App Feedback Management

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

So, here’s a quick story about how I managed to take our app ratings at airtel from a 3.2 to a solid 4.3 in just 30 days. I was on a call with our account executive at MoEngage where we were discussing the RFM model. If you’re not familiar, RFM stands for Recency, Frequency, Monetization—it’s basically a way to understand customer behavior based on how often they use the app, how recently they’ve been active, and if they’ve made any purchases. After the call, I started thinking—how can we use this data beyond just targeting users for offers or notifications? And then it clicked: we could use this to improve our app ratings. Here’s what I did next: instead of showing the app rating prompt to everyone (which was clearly not working), I decided to get more specific. I created a segment of users who were really engaged—people who were listening music for at least 20-30 minutes a day and opening the app 5-6 times daily. These were our power users, the ones who were already loving the app. But I didn’t just stop there. I made sure the rating prompt would only pop up after an “aha moment,” like after they listened to five songs or changed their hello tune. I wanted to catch them at a high point when they were already feeling good about their experience. Plus, we capped the prompt to only show up once a week, so we weren’t bombarding them. And guess what? It worked! By focusing on the users who were most likely to give us positive feedback, we managed to take our ratings from 3.2 to 4.3 in just a month. It was all about understanding who to ask, when to ask, and how to make that moment feel seamless.

Great products aren’t born, they're iterated. How I turned feedback into product gold at Kraftful. At the start of this year, I pivoted Kraftful to address a challenge that had been a constant in my product management career: how to harness all forms of user feedback. From app store reviews to support tickets and user interviews to survey data - I wanted to hear it all. This pivot was a game-changer. Here’s my approach post-pivot: 👂🏻Comprehensive Listening: I started incorporating diverse feedback sources into our product development cycle. This meant not just listening to one or two channels, but all - user interviews, support tickets, in-product surveys, social media DMs, etc. 🧠 Identifying the Core Message: Amidst this sea of feedback, I learned to find the consistent themes. These are the true insights that can guide meaningful product changes. I was lucky to be able to use Kraftful to identify that core message. 💻 Iterative Development: With a more holistic understanding of user feedback, our iterations became more impactful. We were tweaking features and overhauling interfaces based on a complete picture of user needs. 💫 Continuous Feedback Loop: This new approach turned feedback into a continuous cycle, enriching our product development process and making our iterations far more effective. This pivot taught me that great products evolve through a relentless focus on user feedback, no matter how vast and varied it might be. Feedback is your most valuable resource in krafting™ a product that resonates ✌️ The photo is from our YC Demo Day, long before this pivot. #Kraftful #productmanagement #AI

I talked with 100+ product over the last months They all had the same set of problems Here's the solution (5 steps) Every product leader told me at least one of the following: "Our feedback is all over the place" "PMs have no single source of truth for feedback" "We'd like to back our prioritization with customer feedback" Here's a step-by-step guide to fix this 1/ Where is your most qualitative feedback coming from? What sources do you need to consolidate? - Make an exhaustive list of your feedback sources - Rank them by quality & importance - Find a way to access that data (API, Zapier, Make, scraping, csv exports, ...) 2/ Route all that feedback to a "database-like" tool, a table of records Multiple options here: Airtable, Notion, Google sheets and of course Cycle App -Tag feedback with their related properties: source, product area customer id or email, etc - Match customer properties to the feedback based on customer unique id or email 3/ Calibrate an AI model Teach the AI the following: - What do you want to extract from your raw feedback? - What type of feedback is the AI looking at and how should it process it? (an NPS survey should be treated differently than a user interview) - What features can be mapped to the relevant quotes inside the raw feedback Typically, this won't work out of the box. You need to give your model enough human-verified examples (calibrate it), so it can actually become accurate in finding the right features/discoveries to map. This part is tricky, but without this you'll never be able to process large volumes of feedback and unstructured data. 4/ Plug a BI tool like Google data studio or other on your feedback database - Start by listing your business questions and build charts answering them - Include customer attributes as filters in the dashboard so you can filter on specific customer segments. Every feedback is not equal. - Make sure these dashboards are shared/accessible to the entire product team 5/ Plug your product delivery on top of this At this point, you have a big database full of customer insights and a customer voice dashboard. But it's not actionable. - You want to convert discoveries into actual Jira epics or Linear projects & issues. - You need to have some notion of "status" sync, otherwise your feedback database won't clean itself and you won't be able to close feedback loops The diagram below gives you a clear overview of how to build your own system. Build or buy? Your choice

𝗪𝗵𝘆 𝗦𝗰𝗮𝘁𝘁𝗲𝗿𝗲𝗱 𝗙𝗲𝗲𝗱𝗯𝗮𝗰𝗸 𝗖𝗼𝘀𝘁𝘀 𝗦𝘁𝗮𝗿𝘁𝘂𝗽𝘀 𝗠𝗼𝗿𝗲 𝗧𝗵𝗮𝗻 𝗧𝗵𝗲𝘆 𝗧𝗵𝗶𝗻𝗸  Customer feedback is everywhere - emails, chats and spreadsheets. But 50.9% of startups struggle to consolidate it.  This fragmentation isn't just a minor inconvenience - it’s a major roadblock to growth and innovation.  Here’s why this matters:   ↳ Missed opportunities for product improvement   ↳ Inconsistent customer experiences   ↳ Slower response times to critical issues   ↳ Difficulty in identifying trends and patterns  Pro Tip: Implement a centralized feedback management system. It's not just about collecting data, it’s about making it actionable.  By consolidating feedback, you can:  1/ Prioritize effectively   ↳ Identify high-impact issues quickly   ↳ Allocate resources to areas that matter most   ↳ Focus on changes that drive customer satisfaction  2/ Spot emerging trends quickly   ↳ Detect patterns across different customer segments   ↳ Anticipate market shifts before competitors   ↳ Proactively address potential problems  3/ Align teams around customer needs   ↳ Break down silos between departments   ↳ Create a shared understanding of customer pain points   ↳ Foster a customer-centric culture across the organization  4/ Make data-driven decisions   ↳ Base product roadmaps on real customer insights   ↳ Validate or challenge assumptions with concrete data   ↳ Measure the impact of changes over time  Startups with effective feedback systems are 2.5x more likely to grow by 10%+ annually. Understanding your customers is the key to unlocking that growth.  What challenges have you faced in consolidating and acting on customer insights? Share your experiences below! 👇  ♻️ Share this post to your network if it was insightful! ➕ Follow Ragul Mohan for more like this