Case Studies On Customer Experience Excellence

The PMs who win in the next wave won't be the ones who figured out how to prompt to build. They'll be the ones who figured out how to run 10x the customer learning with the same team. Here's why that matters right now. AI has handed engineering teams a jetpack. Cursor. Codex CLI. Claude Code. The delivery side of product development — build, specify, launch — is being automated at a breathtaking pace. But as Andrew Ng recently pointed out, the real bottleneck today isn't coding. It's discovery. While everyone raced to accelerate shipping, the question mark moved upstream. We now have the ability to build faster than we've ever been able to learn. And building fast on the wrong insight isn't speed — it's just expensive mistakes, sooner. The good news: the same AI revolution is quietly making discovery dramatically more powerful too. A few of the emerging use cases: 1️⃣ Analyzing feedback at scale. What used to require a researcher and two weeks can now be done by a PM in an afternoon — feeding thousands of NPS verbatims, support tickets, or app reviews into an AI and getting back a structured synthesis of themes, patterns, and verbatim quotes. 2️⃣ Automating feedback rivers. Tools like Reforge Insights, Enterpret, and Kraftful now continuously monitor customer feedback across every channel and surface actionable signals without anyone having to manually triage. 3️⃣ AI-moderated user interviews. Platforms like Reforge and Listen Labs are making it possible to run interviews at a scale that was never feasible with human moderators — turning what used to be 10 interviews into 100. 4️⃣ Discovery via prototypes. With vibe-coding tools like Lovable, v0, and Bolt, PMs can now build functional prototypes and gather real behavioral data — heatmaps, drop-offs, in-product surveys — before a single line of production code is written. 5️⃣ Natural language metric analysis. Ask your database a plain-English question, get a chart back. No SQL. No waiting for a data analyst. The feedback loop between a hypothesis and an answer just collapsed from days to minutes. The teams that wire these workflows together won't just be better informed. They'll develop a sharper product intuition — the kind that David Lieb (Founder of Google Photos, Partner at YC) described as "the world's most sophisticated machine learning model ever created." Join me Thursday, March 5th at the Lean Product Meetup with Dan Olsen in Mountain View, CA where I'll be sharing the exact 10 AI discovery workflows I now rely on to help me decide what's worth building faster 👉 https://lnkd.in/gfrJVsd3

𝗚𝗿𝗲𝗮𝘁 𝗰𝘂𝘀𝘁𝗼𝗺𝗲𝗿 𝘀𝗲𝗿𝘃𝗶𝗰𝗲 𝗶𝘀𝗻’𝘁 𝗱𝗲𝗮𝗱. 𝗜𝘁’𝘀 𝗯𝗲𝗶𝗻𝗴 𝘁𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗲𝗱 𝗯𝘆 𝗮𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜. Customer service is often seen as a cost center. In reality, it’s a growth engine waiting to be activated. When trust compounds, referrals multiply - and that’s what separates brands that scale from those that stall. With agentic AI, you can leverage service as a growth lever and intelligence as an engine for scale. 𝗪𝗵𝗲𝗻 𝗶𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗲𝗱 𝘄𝗶𝘁𝗵 𝗶𝗻𝘁𝗲𝗻𝘁𝗶𝗼𝗻, 𝗮𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 𝗱𝗲𝗹𝗶𝘃𝗲𝗿𝘀 𝗺𝗲𝗮𝘀𝘂𝗿𝗮𝗯𝗹𝗲 𝗥𝗢𝗜 𝗶𝗻 𝗰𝘂𝘀𝘁𝗼𝗺𝗲𝗿 𝘀𝗲𝗿𝘃𝗶𝗰𝗲, 𝗹𝗶𝗸𝗲: ⬇️ → 𝗖𝘂𝘀𝘁𝗼𝗺𝗲𝗿 𝗹𝗼𝘆𝗮𝗹𝘁𝘆 with best-in-class experience, starting with faster, more accurate resolutions → 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗲𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆 via automating and streamlining workflows → 𝗣𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝘃𝗶𝘁𝘆 by freeing human agents for complex work → 𝗥𝗲𝘃𝗲𝗻𝘂𝗲 𝗴𝗿𝗼𝘄𝘁𝗵 through advocacy and repeat business On the flip side, if done wrong? It backfires - creating inefficiencies, frustrating customers, and eroding trust. 𝗦𝗼, 𝘄𝗵𝗮𝘁’𝘀 𝗸𝗶𝗹𝗹𝗶𝗻𝗴 𝘆𝗼𝘂𝗿 𝗮𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 𝗥𝗢𝗜: ⬇️ 1. 𝗦𝗶𝗹𝗼𝗲𝗱 𝗱𝗮𝘁𝗮 & 𝗱𝗶𝘀𝗰𝗼𝗻𝗻𝗲𝗰𝘁𝗲𝗱 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀 2. 𝗨𝗻𝗱𝗲𝗿𝘁𝗿𝗮𝗶𝗻𝗲𝗱 𝗺𝗼𝗱𝗲𝗹𝘀 & 𝗼𝘃𝗲𝗿𝗵𝘆𝗽𝗲𝗱 𝗲𝘅𝗽𝗲𝗰𝘁𝗮𝘁𝗶𝗼𝗻𝘀  3. 𝗡𝗼 𝗵𝘂𝗺𝗮𝗻-𝗶𝗻-𝘁𝗵𝗲-𝗹𝗼𝗼𝗽 𝘀𝘁𝗿𝗮𝘁𝗲𝗴𝘆 𝗔𝘁 IBM, 𝘄𝗲 𝗯𝗲𝗹𝗶𝗲𝘃𝗲 𝘁𝗵𝗲𝗿𝗲 𝗮𝗿𝗲 𝗳𝗶𝘃𝗲 𝗸𝗲𝘆 𝘀𝘁𝗲𝗽𝘀 𝘁𝗼 transforming customer service: ⬇️ ✅ Pick the right platform to build, deploy, and scale AI agents. ✅ Start with specific use cases tied to KPIs. ✅ Build on a unified data foundation. ✅ Integrate human oversight. ✅ Govern and orchestrate at scale. 📥 To learn more, download our agentic AI Playbook for customer service: https://lnkd.in/dtPU8rFv 2026 is about embedding intelligence into every interaction, finally moving beyond experimenting.

I'm excited to announce the launch of AI Refinery for Sovereign and Agentic AI, a groundbreaking platform that deepens our partnership with NVIDIA. This first-of-its-kind platform champions data sovereignty and operational resilience through physical AI, paving the way for enhanced competitiveness in the journey toward agentic AI.   As I've mentioned before, I firmly believe that AI presents a unique opportunity for Europe to reinvent its economy, drive productivity, resilience, and competitiveness, and support future growth. I'm incredibly proud to see the momentum our clients are gaining, including Public Power Corporation, Roche, Kion Group, Noli, and Nestlé.   Nestlé, for instance, is launching a new AI-powered in-house service that will generate high-quality product content at scale for eCommerce and digital media channels. This initiative exemplifies the transformative potential of AI in driving business efficiency and innovation. The expansion of our AI Refinery platform is particularly significant for European organizations, enabling them to accelerate the deployment of AI agents while addressing their sovereignty concerns. This is especially crucial for the public sector and critical infrastructure industries, such as energy, telecommunications, and defense.   We continue to support our clients in maintaining control over their critical data and leveraging innovative AI solutions through this expanded AI Refinery platform. More details here: https://lnkd.in/dvekqfB6 #Noli #Nestle #PublicPowerCorporation #KionGroup #Roche #AgenticAI #AI #Accenture

Every company says they listen to customers. But most just hear them. There's a difference. After spending years building feedback loops, here's what I've learned: Feedback isn't about collecting data. It's about creating change. Most companies fail at feedback because: - They send random surveys - They collect scattered feedback - They store insights in silos - They never close the loop The result? Frustrated customers. Missed opportunities. Lost revenue. Here's how to build real feedback loops: 1. Gather feedback intelligently - NPS isn't enough - CSAT tells half the story - One channel never works Instead: - Run targeted post-interaction surveys - Conduct deep-dive customer interviews - Analyze product usage patterns - Monitor support conversations - Build customer advisory boards - Track social mentions 2. Create a single source of truth - Consolidate feedback from everywhere - Tag and categorize insights - Track trends over time - Make it accessible to everyone 3. Turn feedback into action - Prioritize based on impact - Align with business goals - Create clear ownership - Set implementation timelines But here's the most important part: Close the loop. When customers give feedback: - Acknowledge it immediately - Update them on progress - Show them implemented changes - Demonstrate their impact The biggest mistakes I see: Feedback Overload: - Collecting too much data - No clear action plan - Analysis paralysis Biased Collection: - Listening to the loudest voices - Ignoring silent majority - Over-indexing on complaints Slow Response: - Taking months to act - No progress updates - Lost customer trust Remember: Good feedback loops aren't about tools. They're about trust. Every piece of feedback is a customer saying: "I care enough to help you improve." Don't waste that trust. The best companies don't just collect feedback. They turn it into visible change. They show customers their voice matters. They build trust through action. Start small: 1. Pick one feedback channel 2. Create a clear process 3. Act quickly on insights 4. Show results 5. Scale what works Your customers are talking. Are you really listening? More importantly, are you acting? What's your approach to customer feedback? How do you close the loop? ------------------ ▶️ Want to see more content like this and also connect with other CS & SaaS enthusiasts? You should join Tidbits. We do short round-ups a few times a week to help you learn what it takes to be a top-notch customer success professional. Join 1999+ community members! 💥 [link in the comments section]

“Let me explain the issue again…I was saying…” Does this sound familiar? We’ve all been there: stuck on the phone or chat, explaining the same problem to a new support agent for the third, fourth, or fifth time, feeling unheard. But customer service isn’t just about solving problems. It’s about making people feel heard. Yet, far too often, support interactions feel robotic, cold, and disconnected. You’re bounced between departments. Asked to repeat yourself again and again. Given a ticket number instead of a real solution. And the worst part? No one seems to remember your last conversation. This isn’t just inefficient; it’s deeply frustrating and exhausting, and it shows a lack of empathy. Customer service must go beyond transactions. It should tap into attentive empathy, truly listening to customers, acknowledging their frustrations and cognitive empathy, and offering relevant solutions based on past interactions and emotional context. So how do we do that at scale? OpenAI’s latest update is a step in that direction. ChatGPT can now remember past conversations across sessions. This simple upgrade unlocks a smarter, more empathetic future for customer service. Imagine this: • Your support agent already knows what you’ve been through • They pick up right where you left off • They tailor responses to your preferences and pain points This is what modern, emotionally intelligent service should feel like. And the data speaks volumes: 🔹 76% of customers say repeating themselves is their #1 frustration 🔹 81% prefer brands that personalize the experience With AI memory in play, customer service teams can now: • Offer personalized support journeys • Reduce friction in every interaction • Proactively engage based on past pain points • Build long-term trust through seamless continuity For businesses, this means: • Smarter, AI-powered systems that improve with every touchpoint • Consistent journeys that feel human even when powered by machines • Stronger retention through empathy-led engagement If you’re a forward-thinking company, here’s what to do: • Invest in AI tools with conversational memory • Redesign support flows to feel continuous, not fragmented • Train agents to collaborate with AI as empathy amplifiers • Prioritize data transparency and privacy to build lasting trust Because when customers feel understood, they don’t just stay, they advocate. #AI #ChatGPT #customerexperience #CX #KSA #SaudiArabia

That’s the thing about feedback—you can’t just ask for it once and call it a day. I learned this the hard way. Early on, I’d send out surveys after product launches, thinking I was doing enough. But here’s what happened: responses trickled in, and the insights felt either outdated or too general by the time we acted on them. It hit me: feedback isn’t a one-time event—it’s an ongoing process, and that’s where feedback loops come into play. A feedback loop is a system where you consistently collect, analyze, and act on customer insights. It’s not just about gathering input but creating an ongoing dialogue that shapes your product, service, or messaging architecture in real-time. When done right, feedback loops build emotional resonance with your audience. They show customers you’re not just listening—you’re evolving based on what they need. How can you build effective feedback loops? → Embed feedback opportunities into the customer journey: Don’t wait until the end of a cycle to ask for input. Include feedback points within key moments—like after onboarding, post-purchase, or following customer support interactions. These micro-moments keep the loop alive and relevant. → Leverage multiple channels for input: People share feedback differently. Use a mix of surveys, live chat, community polls, and social media listening to capture diverse perspectives. This enriches your feedback loop with varied insights. → Automate small, actionable nudges: Implement automated follow-ups asking users to rate their experience or suggest improvements. This not only gathers real-time data but also fosters a culture of continuous improvement. But here’s the challenge—feedback loops can easily become overwhelming. When you’re swimming in data, it’s tough to decide what to act on, and there’s always the risk of analysis paralysis. Here’s how you manage it: → Define the building blocks of useful feedback: Prioritize feedback that aligns with your brand’s goals or messaging architecture. Not every suggestion needs action—focus on trends that impact customer experience or growth. → Close the loop publicly: When customers see their input being acted upon, they feel heard. Announce product improvements or service changes driven by customer feedback. It builds trust and strengthens emotional resonance. → Involve your team in the loop: Feedback isn’t just for customer support or marketing—it’s a company-wide asset. Use feedback loops to align cross-functional teams, ensuring insights flow seamlessly between product, marketing, and operations. When feedback becomes a living system, it shifts from being a reactive task to a proactive strategy. It’s not just about gathering opinions—it’s about creating a continuous conversation that shapes your brand in real-time. And as we’ve learned, that’s where real value lies—building something dynamic, adaptive, and truly connected to your audience. #storytelling #marketing #customermarketing

I had a blast talking with Blake Morgan about Replicant's journey and I am really glad it ended up being one of her most popular interviews. Here are my key points from this discussion: BLUF: AI is transforming customer service, and companies that embrace it will reap the rewards. I believe in a human-centric approach, where AI empowers agents to provide better service and create a more positive customer experience. 💡 On the current state of AI in customer service: We're still in the early stages, but AI is already making a significant impact. Early chatbots were rudimentary, but technology has advanced significantly. Replicant can now automate complex tasks, like food orders for DoorDash or roadside service for AAA. 💡 On the benefits of AI in customer service: AI can improve customer satisfaction, reduce costs, and increase efficiency. It can also improve agent morale by freeing them from repetitive tasks. Companies using Replicant have seen significant increases in NPS and cost savings. 💡 On the future of AI in customer service: AI will play an even greater role, automating more complex tasks. It will not replace human agents but allow them to focus on more complex and rewarding tasks. This will lead to better-trained, higher-paid agents who are more satisfied with their jobs. 💡 On how to implement AI in customer service: Companies should start small by automating one or two simple tasks. They should then track the results and make adjustments as needed. It's essential to move quickly and iterate as technology constantly evolves. 💡 On the importance of a human-centric approach: Companies should not forget the importance of human interaction in customer service. AI should be used to augment human agents, not replace them. Companies should focus on creating a culture of customer service excellence. 💡 On Replicant's new product, Conversation Intelligence: It leverages the latest in large language models to analyze customer conversations. It can be used for next-generation QA, providing agents with real-time feedback. It can also be used to unlock business insights and identify areas for improvement. 💡 On the importance of starting now: Companies that are not already using AI in customer service should start now. The best time to plant a tree was 20 years ago. The second best time is now.

AI in customer service isn’t the future. It’s already delivering ROI — at scale. 📍Kapiva, a health D2C brand in India, just showed how it’s done 👇 Their goal? Turn purchase into loyalty — not just a one-time transaction. The problem? Customer support tickets piling up, delayed responses, and low retention. Enter: AI Chatbots (powered by LimeChat) Here’s what changed: ✅ 70K+ tickets/month handled by AI ✅ 90%+ efficiency boost via automation ✅ 4+ CSAT maintained (without burning out agents) ✅ Seamless escalation to humans when needed ✅ Query resolution that feels human, not robotic This isn’t just cost-saving. It’s experience-enhancing. Because let’s be real — if your CX sucks, your LTV will too. AI isn’t here to replace support teams. It’s here to free them up for the stuff that actually needs a human touch. And D2C brands that get this? They’re winning. Have you seen similar CX automation in action? #D2C #CustomerExperience #AI #MarketingAutomation #Growth

What makes customers stay? Great products, yes — but even better service. One of my favorite stories about this comes from early Amazon. In the late '90s, Amazon was growing fast. During a weekly leadership meeting, the head of customer service proudly shared: “Average phone wait time: 59 seconds.” Job well done, right? But Jeff Bezos wasn’t convinced. He had been reading direct customer emails. Many were unhappy. The data looked fine — but something didn’t feel right. So, in the middle of that meeting, Bezos picked up the phone and called Amazon’s own customer support line. On speaker. In front of everyone. 1 minute passed. Then 2. Then 5. Still nothing. 10 minutes later, there was still no answer. Turns out, the metric was only tracking wait time for answered calls — not the ones that never got picked up. The team was optimizing for the wrong thing. That single call changed Amazon’s entire approach. They rebuilt customer service from the ground up. And it became one of the pillars of their dominance. As Bezos later said: 👉 “When the data and the anecdotes disagree, the anecdotes are usually right.” Too often we chase metrics. But it’s how we listen to customers — and act — that drives true retention and lifetime value. In every business I’m part of, I’ve seen this hold true: Customer service isn’t a support function. It’s a growth engine. What’s your take? Have you seen data tell one story, but customer feedback tell another?

IKEA deployed an AI chatbot called Billie. It handled 47% of customer service queries automatically and saved EUR 13 million. That's a decent result on its own. But it's not the interesting part. The interesting part is what the chatbot couldn't do. When they analysed the 53% of queries Billie failed on, a key insight emerged. Customers weren't just asking about delivery status or opening hours. They were asking for help designing their homes. Room layouts. Furniture combinations. Storage solutions. That demand had been invisible for years. Call centre agents were too busy handling routine queries to notice it. So instead of cutting 8,500 call centre jobs, IKEA retrained those workers as remote interior design advisors. Video consultations, floorplans, 3D room visuals. The remote design channel now generates EUR 1.3 billion a year. Target is 10% of total revenue by 2028. Three things worth noting: - Customer service went from a cost line to a revenue channel - Redeployment of people, not displacement of people due to AI - The real value came from analysing the data and finding an innovative solution hiding in it Most AI-in-customer-service stories end with headcount cuts. This one ends with 8,500 people doing better jobs AND creating additional customer value. Full case study in the comments.