Using AI for Customer Insights

What if you could listen to every customer interaction—at scale? For years, contact center leaders have struggled with limited visibility. Most QA teams review only 2-5% of calls, leaving critical insights buried in recordings that never see the light of day. AI-powered Conversation Intelligence changes that. Instead of relying on outdated keyword spotting or manually scoring a fraction of interactions, AI can analyze 100% of your customer conversations, extracting call drivers, sentiment trends, and agent performance insights in real time. Imagine what you could do with that level of clarity. Identify trends before they become problems—spot surges in customer complaints and act before they escalate. Coach agents with precision—understand exactly where improvements are needed, without listening to hours of calls. Optimize automation strategies—pinpoint high-volume, repetitive workflows that are ripe for AI-driven automation. When every conversation becomes a source of insight, your contact center stops flying blind and starts making proactive, data-driven decisions. How would that change your CX strategy?

I never thought anonymous chatbot chats could rewrite an SME's sales playbook—until I saw it happen in Liechtenstein. This regional producer of specialty retail products struggled to understand their customers. Expensive customer research? Out of reach. Complicated products meant lost sales in their webshop. That's when we built a simple AI chatbot to guide buyers. It wasn't fancy. Just helpful. Running on N8N for privacy – safe server. We evaluated anonymized conversations. Patterns emerged fast. Common queries revealed unmet needs – like finding the right product fast. One finding: Many asked about sustainable product features. This triggered action. First, a revamped Q&A doc for the site. Clearer answers cut bounce rates. Then, input for social media strategies. Posts now addressed those exact pain points. Engagement spiked 30%. Product development? Insights sparked a new line extension covering those needs. No more guessing customer wants. AI turned chats into knowledge gold. Research shows this works across Europe. A 2025 study on AI in SME marketing highlights chatbots for customer insights, boosting creativity and personalization: https://lnkd.in/djvP57tM Another on AI adoption dynamics notes knowledge management gains for small firms: https://lnkd.in/dTMQX4Pf And MDPI's review details AI's role in customer functions for SMEs: https://lnkd.in/dFCGGN7c Your takeaway: Start learning more about your customers with AI today. It's affordable, ethical, and transformative. What's one customer question that's stumped your team? Share below—let's brainstorm. ♻️ Repost to help your network achieve success. And follow Hartmut Hübner, PhD for more. #AI #SMEs #Customers #Innovation #Growth

Annual customer surveys are dead. Here's what replaced them: AI is rewriting the rules of customer understanding. The days when insights came only from annual surveys and scheduled interviews are gone. Tomorrow's standard is "always-on", tapping into real-time user signals and feedback 24/7. At Voi Technology, we process tens of thousands of ride feedback messages every week. Before: Simply too much data to do anything meaningful with. Now: We use AI to surface emerging trends, spot pain points, and trigger immediate actions. If feedback indicates a safety risk, AI creates a quality check task instantly. Whether adjusting scooter availability or flagging maintenance issues before they escalate, our AI-driven insights keep us ahead of rider needs. The result: We are becoming the company we want to be. One that listens to every customer, adapts instantly and delights consistently. If you are still relying on periodic check-ins to understand users, it is time to level up. Make AI-powered, always-on feedback your baseline. Your customers never stop talking, so why should you stop listening?

I was tired of guessing, and being wrong. Here's how I'm using AI to build customer health scores. As someone who's used Customer Success software for over 10 years and works with companies to design their health scores, I can tell you, this has always been a challenge. Most folks were working off assumptions, copying what others had done, or over-engineering scores thinking more inputs meant more accuracy. We’ve all seen it: ✅ Green customers churn ❌ Red customers renew And every time, we scratch our heads and ask ourselves, what are we getting wrong? This doesn't make sense. AI can give us the answer. It allows us to look at everything ... who our customers are, how they behave, what they need, and what they actually do. And from that, we can build truly intelligent profiles of health. No more guessing. Here’s a 5-step process that I used to redefine health: 1️⃣ Redefine your segments Move beyond spend-based segmentation. Segment by journey stage, product use case, or engagement pattern to get more meaningful insights. 2️⃣ Enrich your data Pull together all available data, product usage, support interactions, sentiment signals, firmographics, and demographics. The richer the picture, the better the model. 3️⃣ Label your historical outcomes Identify which customers renewed, expanded, or churned over the past 12–24 months. These become your training labels. 4️⃣ Run AI modeling Use AI to analyze patterns across your segments and outcomes. Prompt it to define health indicators tied to success and risk. 5️⃣ Operationalize in real time Build the model into your workflow. Let it learn and adapt as new data comes in so your health score always reflects what’s actually happening, not what you assumed. The goal isn’t to be perfect. The goal is to be accurate enough to act with confidence. Bonus: Loop in your CS teams to validate and pressure test the output. They’ll help refine the model and drive adoption. What’s powering your health score today ... insights or assumptions?

On paper customer success is a great use case for GenAI, but this post does a good job articulating why the solution needs to be highly customized to every business and a purely horizontal solution might not be very useful. 5-Step Framework for AI-Powered Customer Insight Instead of the "dump and hope" approach, here's a systematic framework that's worked for me and many organizations I've consulted with: 1. Start with a problem hypothesis. 2. Build a taxonomy that maps to action. 3. Count what matters. 4. Analyze temporal trends. 5. Look for what’s missing For example, if churn is high after the first week, your hypothesis might be: "Users aren’t finding value fast enough." Now you can use AI to test that. You're no longer asking the model to "summarize feedback" — you're asking it to find signal related to that specific hypothesis. You’re guiding the model’s attention. Too often I see taxonomies that are intellectually clean but practically useless. They categorize feedback into buckets like "usability," "performance," or "pricing". Nice in theory. But where do you go from there? Instead, design categories that align with things you can actually change. For example: "Signup friction (UI issues)" "Signup friction (copy confusion)" "Lack of onboarding guidance" "Missing core feature expectations" "Breakdowns in customer support loop" Each label should suggest who needs to act and what they need to look at. If a tag doesn’t help someone make a decision or prioritize work, it doesn’t belong.

Your Customers Are Speaking...Are You Really Listening? Yesterday, I was asked in a LinkedIn Direct Message..."How do we access all of our customer data going back years without buying expensive research reports from 3rd party companies?" Easy... You Already Own the Data. AI Makes It Work for You. 1. Every Conversation, Every Source...Unified & Analyzed Imagine pulling in data from: (Past, Present & Future) - Emails - Live Chats & Chatbots - Phone & Video Call Transcripts - Social Media & Reviews AI-powered tools like NLP and Sentiment Analysis can scan through years of data, identifying patterns, trends, and critical insights that would have been missed otherwise. What if 40% of lost deals stem from the same unaddressed objection your reps keep missing? AI will surface it. 2. Unlocking the WHY...Decoding Customer Intent with AI - Detecting buying signals. - Identifying friction points. - Uncovering emotional triggers. Example: AI scans thousands of chat transcripts and finds that customers hesitate during pricing discussions 70% of the time. The solution? Optimize pricing explanations and offer preemptive value justification. 3. Transforming Insights Into Revenue & Retention - Sales Playbook Optimization. - Real-Time Rep Coaching. - Automated Response Recommendations. - CX Personalization. A 10% increase in CX intelligence can drive a 25% increase in revenue. The insights already exist...you have to access them. 4. AI-Powered Feedback Loops: The Self-Improving System With AI, your customer insights don’t just sit in reports...they fuel a self-optimizing feedback engine: - AI surfaces new trends in customer concerns and objections. - Sales & CX teams adjust messaging, training, and strategy. - AI monitors impact, learns from results and refines its recommendations. - Repeat. Improve. Win. YOU HAVE THE DATA...Turn it into gold! Workplace AI #businessintelligence #data #customerexperience #sales #ceo

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.

A few months ago, we doubled our website conversion rate at MadKudu (yes, doubled). Rather than doing A/B testing or traditional user research, we used Gong’s revenue AI to analyze over 1,000 recent calls. We asked Gong which pain points were mentioned the most, and what exact words prospects used to describe those. We used the insights to update our website and overall messaging. The results were amazing. Three takeaways from our experience: 1️⃣ AI is unlocking a completely new level of customer insights (something unavailable just a year ago). 2️⃣ This can fundamentally change how we align our teams (even product). 3️⃣ This next level of customer-centricity can drive business results almost immediately. More on this in this post for The Edge: https://lnkd.in/dhdUamS5 Have you tried something similar? Any experience to share?

"Learning to walk again, I believe I've waited long enough"  🎤 "Walk" by Foo Fighters Had a fascinating conversation with a group of CS leaders last week about AI. The dialogue reminded me of how we learn to ride a bike - wobbly at first, but gradually our brain forms new patterns until it becomes second nature. AI learns similarly, and it's transforming how we think about #CustomerSuccess. Here's what's blowing my mind: 🔎 Pattern Recognition: Just like how great CSMs spot customer health issues before they become problems, AI is identifying patterns humans miss. At Gainsight, we recently saw this firsthand when Staircase AI detected brewing sentiment issues in email threads that weren't even copied to our CS team. It caught subtle tone changes that signaled future churn risk. 🎯 Learning from Mistakes: Remember your first customer call? AI also improves through trial and error. One thing we've learned from implementing Staircase is that relationship patterns often hide in unexpected places - casual Slack messages sometimes reveal more about customer health than formal QBRs. 🌱 Unexpected Discoveries: The most exciting part? AI is finding patterns we never knew existed. Last week, our system identified a customer at risk not from negative sentiment, but from a sudden shift to overly formal communication - a pattern that often precedes vendor reevaluation. 🤝 Human + Machine Partnership: The future isn't about AI vs humans. It's about how we work together. Our best CSMs are using AI to analyze thousands of customer interactions instantly, freeing them to focus on building deeper relationships. One CSM told me last week: "AI handles the patterns, I handle the people."But here's what keeps me up at night: Are we moving fast enough? While we debate whether to embrace AI, our customers are already experiencing AI-powered experiences everywhere else. What unexpected patterns has AI helped you discover in your customer relationships?

Voice data holds the key to better customer experiences. Every day, businesses have thousands of conversations with customers. Inside those interactions are insights that can strengthen relationships, improve service, and drive better outcomes. The challenge has always been finding a way to access that data without breaking the bank. Now, AI is making that possible. Businesses of all sizes can use AI to analyze voice data to spot trends, understand customer needs more clearly, and make decisions that create a real impact. From improving response times to personalizing interactions and identifying common pain points, this data provides a roadmap for meaningful change. The answers CX teams need are already in the conversations they’re having. Now is the time to start putting them to work.