Hybrid Customer Experience Techniques

Continuing from last week’s post on the rise of the Voice Stack, there’s an area that today’s voice-based systems often struggle with: Voice Activity Detection (VAD) and the turn-taking paradigm of communication. When communicating with a text-based chatbot, the turns are clear: You write something, then the bot does, then you do, and so on. The success of text-based chatbots with clear turn-taking has influenced the design of voice-based bots, most of which also use the turn-taking paradigm. A key part of building such a system is a VAD component to detect when the user is talking. This allows our software to take the parts of the audio stream in which the user is saying something and pass that to the model for the user’s turn. It also supports interruption in a limited way, whereby if a user insistently interrupts the AI system while it is talking, eventually the VAD system will realize the user is talking, shut off the AI’s output, and let the user take a turn. This works reasonably well in quiet environments. However, VAD systems today struggle with noisy environments, particularly when the background noise is from other human speech. For example, if you are in a noisy cafe speaking with a voice chatbot, VAD — which is usually trained to detect human speech — tends to be inaccurate at figuring out when you, or someone else, is talking. (In comparison, it works much better if you are in a noisy vehicle, since the background noise is more clearly not human speech.) It might think you are interrupting when it was merely someone in the background speaking, or fail to recognize that you’ve stopped talking. This is why today’s speech applications often struggle in noisy environments. Intriguingly, last year, Kyutai Labs published Moshi, a model that had many technical innovations. An important one was enabling persistent bi-direction audio streams from the user to Moshi and from Moshi to the user. If you and I were speaking in person or on the phone, we would constantly be streaming audio to each other (through the air or the phone system), and we’d use social cues to know when to listen and how to politely interrupt if one of us felt the need. Thus, the streams would not need to explicitly model turn-taking. Moshi works like this. It’s listening all the time, and it’s up to the model to decide when to stay silent and when to talk. This means an explicit VAD step is no longer necessary. Just as the architecture of text-only transformers has gone through many evolutions, voice models are going through a lot of architecture explorations. Given the importance of foundation models with voice-in and voice-out capabilities, many large companies right now are investing in developing better voice models. I’m confident we’ll see many more good voice models released this year. [Reached length limit; full text: https://lnkd.in/g9wGsPb2 ]

Voice AI is more than just plugging in an LLM. It's an orchestration challenge involving complex AI coordination across STT, TTS and LLMs, low-latency processing, and context & integration with external systems and tools. Let's start with the basics: ---- Real-time Transcription (STT) Low-latency transcription (<200ms) from providers like Deepgram ensures real-time responsiveness. ---- Voice Activity Detection (VAD) Essential for handling human interruptions smoothly, with tools such as WebRTC VAD or LiveKit Turn Detection ---- Language Model Integration (LLM) Select your reasoning engine carefully—GPT-4 for reliability, Claude for nuanced conversations, or Llama 3 for flexibility and open-source options. ---- Real-Time Text-to-Speech (TTS) Natural-sounding speech from providers like Eleven Labs, Cartesia or Play.ht enhances user experience. ---- Contextual Noise Filtering Implement custom noise-cancellation models to effectively isolate speech from real-world background noise (TV, traffic, family chatter). ---- Infrastructure & Scalability Deploy on infrastructure designed for low-latency, real-time scaling (WebSockets, Kubernetes, cloud infrastructure from AWS/Azure/GCP). ---- Observability & Iterative Improvement Continuous improvement through monitoring tools like Prometheus, Grafana, and OpenTelemetry ensures stable and reliable voice agents. 📍You can assemble this stack yourself or streamline the entire process using integrated API-first platforms like Vapi. Check it out here ➡️https://bit.ly/4bOgYLh What do you think? How will voice AI tech stacks evolve from here?

VCs used to insist that software companies *not* engage in services. Services were viewed as (a) low-margin and (b) evidence of product gaps. No longer. They've now become a source of competitive differentation. The latest post from Ashu Garg and Jaya Gupta of Foundation Capital — The $4.6T Services-as-Software Opportunity: Lessons from Year One — does a fantastic job of articulating the rationale. The label of "forward-deployed engineers" (FDEs) has given a new shine to these services. Palantir Technologies pioneered that framing to tremendous success. In #martech, OfferFit by Braze executed this approach beautifully as well. But they are services under a more technical name. Over the past 10 years in writing about #martech, I've charted the convergence of former dichotomies: (1) from suite vs. best-of-breed to platform ecosystems, (2) from software vs. services to blended business models, and (3) from build vs. buy to customize on commercial platforms. This is now culminating in a mega-convergence of all three of these themes into a COMPOSABLE CANVAS. What's software? What's a service? What was made internally vs. externally? All these things are blending together. And it's absolutely amazing what it makes possible.

Retail is dead. Foot traffic is down across the board. That’s the narrative we hear over and over being pushed in the media. Yet TALA - Grace Beverley’s brand born online - has opened their first physical store in Carnaby Street this weekend, to queues around Soho & a sell-out ticketed event. So rather than being dead, what if the role of brand retail has simply transformed? My take 👉 The store is no longer solely top of the funnel or entirely about discoverability. It’s the destination. The community hub. The clubhouse. It’s where content becomes tangible. Where brand world becomes real world. Where you walk through the door and it feels like stepping into their Instagram, their TikToks, their values. We’re not just talking racks and rails - there’s a coffee bar, photobooths, events, and experiences. This is community-led commerce. It’s a cultural space disguised as a high street shop. And I believe this is where we see the real revival of the high street - not as a retail destination, but as a brand world brought to life. A place to deepen connection with your community - ultimately strengthening the life time value of that customer. The blueprint is clear: Content captures. Community keeps. IRL deepens. TALA joins the ranks of Gymshark, Odd Muse and Glossier, Inc. - brands that built strong digital tribes before laying a single brick and now use their stores as destinations for the community to connect IRL. And in a world where discovery is unpredictable - spanning podcasts, group chats, TikToks and Substack - trying to funnel people in linearly is a lost cause. The smartest brands aren’t forcing a path. They’re showing up where their community already is & then inviting them in deeper. Retail isn’t dead. It’s reinventing itself & I'm so here for it. Calling it now - your favourite digital brand worlds will manifest in real life in the next 18 months whether through pop ups or permanent stores. Mark my words!

The open-source AI ecosystem for agents developers has exploded in the past few months. I've been testing dozens of new libraries, and honestly, it's becoming increasingly difficult to keep track of what actually works and what the state of the art is. So, I built an updated map of the tools that matter, the ones I'd actually reach for when building a new agent. The interesting pattern I'm seeing: we're moving past the "ChatGPT wrapper" phase into genuine infrastructure. The overview includes 40+ open-source packages across: → Agent orchestration frameworks that go beyond basic LLM wrappers: CrewAI for role-playing agents, AutoGPT for autonomous workflows, Langflow for visual agent building. → Tools for computer control and browser automation: Browser Use and Stagehand for LLM-friendly web navigation, Open Interpreter for local machine control, and Cua to control Mac environments. → Voice interaction capabilities beyond basic speech-to-text: Ultravox for real-time voice, Dia for natural TTS, Pipecat for complete voice agent stacks. → Memory systems that enable truly personalized experiences: Mem0 for self-improving memory, Letta for long-term context across sessions, LangMem for shared knowledge bases. → Testing and monitoring solutions for production-grade agents: AgentOps for benchmarking, Langfuse for LLM observability, VoiceLab for voice agent evaluation. Full breakdown with GitHub repos links https://lnkd.in/g3fntJVc

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]

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

If more of your store sales start on TikTok lately, you might wanna read this. 𝘛𝘩𝘦 𝘴𝘢𝘭𝘦 𝘪𝘴 𝘥𝘦𝘤𝘪𝘥𝘦𝘥 𝘣𝘦𝘧𝘰𝘳𝘦 𝘺𝘰𝘶𝘳 𝘤𝘶𝘴𝘵𝘰𝘮𝘦𝘳 𝘦𝘷𝘦𝘯 𝘦𝘯𝘵𝘦𝘳𝘴 𝘺𝘰𝘶𝘳 𝘴𝘵𝘰𝘳𝘦. The checkout happens in-store. But the sale happens everywhere else. Here's the reality: This year 60%+, and in 2027, 70% of retail sales will be digitally influenced. I can't emphasize this enough; here's what most brands miss—digital influence isn't just about online sales. It's about shaping every moment before the customer even walks into your store. L'Oréal cracked this code: 100M+ AR try-on sessions driving real conversions. 31 brands orchestrating seamless experiences across 72 countries. No.1 in beauty influencer marketing (29% market share), 20-80% higher conversion rates through enhanced digital experiences. The new customer journey isn't linear—it's layered: - They discover you on social - Research you through reviews and UGC - Try your product virtually through AR - Get retargeted with personalized content - Finally purchase in-store (feeling confident they're making the right choice) Every touchpoint matters, and every interaction influences the final decision. The brands winning today aren't just selling products—they're orchestrating experiences across owned, paid, and earned media that guide customers from curiosity to checkout. Digital discovery is increasingly pay-to-play and shoppers are paying attention. ++ Tactical Recommendations for CPG / FMCG Brands ++ 1. Beyond just having perfect, high SOV product pages, create discovery ecosystems. - Optimize for "zero-moment-of-truth" searches. - Activate shoppable content at scale. - Leverage user-generated content as social proof. Brands that do these see a 35% higher conversion rate from digital touchpoints to in-store purchases. 2. Connect digital engagement directly to retail execution. - Geo-target digital campaigns to drive foot traffic - Create "store-specific" digital content CPG brands using geo-targeted social ads see a 23% higher in-store sales lift in targeted markets. 3. Most important one; stop flying blind—measure digital influence on offline sales. - Implement unique promo codes for each digital touchpoint to track conversion paths. - Use customer surveys at point of purchase. - Partner with retailers on shared data insights Brands with proper attribution see 15-25% improvement in marketing ROI within 12 months. 𝗧𝗼 𝗮𝗰𝗰𝗲𝘀𝘀 𝗮𝗹𝗹 𝗼𝘂𝗿 𝗶𝗻𝘀𝗶𝗴𝗵𝘁𝘀 𝗳𝗼𝗹𝗹𝗼𝘄 ecommert® 𝗮𝗻𝗱 𝗷𝗼𝗶𝗻 𝟭𝟰,𝟲𝟬𝟬+ 𝗖𝗣𝗚, 𝗿𝗲𝘁𝗮𝗶𝗹, 𝗮𝗻𝗱 𝗠𝗮𝗿𝗧𝗲𝗰𝗵 𝗲𝘅𝗲𝗰𝘂𝘁𝗶𝘃𝗲𝘀 𝘄𝗵𝗼 𝘀𝘂𝗯𝘀𝗰𝗿𝗶𝗯𝗲𝗱 𝘁𝗼 𝗲𝗰𝗼𝗺𝗺𝗲𝗿𝘁® : 𝗖𝗣𝗚 𝗗𝗶𝗴𝗶𝘁𝗮𝗹 𝗚𝗿𝗼𝘄𝘁𝗵 𝗻𝗲𝘄𝘀𝗹𝗲𝘁𝘁𝗲𝗿. #CPG #FMCG #AI #ecommerce Procter & Gamble PepsiCo Unilever The Coca-Cola Company Nestlé Mondelēz International Kraft Heinz Ferrero Mars Colgate-Palmolive Henkel Bayer Haleon Kenvue The HEINEKEN Company Carlsberg Group Philips Samsung Electronics Panasonic North America

Empathy-powered. Digitally enabled. Patient connected In today’s fast-evolving healthcare landscape, connected care isn’t just about tech—it’s about enhancing human connection at every touchpoint. Key insights from Deloitte ’s 2025 Global Health Care Executive Outlook show how we can harmonize digital transformation with the human-centric care our patients deserve: 1. Prioritize integrated digital platforms • ~70% of global C‑suite leaders are investing in digital tools and services to enable seamless patient journeys . • This connectivity supports continuous care—whether in-hospital, remote, or at home. 2. Modernize core systems while keeping the human anchor • 60% are upgrading EMRs and ERP systems . • When clinicians can access integrated data swiftly, they spend less time documenting and more time connecting with patients. 3. Embed empathy into every digital interaction • Cybersecurity (78% prioritize) builds trust—patients feel cared for when their data is protected . • A secure, respectful environment is the foundation for truly human-centered care. 4. Enhance clinician well-being to improve connectedness • 80% of leaders recognize workforce strain; digital tools can reduce burnout and foster deeper patient engagement . • When staff feel supported, they show up both professionally and emotionally. 5. Expand virtual and hybrid care with a personal touch • 65% of consumers find virtual care more convenient —but scaling it successfully means integrating empathy and follow-up. • Reimagining care pathways ensures consistent human connection, whether digital or face-to-face. ⸻ 🎯 Managing connected care with humanity means: • Leveraging interoperable systems that share real-time insights across care teams. • Training clinicians in digital empathy—listening through the screen, addressing emotional cues. • Designing secure, intuitive platforms that empower patients without overwhelming them. • Supporting staff with AI-driven admin relief, enabling them to focus on people. • Creating holistic care pathways that blend telehealth, in-clinic, and home-based services under one cohesive plan. By weaving technology into our care systems thoughtfully, we can create a healthcare experience that’s efficient, personalized, and emotionally resonant. Looking forward to your thoughts: how is your organization balancing connectivity with compassion? Sara Siegel Link to the report: https://lnkd.in/etDPEc3a #connectedcare

Omnichannel Marketing: Reaching Customers Where They Are Introduction & Overview Omnichannel marketing is crucial in today's landscape, focusing on a seamless, integrated experience across various channels. Unlike multichannel marketing, which operates independently, omnichannel meets consumers where they are, offering a smooth, personalized journey. This approach enhances customer satisfaction, boosts conversion rates, and strengthens brand loyalty, driving growth. Key Concepts -Cross-Channel Integration: Ensures consistent messaging and experience across all channels. -Customer-Centric Approach: Focuses on understanding customer needs to create personalized experiences. -Personalization: Tailors messages based on user behavior, enhancing relevance. -Data-Driven Insights: Uses analytics to optimize channels based on customer behavior. Challenges Omnichannel marketing faces challenges like integrating diverse platforms for a seamless experience, managing and securing customer data from multiple sources, and balancing personalization with privacy concerns. Additionally, coordinating offline and online efforts to ensure consistent branding requires effective team collaboration. Strategies & Solutions Enhance your omnichannel marketing by creating detailed customer profiles, maintaining consistent branding, and using marketing automation for timely communication. Optimize the mobile experience, engage customers through social media, connect online and offline interactions, and continuously analyze data to refine your strategies. Benefits & Insights Omnichannel marketing enhances customer experience and engagement, increases retention and conversion rates, and provides valuable insights through integrated data collection. This approach builds a competitive edge by offering a seamless, personalized journey that meets customer needs across channels, fostering loyalty and driving sales. Conclusion Omnichannel marketing helps brands deliver a consistent, personalized experience by integrating online and offline channels, using data to enhance customer journeys. Though it requires initial investment, the approach strengthens customer relationships, boosts engagement, and drives sales growth. Next Steps Start with an audit of your marketing channels for consistency, then develop a strategy for gathering and using customer data while maintaining privacy. Invest in cross-channel marketing automation, explore online-offline integrations, and use data analysis to continuously refine your omnichannel strategy. #omnichannelmarketing #customerengagement #digitaltransformation #reachyourcustomers #marketingstrategy