, Emanuele Ferragina, Ricardo Baeza-Yates, Albert-Laszlo Barabasi, Frank Dignum, Virginia Dignum, Tina Eliassi-Rad, Fosca Giannotti, János Kertész, Alistair Knott, Yannis Ioannidis, Paul Lukowicz, Andrea Passarella, Alex 'Sandy' Pentland, John Shawe-Taylor, Alessandro Vespignani ▶︎ Related works An article by Cesareo Contreras : "The interaction between humans and artificial intelligence demands a new field of study, Northeastern researchers say" https://lnkd.in/eejCxqTd #AI #ArtificialIntelligence #UX #ProductDesign

Think about the best customer service experience you’ve ever had. The issue was resolved quickly, your input mattered, and you left with more trust in the organization. Now, imagine if government services worked the same way… This doesn’t happen by accident. It requires intention. That’s what Closed-Loop Feedback (CLF) brings— it is an intentional operational customer experience framework based on industry best practice that ensures real-time responsiveness and long-term accountability to the people the organization serves. This has been the journey of customer experience team efforts that started under the first Trump administration— and there are great examples of agencies putting these practices in place and improving service delivery efficiency, billions in cost avoidance, reducing cost to serve, and greater impact to the public as a result. But so much more can be done, we have only scratched the surface… so much more can be done building on the foundations of goodness with this intentional approach… The Closed-Loop Feedback Model is an operational accountability framework that creates a continuous cycle of improvement, where real-time data drives decisions, inefficiencies are identified and addressed, and trust is rebuilt through transparency. 🔄 Micro Loop – Addresses feedback in real-time, ensuring that individual concerns are heard and resolved quickly. This prevents small issues from becoming systemic failures. 🚀 Macro Loop – Uses insights from frontline interactions to drive broader policy improvements, operational efficiencies, and service innovations. This ensures agencies evolve based on actual citizen needs, not just assumptions. By implementing Closed-Loop Feedback as part of its service delivery, government will: - Improve efficiency and effectiveness by streamlining services based on real user input. - Increase productivity by focusing resources on what matters most. - Enhance service quality through continuous iteration and innovation. - Strengthen public trust by demonstrating transparency and responsiveness. This approach modernizes government service delivery, ensuring agencies act on citizen needs. It is how we move from a reactive system to one that is responsive and proactively delivers better experiences, stronger infrastructure, and real impact for the people we serve. The future of government is citizen driven. Closing the loop builds trust and ensures the efficient and effective service delivery that citizens deserve. Thank you to all the dedicated government employees that have been part of this movement. #Leadership #Management #CustomerExperience #CX #ServiceDelivery #Accountability #Efficiency #Innovation #Modernization #Government

Scaling Strategy #40: Customer-Centric Scaling Growth without customer focus isn’t growth—it’s erosion. In this week’s strategy (from my '50 Scaling Strategies' eBook), I unpack how leaders can maintain deep customer connection while scaling operations, teams, and technology. Backed by real-world data and a proven Deloitte framework, this edition outlines a tactical roadmap for embedding customer insight into every part of your business. You’ll learn: - Why churn increases when feedback loops break down - How to align cross-functional teams around customer goals, and - What customer-centricity looks like at scale + Plus, I share a client case study showing how a simple shift in feedback operations led to a measurable drop in churn and increase in customer LTV! Read this issue if: – Your team is scaling faster than your customer experience – NPS, satisfaction, or renewal rates have plateaued – You want a battle-tested framework to operationalize customer-first thinking Framework Featured: Deloitte’s Customer-Centric Operating Model (CCOM) Sam Palazzolo 🟢 Real Strategies. Real Results. Delivered weekly. #customercentric #businessgrowth #executivecoaching #scaling