Customer Experience

The difference becomes much clearer when you put it into a real product. Take ElevenLabs’ voice AI as an example. 𝟏. 𝐓𝐡𝐞 𝐛𝐚𝐬𝐞 𝐥𝐚𝐲𝐞𝐫: 𝐠𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐯𝐞 𝐀𝐈 𝐜𝐚𝐩𝐚𝐛𝐢𝐥𝐢𝐭𝐲 At the first layer, ElevenLabs can turn text, scripts, voice references, or multilingual content into natural speech. For many products, this appears as a generative AI feature: AI narration in an education platform automatic voiceover in a video tool multilingual dubbing for content natural voice response in a support system Here, the value is mainly output quality. The system is generating voice, but it is not necessarily running a workflow. 𝟐. 𝐓𝐡𝐞 𝐦𝐢𝐝𝐝𝐥𝐞 𝐥𝐚𝐲𝐞𝐫: 𝐀𝐈 𝐯𝐨𝐢𝐜𝐞 𝐚𝐠𝐞𝐧𝐭 The next layer is when voice becomes interactive. A generated voice is not an agent. But a voice interface that can listen, understand intent, respond in context, ask follow-up questions, and manage a conversation starts to look much closer to one. This is where voice AI becomes more than audio generation. It becomes an interaction layer. The user is not just listening to generated speech. They are talking to a system that can handle a role inside a conversation. 𝟑. 𝐓𝐡𝐞 𝐡𝐢𝐠𝐡𝐞𝐫 𝐥𝐚𝐲𝐞𝐫: 𝐚𝐠𝐞𝐧𝐭𝐢𝐜 𝐀𝐈 𝐬𝐲𝐬𝐭𝐞𝐦 The more interesting layer appears when the voice agent is connected to real company systems. CRM. Support tickets. Calendars. Order databases. Knowledge bases. Payment tools. Internal APIs. Telephony stacks. Workflow automation tools. At that point, the system can do more than speak naturally. It can check an order, update a customer record, create a ticket, schedule a demo, trigger a follow-up, escalate to a human, or write the result of the conversation back into the system. In short: Generative AI creates the voice. An AI agent uses voice to interact. An agentic system connects that interaction to tools, data, permissions, and workflows. Explore more here https://lnkd.in/g57BYwHz *The chart is simplified, but it gives us a useful starting point to map these ideas to an actual product.

A learning culture is not built by offering more training. It emerges where curiosity, connection, and purpose intersect. Andrew Barry, in The Curious Lion, describes learning culture as a lotus where several forces overlap. I find this framing helpful because it moves the conversation beyond HR programs and into the fabric of the organization. At the individual level, there is curiosity. People must feel invited to ask questions, challenge assumptions, and explore. Without individual curiosity, learning remains compliance. At the organizational level, there is mission. Learning needs direction. When people understand what the company stands for and where it is going, their curiosity becomes focused rather than scattered. At the relational level, there is human connection. Learning accelerates in environments where people feel safe to speak, experiment, and reflect together. The fourth circle is continuous learning. Learning must be ongoing, not episodic. Not a workshop, but a way of operating. Continuous learning ensures that curiosity, mission, and connection reinforce each other over time rather than fading after the latest initiative. When these circles overlap, deeper elements emerge: Shared vision aligns effort. Shared experiences create collective memory. Shared assumptions shape how reality is interpreted. Shared stories transmit meaning across generations. At the center sits what we call learning culture. Not an initiative, but a pattern of how people think, relate, and evolve together. The question for leaders is not, “Do we offer learning opportunities?” It is, “Do curiosity, mission, and connection truly reinforce each other continuously in our organization?” That is where learning becomes cultural rather than occasional.

IndiGo (InterGlobe Aviation Ltd) CRISIS WASN’T IN THE SKIES. IT WAS IN THE LEADERSHIP CABIN. Three things stood out. One: Employees were left alone to face furious customers. No leader should ever let that happen. If you don’t stand by your people in a storm, don’t expect them to stand by your customers in the sun. Customer experience collapses the moment employees feel abandoned. Two: In any crisis, honesty is the only strategy that works. This time, the communication wasn’t transparent. When leaders hide the full picture, years of goodwill can disappear overnight. A crisis can earn trust, but only if you tell the truth. Three: The belief that “we are too big to be ignored” has ended more companies than competition ever has. Customers always have a choice. And if they don’t, they will create one. We shouldn’t watch the Indigo crisis like spectators. This is a reminder for every leader to build their own crisis blueprint. Because crises will come, when they do, your response becomes your reputation. There is more to business than profits. There are people, trust, and how you show up when it matters most.

Most Retrieval-Augmented Generation (RAG) pipelines today stop at a single task — retrieve, generate, and respond. That model works, but it’s 𝗻𝗼𝘁 𝗶𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝘁. It doesn’t adapt, retain memory, or coordinate reasoning across multiple tools. That’s where 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 𝗥𝗔𝗚 changes the game. 𝗔 𝗦𝗺𝗮𝗿𝘁𝗲𝗿 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 𝗳𝗼𝗿 𝗔𝗱𝗮𝗽𝘁𝗶𝘃𝗲 𝗥𝗲𝗮𝘀𝗼𝗻𝗶𝗻𝗴 In a traditional RAG setup, the LLM acts as a passive generator. In an 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗥𝗔𝗚 system, it becomes an 𝗮𝗰𝘁𝗶𝘃𝗲 𝗽𝗿𝗼𝗯𝗹𝗲𝗺-𝘀𝗼𝗹𝘃𝗲𝗿 — supported by a network of specialized components that collaborate like an intelligent team. Here’s how it works: 𝗔𝗴𝗲𝗻𝘁 𝗢𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗼𝗿 — The decision-maker that interprets user intent and routes requests to the right tools or agents. It’s the core logic layer that turns a static flow into an adaptive system. 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗠𝗮𝗻𝗮𝗴𝗲𝗿 — Maintains awareness across turns, retaining relevant context and passing it to the LLM. This eliminates “context resets” and improves answer consistency over time. 𝗠𝗲𝗺𝗼𝗿𝘆 𝗟𝗮𝘆𝗲𝗿 — Divided into Short-Term (session-based) and Long-Term (persistent or vector-based) memory, it allows the system to 𝗹𝗲𝗮𝗿𝗻 𝗳𝗿𝗼𝗺 𝗲𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲. Every interaction strengthens the model’s knowledge base. 𝗞𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗟𝗮𝘆𝗲𝗿 — The foundation. It combines similarity search, embeddings, and multi-granular document segmentation (sentence, paragraph, recursive) for precision retrieval. 𝗧𝗼𝗼𝗹 𝗟𝗮𝘆𝗲𝗿 — Includes the Search Tool, Vector Store Tool, and Code Interpreter Tool — each acting as a functional agent that executes specialized tasks and returns structured outputs. 𝗙𝗲𝗲𝗱𝗯𝗮𝗰𝗸 𝗟𝗼𝗼𝗽 — Every user response feeds insights back into the vector store, creating a continuous learning and improvement cycle. 𝗪𝗵𝘆 𝗜𝘁 𝗠𝗮𝘁𝘁𝗲𝗿𝘀 Agentic RAG transforms an LLM from a passive responder into a 𝗰𝗼𝗴𝗻𝗶𝘁𝗶𝘃𝗲 𝗲𝗻𝗴𝗶𝗻𝗲 capable of reasoning, memory, and self-optimization. This shift isn’t just technical — it’s strategic It defines how AI systems will evolve inside organizations: from one-off assistants to adaptive agents that understand context, learn continuously, and execute with autonomy.

When I started building my brand ecosystem publicly, everything shifted. The traditional advice says, "build it and they will come." But after studying founder brands, I've learned that most founders are stuck choosing between getting attention and maintaining integrity. Last year, I watched a brilliant entrepreneur struggle with this exact paradox. When I shared my Brand Trust Equation with her, something beautiful happened. Here's what I learned about building in public through systematic brand development: 1. Identity System Transparency Share your core messaging, positioning, and values openly. Building your identity in public creates accountability for authentic choices. Your audience connects with the journey, not just the destination. 2. Content System Broadcasting Document your strategic output across all platforms transparently. Sharing your content framework helps others while establishing your authority. Your systematic approach demonstrates professionalism and intentionality. 3. Experience System Documentation Show how people interact with your brand at every touchpoint. Building your customer journey in public creates better experiences for everyone. Your process transparency helps prospects know exactly what to expect. 4. Conversion System Sharing Reveal how attention becomes revenue in your business model. Building your funnel in public demonstrates the value of systematic thinking. Your transparent approach shows prospects the clear path forward. 5. Lighthouse Content Strategy Create cornerstone pieces that attract your ideal audience while repelling everyone else. Building your manifesto, methodology, case studies, and vision in public establishes authority. Your transparent philosophy becomes a filter for quality connections. This approach builds long-term brand equity instead of short-term attention. 6. Platform Synergy Framework Show how different platforms serve different purposes in your ecosystem. Building your multi-platform strategy in public creates strategic alignment. Other founders learn how to maximize impact across channels. This isn't just about building brands, it's about creating beautiful, systemized, and authentic businesses that serve both founders and their communities. When you build your brand ecosystem in public, you're not just attracting attention. You're building trust through the Brand Trust Equation: (Consistency × Authenticity × Value) ÷ Self-Promotion. The solution isn't choosing between integrity and attention, it's building systems that deliver both simultaneously through transparent, value-first brand development. The future belongs to those brave enough to build their brand systems in public. __ Enjoy this? ♻️ Repost it to your network and follow Matt Gray for more. Curious how this could look inside your business? DM me ‘System’ and I’ll walk you through how we help clients make it happen. This is for high-commitment founders only.

Loyalty is failing. Gen Z & long-term commitment. 22% of Gen Z consumers consider themselves loyal to one brand is a clear warning for legacy loyalty strategies. Unlike previous generations, Gen Z doesn’t see brand loyalty as a long-term commitment, they’re loyal to moments, not just names. +43% increase in engagement and sales conversions among Gen Z Beauty brands offering "limited-edition drops" and collaborative experiences. +71% Gen Z say they would rather spend money on an experience than a product. >>Loyalty is FAILING, but why<< +Transactional systems feel outdated: Point-based rewards for repeat purchases don’t excite this audience. They expect more than discounts or free samples. +They’re brand-agnostic but experience-driven: Gen Z freely switches between brands if the experience, aesthetic, or values feel fresher or more aligned with their identity. +They buy into stories, not just products: They want to align with brands that represent something, social causes, cultural movements, or communities they relate to. >>DYNAMIC LOYALTY<< What’s this? as it name indicates its a system that rewards interaction, aligns with their values, and constantly evolves. And that is what your brand needs. → Create experience-driven loyalty programs: Offer early access to limited drops, invite-only events, or backstage content. Think like a fan club, not a punch card. +Example: A loyalty tier that unlocks tickets to a pop-up experience or an exclusive AR filter. →Let them co-create: Invite Gen Z customers to co-develop product ideas, designs, or campaign themes. Give them ownership in your brand’s creative journey. +Example: Voting on packaging designs or joining beta tester groups. →Align with their values: Sustainability, inclusivity, and social good aren’t nice-to-haves. they’re expectations. Use loyalty programs to reward actions too, like recycling, sharing causes, or supporting small creators. +Example: “Earn loyalty points by returning empties or attending a sustainability workshop.” →Deliver constant novelty: Rotate limited editions regularly. Use scarcity and surprise to create FOMO and buzz. +Gen Z doesn’t commit to a single brand, but they’ll keep returning if each visit feels fresh and share-worthy. →Go omnichannel but social-first. Should live across TikTok, Instagram, pop-ups, and web. Let them earn or unlock rewards through social engagement, not just purchases. +Example: A user gets exclusive content or perks for creating UGC with your brand. Bottom Line. Loyalty must be earned over and over through experience, relevance, and emotional connection. Think dynamic loyalty: a system that rewards interaction and go for it. Find my curated search of examples and get ready for your next HIT. Featured Brands: Balmain Benefit Chanel Charlotte tilbury Cerave Fennty L’Oreal OGX YSL #beautypackaging #beautybusiness #beautyprofessionals #experienceretail #luxuryexperiences #genz

🚫 How to Run UX Research Without Access To Users. With practical techniques to avoid guesswork and gather insights if you can’t talk directly to users. Attached cheatsheet (with and without access to users) by Nielsen Norman Group. 🚫 Ask for reasons for no access to users: there might be none. ✅ First, study job openings to map existing workflows/tasks. ✅ Make friends with sales, customer success, support, QA. ✅ Find colleagues who are the closest to your customers. ✅ Convey your questions indirectly via your colleagues. ✅ If you can’t get users to come to you, go where they are. ✅ Ask to observe or shadow customers at their workplace. ✅ Listen in to customer calls and interview call centre staff. ✅ Request access to analytics, CRM reports, call centre logs. ✅ Use Google Trends to find product-related search queries. ✅ Gather insights from search logs, Jira backlog, support tickets. ✅ Explore past/ongoing NPS and Voice-of-Customer programs. ✅ Study reviews, discussions, comments for your product/competitors. ✅ Map key themes and user sentiment on TrustPilot, AppStore etc. ✅ Recruit users via UserTesting, Wynter (B2B), Maze, UserInterviews. ✅ Ask for small but steady commitments: 5 users × 30 mins, 1× month. 🚫 Avoid ad-hoc research: set up regular check-ins and timelines. As H Locke noted, if we shed the light strongly enough from many sources, we might end up getting a glimpse of the truth. Ironically, the stakeholders who can’t give you time or resources to talk to users often are the first to demand evidence to support your initiatives. Sometimes the reason why companies are reluctant to grant access to users is simply the lack of trust. They don’t want to disturb relationships with big clients which is carefully maintained by the customer success team. They might feel that research is merely a technical detail that clients shouldn’t be bothered with. Show that you deeply care about that relationship and that you don’t want to disturb it any way. What you do want though is to reduce costs and risk — the risk of drawing wide-reaching conclusions from very little research, or none at all. Your best shot is to explain research as a powerful risk mitigation tool. And: search for people whose priorities align with yours — people who value and see the impact of UX in their units. They would absolutely love to support your work because it also supports their work — and they will put up a good word for you if they only had known that you existed. ✤ Useful resources: UX Research Cheat Sheet, by Susan Farrell from NN/g (attached) https://lnkd.in/eUTHKWvF What Can You Do When You Have No Access To Users?, by H Locke https://lnkd.in/ewHEKhBS UX Research When You Can’t Talk To Users, by Chris Myhill https://lnkd.in/ez5-b6zf #ux #research

Introducing the web's first market map of the Product Analytics Market: I was floored when I couldn't find one of these online. Surely, Gartner or CBInsights or A16Z would have created one? It turns out not. So I spent the past 3 months: • Talking with 25 buyers • Researching the space myself • Interviewing 5 product leaders at key players This is what I learned about the most significant players in each space: (that PMs and product people need to know) 1. Core Product Analytics Platforms     The foundational tools for tracking user behavior and product performance Amplitude : The leader, an all-in-one platform for PMs to master their data Mixpanel : The leader in easy UX and pioneer in event-based analytics Heap | by Contentsquare: The automatic event tracking and real-time insights leader 2. A/B Testing & Experimentation     Platforms for analysis Optimizely : The premier tool for sophisticated A/B and multivariate testing VWO : The best for combining A/B testing with heatmaps and session recordings AB Tasty: The all-in-one solution for testing, personalization, and AI-driven insights 3. Feedback & Session Recording     Capture qualitative insights and visualize user interactions Medallia: The top choice for comprehensive experience management Hotjar | by Contentsquare: The go-to for visual feedback and user behavior insights Fullstory: The best for detailed session replay and user interaction analysis 4. Open-Source Solutions     Customizable, free analytics platforms for data sovereignty Matomo: The robust, privacy-focused open-source analytics platform Plausible Analytics: The lightweight, privacy-first analytics solution PostHog: The versatile, open source product analytics tool 5. Mobile & App Analytics     Specialized tools for mobile and app performance analysis UXCam: The best for in-depth mobile user interaction insights Localytics: The leader in user engagement and lifecycle management Flurry Analytics: The comprehensive, free mobile analytics platform 6. Data Collection & Integration     Gather and unify data across platforms Segment: The top choice for effortless customer data unification Informatica: The enterprise-grade solution for data integration and governance Talend: The flexible, open-source data integration tool 7. General BI & Data Viz     Non-product specific tools for data analysis and visualization Tableau: The leader in interactive, rich data visualization Power BI: The best for deep integration with Microsoft tools Looker: The modern BI tool for customizable, real-time insights 8. Decision Automation & AI     Systems for automated insights and decisions Databricks: The unified platform for data and AI collaboration DataRobot: The leader in automated machine learning and AI Alteryx: The comprehensive solution for analytics automation Check out the full infographic to see where your favorite tools fit and discover new platforms to enhance your product analytics stack.

Netflix Is Going Physical — And It Might Just Rewrite the Experiential Playbook At Cannes Lions, Netflix unveiled more details about its boldest move yet in fan engagement: Netflix House — permanent, immersive venues launching this fall in Philadelphia and Dallas. Think “Stranger Things” escape games, “Squid Game” obstacle courses, “Wednesday” carnivals, mini-golf through your favorite titles, themed cocktails, exclusive merch, and yes — a TUDUM Theater to host fan events and screenings. But this isn’t just a cool activation. It’s a strategic pivot that’s worth unpacking: ✅ Strategic Intent: Netflix isn’t trying to build a theme park empire. This is about deepening emotional ties with fans, amplifying buzz, and future-proofing its brand beyond the streaming wars. These venues aren’t just fun — they’re fan conversion engines. ✅ A New Content Loop: Every attraction is designed to be shared — built for UGC, influencer walkthroughs, cosplay, TikToks, and viral moments. Fans become marketers. Data becomes feedback for future IP development. The venue becomes a living R&D lab. ✅ Not Just Eyeballs — Wallets: With exclusive merch, themed dining, and potential collabs (think Netflix x Funko or Netflix Bites F&B), the monetization flywheel is in motion. Even modest visitor volume could generate $25–30M/year per location — and that’s before you count the uplift in brand love or viewership. ✅ Global Signals: This could be the first step toward regional pop-ups, international localization (imagine a “Lupin” heist experience in Paris or “Money Heist” in Madrid), and even a Netflix-con-branded event model. It’s fandom scaled offline. 💡 Big Picture? Netflix is building something Disney mastered decades ago — real-world storytelling at scale. And if this works, it unlocks a new dimension: streaming IP that lives, breathes, and sells in the physical world. 📊 Our modeled impact: ⌙ ~1M visitors in Year 1 ⌙ 100M+ earned impressions ⌙ 10–15% churn reduction among local superfans ⌙ $5–10 lift in ARPU among engaged segments ⌙ Payback in ~3–5 years — with marketing ROI baked in 🎯 This isn’t about “content” anymore. It’s about building culture. Kudos to Marian, Greg, Josh, Mitzi, Emily, Nidia, Lauren, Jessica, Nikki and team. #Netflix #Cannes #Media #Licensing #ConsumerProduct

This is how Anthropic decides what to build next—and it's brilliant. Instead of endless spec documents and roadmap debates, the Claude Code team has cracked the code on feature prioritization: prototype first, decide later. Here's their process (shared by Catherine Wu, Product Lead at Anthropic): Step 1: Idea → Prototype Got a feature idea? Skip the spec. Build a working prototype using Claude Code instead. Step 2: Internal Launch Ship that prototype to all Anthropic engineers immediately. No polish required—just functionality. Step 3: Watch & Listen Track usage religiously. Collect feedback actively. Let real behavior, not opinions, guide decisions. Step 4: Data-Driven Prioritization - High usage + positive feedback → roadmap priority - Low engagement or complaints → back to iteration This "prototype-first product shaping" flips traditional product development on its head. Instead of guessing what users want, they're measuring what users actually use. The beauty? They're dogfooding their own tool to build their own tool. The feedback loop is immediate, honest, and impossible to ignore. The takeaway: Your best product decisions come from real user behavior, not theoretical frameworks. Sometimes the fastest way to validate an idea isn't a survey or interview—it's a working prototype.