Implementing Voice of Customer Programs

Missed calls lead to customer frustration and lost revenue. Voice AI changes this. With voice AI, these missed opportunities can be transformed  into meaningful conversations that build loyalty and drive growth. On the latest The Modern Customer Podcast, I had a conversation with Carson Hostetter, EVP & GM of AI + CX at RingCentral, about how companies are using voice AI to: ✅ Replace outdated IVR menus with human-like dialogue ✅ Protect revenue by ensuring no call is missed ✅ Turn conversations into proactive customer service and business growth Carson knows the stakes. As RingCentral’s former CRO, he helped grow ARR from $1.4B to $2.4B and scaled its enterprise business from $5M to $500M. Now, he’s applying that same growth mindset to AI. 👉 Listen to the full episode and discover how companies are already turning calls into ROI with voice AI. This episode of The Modern Customer Podcast is sponsored by RingCentral.

Customer service conversations are the heartbeat of your business. They are a treasure trove of data about your operation and product flows, your agents and how they treat your customers, and your customers' preferences and needs. Yet, most contact centers analyze only a fraction of these interactions, using dated technology, leaving valuable insights untapped and decisions driven by incomplete data. At Replicant, we believe it’s time to bring every conversation to light. That’s why Conversation Intelligence is transforming customer service conversations into actionable insights. By analyzing 100% of calls with the latest audio AI, leaders can identify operational issues that lead to unnecessary calls, optimize agent performance, and pinpoint automation opportunities—turning their contact centers into strategic assets. For example, a large e-commerce provider used Conversation Intelligence to uncover an issue impacting 5% of their calls. Within one week, they implemented a fix that redefined their customer service strategy, eliminating inefficiencies and elevating their customer experience. This isn’t just about solving problems; it’s about leading with clarity. When every customer conversation becomes a data point for innovation, and AI summarizes it into actions for you, your contact center becomes a competitive advantage. The future belongs to leaders who anticipate, innovate, and act boldly. Are you ready to lead the way?

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]

Your customers are still calling.... With all the talk of AI and Agentic, we sometimes forget that voice is "OG" tech. Voice is where urgency and emotion show up. When real people feel real pressure, they need to speak. They want quick answers and a human safety net when it's needed. That moment will define your brand. I'll share two examples from a service/insurance company I came across recently that made voice the linchpin in the journey. A national brand had long waits and repeat calls. They turned on speech recognition and live guidance for agents. The system heard the reason for the call, pulled the right steps, and wrote a short summary for a handoff. Waits fell, repeat explanations dropped, and agents sounded confident. Another team at the same company used the same pattern for warranty claims. The caller read the model number, the system filled the form, and the agent confirmed it. Faster for everyone, fewer errors, less stress. You can pick one case that shows up every day at your company. You likely don't even have to send your customers a survey to figure out which ones are important. Ask any business line leader, and they can rattle off 5 of them right away. Here's how... Look at status checks, appointment moves, warranty lookups, or simple plan changes all work. Write the happy path in plain English, then three natural variations. Include one confused version and one upset version. Turn on live transcription for that case. You can use your current CCaaS tool if it supports it, or a basic speech-to-text service plus a rules layer. Put one screen in front of the agent. Show the transcript, the detected reason, and the next two steps. No extra tabs. Track three items: 1) Time on call 2) Call rate within seven days. 3) Agent confidence scored by a one-question survey that asks, “Did you feel you had what you needed to resolve this call?” with a simple yes or no. Keep a human escape. If the caller says, “I want a person,” route to the right queue and pass the transcript so they do not repeat themselves. Run this for two weeks, compare the three metrics, and decide to expand or fix. If results are flat, improve the phrases you detect and the agent screen before you add new cases. What you actually need to do this: A live transcription tool, one light intent layer to detect the reason for the call, and one simple agent desktop that shows steps. You do not need a massive platform to test this. You need a small pilot that proves value without new headcount. If you want a one-page starter kit that teams are using to launch this pilot in under two weeks, message me. I will send it and help you tailor it to your stack. If you think this post might help a colleague, send it to them. If this sounds like a better way to do voice with AI, and you need a partner who has done it before, DM me. I'd love to help you get your first win on the board. #customerservice #contactcenter #customerexperience #ccaas #saas

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.

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

For years, brands have relied on surveys, sliders, and forms to understand what customers think. The truth is, most people don’t think in checkboxes. They think out loud. AI voice changes how feedback works by removing the friction entirely. Instead of typing, customers can simply speak. They can explain what they like, what feels off, what they wish existed next, and why. Those spoken responses can then be transcribed, structured, and analyzed at scale, giving brands access to insight that is richer, more human, and far more representative than traditional polls ever allow. In the article, I explore what this looks like in practice, from fashion brands like SKIMS gathering nuanced feedback on fit and feel, to Ben & Jerry's’s using voice to surface flavor ideas and emotional associations, to the LEGO Group understanding how customers respond to colors, variants, and design choices in their own words. This shift turns feedback from a task into a conversation and transforms polls into living inputs that brands can learn from continuously. If you’re thinking about how to listen better at scale, this one’s worth the read.

Are your customers humans or just account numbers in your ledger? Do they feel nurtured or merely processed when they interact with your company? Despite the marvels of modern technology, it hasn't usurped the throne of the ultimate relationship tool in business - the art of one-on-one communication. It's this 'Human Touch' that forges the most potent emotional bond with a customer. But how do you infuse this Human Touch in your customer interactions? Instead of leaving you wondering, let me share a few practical, yet potent tips..." ⭐Personalize Communication: Tailor interactions to each customer’s needs and preferences. ⭐Active Listening: Fully engage with what customers are saying to understand their concerns. ⭐Empathy and Compassion: Show genuine understanding and concern for customers’ feelings. ⭐Follow-Up: Check in with customers post-interaction to ensure their satisfaction. ⭐Humanize Your Brand: Share relatable stories about your team and company journey. ⭐Accessibility: Provide easy access to human support, avoiding over-reliance on automation. ⭐Feedback Loops: Actively collect and respond to customer feedback. ⭐Surprise and Delight: Exceed expectations with unexpected gestures that resonate. ⭐Consistent Experience: Maintain a uniform, high-quality experience across all customer touchpoints. "Which of these tips resonate with you the most? Will you implement them? Or do you have a novel approach to share? Speak up - your insights might just inspire another business to improve their customer experience!

Everyone talks about Voice of Customer. Nobody talks about how to scale it. For as long as I can remember, VOC insights have been the responsibility of the CS. Our burden. Our job. Our problem. But in 2025, that mindset is outdated. We’re in the age of AI and automation, and it’s time we start acting like it. Customer insights should be automatically democratized across the organization for everyone to use. A customer voices a product concern on a call? That snippet should be clipped and sent to Product, with usage data and ARR context. A customer shares impressive ROI in a QBR? That’s marketing gold. It should go to their inbox saying: “This is our next case study.” Multiple tickets about the same issue? That trend should ping Engineering, before anyone even opens a spreadsheet. We’re sitting on a mountain of customer gold. But CS shouldn’t be the only team eating well. My favorite example? A weekly mashup of real customer feedback, clipped like a podcast for your CEO or founder. They should be spoon-fed the customer’s voice. AI and automation make that not just possible, but easy. What types of Voice of Customer content do you think should be democratized across your org — or already are? Things I am thinking about: Product Feedback Feature Ideas References Case Studies Speaking Engagements Revenue Risk Use Cases Relationship Mapping Revenue Growth

Is #VoiceAI the #NextBigThing for businesses since the #internet boom of the 90s? 🤖 I genuinely believe so. We're on the cusp of a massive change in how we interact with technology. Remember when every business suddenly needed a website? I see the same thing happening with voice AI by 2026. We're moving beyond simple voice commands. We're building #AIAgents that can truly understand conversational context, speak like a human, and, most importantly, act. Think about an Voice enabled #ConversationalAI that can: 💎 Search the web for you 💎 Update your customer database in real-time 💎 Send follow-up emails 💎 Book appointments and manage calendars This isn't science fiction; it's what's possible right now. I recently built a #demo for a client to showcase exactly this. I created an outbound calling system that contacts their customers and partners, asks relevant business questions, and instantly updates their database with the new information. No more manual data entry, no more outdated records. Check out the video to see it in action! 👇 This is just one example. The potential use cases are huge: 📞 Automated Surveys: Gather customer feedback on products and services effortlessly. 🤝 Sales & Outreach: Pitch new solutions to potential customers. 🛎️ Virtual Office Assistant: Handle incoming calls, answer customer questions, and book appointments 24/7. The goal is to create a better overall #CustomerExperience and free up valuable time for your team, resulting in #OperationalEfficiency and #Productivity jump. 👨💻 Curious about the #tech behind it? 👨💻 In simple terms, the system uses a backend to initiate a call through a #WebRTC platform (I used LiveKit, but others work too). This platform then connects to our backend "worker node" that asks the #Livekit to dial the number via Twilio #SIP to reach the person on the regular #PSTN phone network. Once the call is answered, the system cleverly adds the Voice AI agent into the conversation. From there, the person and the #AI can talk naturally. When the call ends, the system cleans up everything automatically. The best part? It's built to scale. By containerizing the worker nodes (think of them as little self-contained packages of code using #Docker) and managing them with something like #Kubernetes, the system can handle thousands of simultaneous calls without breaking a sweat. If things get busy, it just spins up new workers to handle the load. It's a seamless and powerful #architecture. I'm incredibly passionate about AI in general and its potential to revolutionize how businesses operate. If you found this #demo #insightful, I'd appreciate it if you'd like, comment, or share it with your network! What are your thoughts on this? Do you see Voice AI becoming a standard tool for businesses? Let's discuss in the comments! ⬇️ #VoiceAI #ML #MachineLearning #ArtificialIntelligence #FutureOfBusiness #Innovation #Automation #OutboundCalling #AISDR #LLM #CustomerService #AISales #AgenticAI