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Having processed 100,000+ customer feedbacks with AI, I can confidently say: Customer feedback analysis is one of the best use cases for AI. And it’s also one of the worst. Let me explain: I’ve worked a lot lately on use cases that deal with analyzing customer feedback with AI: → Comments → Surveys → Reviews Thanks to Large Language Models, it’s never been easier to process this stuff at scale. Where you previously had to build complex topic models, you can now simply paste text into ChatGPT and get an interesting-sounding answer. Which is exactly the problem. Because dumping customer feedback into an LLM doesn’t actually give you any insight. It gives you a summary. Like: “Users want better usability.” “They’re confused by onboarding.” (You didn’t need AI to tell you that.) LLMs rarely generate true novelty. They compress to the mean. Which means they’ll repeat what you already suspect – just faster and fancier. If you want actual insight, you need structure. Here’s the 5-step method I use with clients: 1. Start with a problem hypothesis. Don’t ask: “What are they saying?” Ask: “Are they struggling with X?” 2. Build a taxonomy that maps to action. “Didn’t get login email” → “Email deliverability issue” “Too expensive” → “Pricing objection” 3. Count what matters. Still the most underrated skill in analytics: frequency by category. 4. Analyze temporal trends. *When* people say something often matters more than *what* they say. 5. Look for what’s missing. No positive feedback in 1,000 comments? That’s a signal too. I’m sharing the exact approach I use to turn raw customer comments into real buyer insight – using AI the right way. If you work with feedback, you’ll want this breakdown. Sign up before Friday 12pm CET → https://lnkd.in/eFzzQrMJ

Most product managers prioritize features the wrong way. AI can fix that. Here are 3 powerful AI prompts to revolutionize your workflow. Here are 3 AI prompts that will change how you rank features based on user needs and business impact: 1️⃣ Comprehensive Feature Analysis: A deep dive into each feature's potential impact and alignment with goals. 💡 Prompt: "Analyze the following features: {feature_list}. For each feature, provide a detailed assessment of its potential impact on user satisfaction, retention, and revenue growth. Consider our current user base demographics, market trends, and competitive landscape. Prioritize these features based on their alignment with our Q4 goal of improving user retention by 15%. Finally, rank the features in order of priority and explain the rationale behind this ranking." 2️⃣ User Feedback Synthesizer: AI powered analysis of user pain points and feature requests. 💡 Prompt: "Aggregate and analyze customer feedback from the following sources: {feedback_sources} (e.g., app store reviews, customer support tickets, user interviews, NPS surveys). Identify the top 5 recurring themes or pain points mentioned by users. For each theme, provide specific examples of user quotes or data points. Rank these themes based on frequency of mention and severity of impact on user experience. Then, map each theme to potential feature improvements or new feature ideas. Prioritize these feature ideas based on their potential to address user pain points, estimated development effort, and alignment with our product strategy. Share a detailed rationale for your prioritization, including any potential risks or trade-offs to consider." 3️⃣ Development Effort Estimator: A comprehensive analysis of resource requirements. 💡 Prompt: "Estimate the development effort for implementing {feature_name} in our {product_type}, considering our team of 10 engineers and 8-week timeline. Break down the implementation into key components or stages (e.g., design, frontend development, backend development, testing, deployment). For each component, estimate the number of engineer-days required, potential technical challenges, and any dependencies on other systems or third-party integrations. Consider our team's expertise and any learning curve associated with new technologies. Identify any potential bottlenecks or risks that could impact the timeline. Suggest strategies to mitigate these risks, such as parallel development tracks or phased rollout approaches. Provide a confidence level (low, medium, high) for each estimate and explain the reasoning. Finally, give a range estimate for the total development time (best case, expected case, worst case) and suggest any features or scope that could be adjusted to fit within the 8-week timeline if necessary." Product Managers, these AI prompts are designed to enhance your decision making, not replace it. Use them to gain data-driven insights, then apply your expertise to make the final call.

If you're a PM or PMM swamped with customer feedback, it's time you built an AI workflow. This is a problem I had to tackle at vFairs. I wasn't sure how to munch on 2000+ reviews across G2/Capterra and the many more from other surveys we conduct. Making sense of all the feedback themes can get overwhelming, especially when you have to slice them across segments for better results. I initially solved my problem in 3 steps: 1. Used Browse AI to scrape public reviews into a spreadsheet. 2. Added the sheet as a knowledge base to a Custom GPT. 3. Created a prompt library to mine the reviews (see attached). Eventually, I moved to Claude Projects (I found the analysis a tad better). Now, I'm working on crafting automated workflows to do this on auto-pilot. Using a tool like n8n or Zapier, I could scrape a Google Drive or web pages, pipe them into a ChatGPT block with my set of prompts, and have it Slack me the results every month. Next step will be to explore building an agent that can do a lot more: widen the net to explore wherever vFairs is mentioned on the net and send a summary to my email. I'm still rough on the edges with my agent skills, though. (if anyone has a resource on that, I'd appreciate that!) Bottom line: AI is changing how we process feedback. Gone are the days when volumes of valuable customer feedback remain un-analyzed due to other escalating priorities. -- Are you using AI to analyze feedback?

Customer Sentiment Feedback Analysis Project 📊✨ After three consecutive projects away from Excel, I felt it was time to return home for a while 😅😅 and sharpen my skills by analyzing and visualizing data showing how satisfied customers were after using different types of products purchased from a company. Hence, this Customer Sentiment Feedback Analysis Project. Let’s be for real, we’ve all been there: you place an order online for a product you’ve been hyped about, only for it to arrive days (or weeks) after the promised date 📦⌛, or it shows up damaged 💔, or—on the rare lucky days—it actually meets or even exceeds your expectations 🎉. Whatever the case, the objective of this project was simple: show why customer feedback data matters and how the insights from it should guide business decisions moving forward. After all, Customer is King 👑. Before diving into analysis, I cleaned the dataset with Power Query 🧹 and sketched a wireframe in PowerPoint ✍🏽. Then, with the help of Pivot Tables, I pulled out some key insights 🔍. Here are my recommendations: ⚡ Electronics and sports categories generate the highest volume of feedback, showing strong customer engagement in these areas. ⭐ Despite high feedback volumes, customer ratings for products like vacuum cleaners, tennis rackets, and yoga mats sit around 3.63 to 3.69 — reflecting generally positive sentiment. 🎧☕ Some products, like coffee makers and Bluetooth speakers, deliver consistent ratings but still leave room for quality or service improvements. 🗽🌴 Regional differences are clear: states like New York, California, Texas, and Illinois show very diverse sentiment patterns, pointing to the need for tailored strategies. 📉 With an overall product rating of 3.55, customers are generally satisfied, but businesses have plenty of room to boost product quality and overall experience. 👉 My takeaway? Feedback isn’t just noise. It’s free consulting straight from the people who matter most — the ones buying your product 💡🛒. 🔗 You can read the full report on Medium: https://lnkd.in/dzeBpnsF 🔗and also explore more of my projects by checking out my portfolio: https://lnkd.in/dRus6nar I’m also actively open to internship opportunities in data analysis—so if you know of any roles where my skills in Excel, Power BI, SQL, and data storytelling could add value, I’d love to connect.

We're losing 8% of customers annually. That's $4.2M in recurring revenue walking out the door. Your team asks: "What are we doing wrong?" I ask: "What are your customers telling you?" Usually, the answer is: "We send surveys every quarter." That's not Voice of the Customer. That's a survey. Here's what proper VoC actually delivers: EARLY WARNING SYSTEM Multi-channel feedback catches churn signals 60-90 days before customers leave: - Product usage drops (behavioral data) - Support ticket patterns (friction points) - Sentiment shifts (NPS declining, CSAT falling) - Engagement decline (email opens, feature adoption) One client reduced churn 23% by acting on these signals. ROOT CAUSE, NOT SYMPTOMS Cross-functional analysis identifies WHY customers leave: - Is it product gaps? (CPO priority) - Onboarding friction? (COO efficiency issue) - Pricing concerns? (CFO/CRO revenue opportunity) - Poor support experience? (Cost to serve problem) You fix the RIGHT things, not just the LOUD things. CLOSED LOOP = REVENUE RETENTION When customers see you act on their feedback: - Engagement increases 30%+ - Retention improves 15-25% - Expansion revenue grows (satisfied customers buy more) VoC doesn't cost money. It makes money. The difference between survey summaries and strategic VoC: Survey summaries - tell you scores went up or down. No action plan. No predictive signal. Cost: $0. Value: $0. Strategic VoC (4-6 week reporting cadence, continuous insights) - Identifies churn signals 60-90 days early, can reduce churn 15-25%, reduce cost to serve 20-30%, increase customer lifetime value 10-20%. ROI: Typically 3-5X in year one when implemented well. Voice of the Customer isn't a reporting task. It's how you turn customer insight into revenue retention. What's the biggest barrier stopping your organisation from making that shift? #VoiceOfTheCustomer #CustomerExperience #CXStrategy

Recently, I had conversations with many 𝗽𝗿𝗼𝗱𝘂𝗰𝘁 𝗮𝗻𝗱 𝗶𝗻𝗻𝗼𝘃𝗮𝘁𝗶𝗼𝗻 𝗺𝗮𝗻𝗮𝗴𝗲𝗿𝘀 who are really frustrated with how slow and costly traditional market research can be. The waiting times for clear outcomes are ridiculous and the costs can be unreasonably high. Budget constraints have always been a major hurdle, making it challenging for businesses of all sizes to access high-quality and genuine customer insights. In addition, not all companies stay in touch with customers throughout the innovation process, even though it leads to better products. I have always worked to democratize innovation to make it faster, affordable and more effective. One of our most exciting recent developments, which I call 𝗜𝗻𝗻𝗼𝘃𝗮𝘁𝗶𝗼𝗻 𝗼𝗻 𝗦𝘁𝗲𝗿𝗼𝗶𝗱𝘀, is the use of AI-powered video surveys to gather actionable customer insights quickly and affordably. It's a game-changer!  𝗕𝘂𝘁 𝘄𝗵𝗮𝘁 𝗿𝗲𝗮𝗹𝗹𝘆 𝗺𝗮𝗸𝗲𝘀 𝘁𝗵𝗶𝘀 𝗮𝗽𝗽𝗿𝗼𝗮𝗰𝗵 𝘀𝘁𝗮𝗻𝗱 𝗼𝘂𝘁?  1. Unmatched Speed: You can get and analyze customer feedback in days instead of months. Based on them, you can pivot quickly and stay ahead of the competition. In today's fast-paced markets, this level of agility makes a huge difference.   2. Cost-Effective Innovation: Surveys, focus groups, and data analysis can cost a lot of time and money. With this new approach, high-quality insights are now available to everyone - Spending less and gaining more stretch your resources further.  3. Continuous Customer Engagement: Innovation isn’t a one-time event; it’s a continuous process. Now you can keep connected with your customers, gathering ongoing feedback as your products and services evolve - Ensuring your innovations always align with market needs.  𝗛𝗲𝗿𝗲 𝗮𝗿𝗲 𝘁𝘄𝗼 𝗿𝗲𝗮𝗹 𝗲𝘅𝗮𝗺𝗽𝗹𝗲𝘀 𝗼𝗳 𝗵𝗼𝘄 𝘁𝗵𝗶𝘀 𝗮𝗽𝗽𝗿𝗼𝗮𝗰𝗵 𝘄𝗼𝗿𝗸𝘀:   • Cutting Development Time by 75%: A global FMCG company needed to innovate quickly to meet new health trends. We helped them cut their development time from 3 years to 9 months and save 50% - Rapidly engaging their target audience, gathering insights and iterating their product!  • From Data Confusion to Clarity: A European cosmetic brand struggled to make sense of their data. We helped them gain clear, actionable insights that traditional data analysis failed to provide. Making a compelling case for strategic changes, the CEO presented these insights visually to the board! It's not just about faster and cheaper market research—it's about more effective and efficient innovation. Check out this snapshot from our latest research on 𝗗𝘂𝘁𝗰𝗵 𝗴𝗿𝗼𝗰𝗲𝗿𝘆 𝘀𝗵𝗼𝗽𝗽𝗶𝗻𝗴 𝗯𝗲𝗵𝗮𝘃𝗶𝗼𝗿. Soon I'll publish the insights, including the process steps and details on how we did it.   Let’s keep the conversation going: I'd love your feedback! And drop me a message If you're interested in learning more about how this can transform your innovation process.   #Innovation #MarketResearch #CustomerInsights #AIPowered #ProductDevelopment 

Want to know what customers think about your brand and your competitors' brands? In just 15 minutes, you can uncover key insights from data that might be right under your nose with the help of AI. Unsure where to start? Begin with the resources you already have: ► Customer Feedback - Reviews, social media comments, and survey results (e.g., market research, CSAT, NPS) ► Customer Interactions - Transcripts from interviews, calls, and advisory board meeting notes ► Published Content - Case studies, customer quotes in articles ► Other Relevant Data Sources Note: If you're dealing with sensitive information, remember to redact and anonymize it prior to using AI. Here’s a practical example using Asana and its publicly available customer reviews on Capterra: Review the screenshots in the carousel below to see how my team and I used ChatGPT (GPT-4) to extract: ► Asana pros and cons with word clouds plus ideal customer profiles ► Table summarizing previous vendors, reasons for switching, and alternatives considered ► Tables highlighting potential customer inquiries at different funnel stages, value propositions (old way, new way, quantification ideas), and objection handling FAQ This process involved simply copying and pasting customer reviews into a document, uploading a PDF version to ChatGPT, and providing clear instructions on the insights I wanted to extract. The entire process took about 15 minutes and the results are grounded in insights from the customer reviews. Expanding your analysis: While this example focuses on reviews, consider analyzing other types of customer data to gain a comprehensive understanding of your customers and competitors. Remember: The quality and diversity of the data you provide will directly impact the insights generated. To ensure a well-rounded understanding, consider using a wide range of data sources that cover different aspects of the customer experience. AI can quickly help you start, but it’s just the beginning. Human oversight is crucial to guide, challenge, double-check, and collaborate with AI for the best results. After reviewing the AI-generated insights, you might identify gaps or areas needing deeper analysis, which you can then explore further. Have you used AI to gain customer or competitive insights? What worked and what didn’t? Share your experiences and tips in the comments below. For collaboration on inspiring your teams with applied AI in sales and marketing, feel free to reach out to me, Tahnee Perry, or Daniel O'Neill. #CustomerInsights #CustomerReviews #CompetitiveAnalysis #AIAnalytics #ResponsibleAI #GrowthPathPartners

After we launched Inari (YC S23) a few weeks back, we were surprised to hear over and over from PMs and designers that their biggest pain was actually how time consuming pulling out insights from customer feedback data is. So we did a little hackathon last week and are now releasing an AI-powered customer insights engine! You can use this tool to understand what’s on your customer’s minds, figure out which themes will boost engagement and retention, then prioritize your roadmaps. Here’s how it works: 1. We handle the annoying “data plumbing” - connect your customer feedback data sources, CSVs, or even drop in long docs/PDFs from your customer interviews. We’ll extract the key datapoints from these data sources to be analyzed. 2. We use LLMs and other models to sift through each piece of feedback - summarizing themes, sentiment, feature requests, bugs or defects, and praises. If it’s a long piece of feedback like a customer interview, we’ll chunk the doc and pull out the important highlights. Teams can adjust the categorization heuristics/prompts themselves as needed. 3. We add some basic analytics and workflows on top of the processed customer feedback data so it’s easy to understand key themes, monitor changes on different time series, and filter based on which team, type, source, or date the user wants to look at. If any product, design, support, or other teams want an easy way to pull out customer themes, requests, quotes, and other insights for planning, triaging requests, and other use cases - let us know and we’d love to get this live for you (frank@useinari.com)!

Your customers have already told you why they’re buying...or why they’re not. You’re just not using the data. Most brands are sitting on a goldmine of customer feedback...but instead of using it to drive more sales, they’re out here throwing money at ads and guessing what customers want. Buried inside your reviews, emails, and order history are patterns that show you exactly: Why people hesitate before buying What product tweaks could increase conversions Which products are frequently bought together (aka, easy upsell opportunities) Instead of guessing, here’s how to use AI to extract these insights and turn them into more revenue: Step 1: Collect Customer Feedback & Purchase Data Export customer reviews (Shopify, Trustpilot, Amazon, etc.) Pull support emails & live chat transcripts (people ask what they’re unsure about) Look at order data—what products are customers buying together? Step 2: Plug This Into ChatGPT (Along with your customer feedback!) Prompt: "Analyze this customer feedback and purchase data for recurring objections, buying patterns, and conversion barriers. Identify common concerns stopping people from purchasing, trends in frequently bought products, and messaging gaps that could increase sales." Step 3: Look for Patterns & Take Action 🚀 If people hesitate because of price → Your value messaging isn’t strong enough. 🚀 If customers buy one product and come back for another → Create an upsell or bundle it upfront. 🚀 If customers keep asking the same pre-purchase questions → Your product descriptions need work. 🚀 If certain products are always bought together → Feature them as “frequently bought together” or offer a bundle. This takes 10 minutes...but it’s the difference between guessing and scaling smarter. Your customers are already telling you how to sell to them. AI just helps you see it faster. Try this and let me know what insights you uncover. Bet you’ll find something you weren’t expecting.

Surveys are great for growth optimization. But what about the 95% who never fill them out? They're leaving reviews everywhere - Reddit, Amazon, Trustpilot. This prompt finds them ALL and shows you exactly what's blocking growth. Your best research is already written 👀 -------- Prompt: "I want you to conduct a comprehensive review mining analysis for [BRAND NAME] [BRAND URL/PRODUCT]. Please follow these steps: 1. INITIAL RESEARCH: - Use web search, Reddit search, Amazon reviews, and any available review platforms - Search for: "[brand] reviews", "[brand] complaints", "[brand] customer service", "[brand] Reddit" - Look for recent reviews (last 6-12 months) and overall patterns - Find both positive and negative feedback - Get actual customer quotes and specific examples - 2. CREATE A REVIEW MINING SUMMARY with these sections: ## What People LOVE About [Brand]: - List main positive themes with specific customer quotes - Include citations for all claims - Rank by frequency of mention - Note specific benefits users report - ## What People DON'T Like: - List main complaints with specific examples and quotes - Focus on: customer service issues, subscription problems, product quality, pricing concerns, transparency issues - Include severity and frequency of complaints - Note any business practice concerns - ## Mixed Reviews On: - Features with divided opinions and why - ## Overall Sentiment: - Star ratings across platforms - General reception summary - Key takeaways - 3. ENHANCE WITH CUSTOMER PERSONAS: - ## Customer Personas & Their Experiences Create 5-6 distinct personas based on the reviews, including: ### [Persona Name] (Age range) Quote Examples: [Real quotes representing this persona] What They LOVE: [Specific benefits valued by this persona] What They HATE: [Specific pain points for this persona] Include sections for: - Most Satisfied Customer Types - Most Dissatisfied Customer Types - Common Threads Across All Personas - IMPORTANT REQUIREMENTS: - Use exact customer quotes whenever possible - Cite all sources - Look for red flags: subscription issues, hidden fees, poor customer service, lack of transparency - Note positive patterns: specific benefits, value propositions, success stories - Include dates/recency of reviews when relevant - Provide platform sources (Reddit, Amazon, Trustpilot, etc.) - Bold key insights - Use bullet points for easy scanning - The goal is to provide a complete picture of customer sentiment that would help someone make an informed decision about this brand, understanding both what works well and what problems they might encounter."