Customer Experience

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.

This may be an unpopular opinion but.... the most important characteristics I look for in a leader are vulnerability, empathy, and intuition. Everything else is secondary. Why? ➡️ Hire a leader with empathy because if they can create a culture where your employees are not terrified to fail or make a mistake, that will allow them to be more innovative. At Spanx we had 'oops' meetings where we would go around and talk about a mistake we made that week. Employees (and leadership!) had to stand up and share their biggest screw-ups. It made it to where the fear of embarrassment didn't kill performance. ➡️ Hire a leader who's vulnerable and doesn't feel the need to put on a facade to be taken seriously. When I started Spanx, instead of talking at my customer, I wanted to talk to them. I made myself vulnerable, and I tried to apply that same logic to working with my employees. Vulnerability helps you connect with everyone. Your customers, your employees, even your critics! ➡️ Hire a leader who's in touch with their intuition. Do they know how to listen to their gut? Do they know when to throw out the data and the 'expert opinions'? The Spanx team and I did this in 2019 when picking the famous leather legging as our hero product of the year.... we had no proof that it would create a cult-following but we had a gut feeling and we trusted it. What are your top 3 things you look for in a leader? ⬇️

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.

The disconnect between sales managers and reps in 2025 is wild. Manager: "Just pick up the phone!" Rep: *sends 47 emails, 12 texts, 3 LinkedIn messages, and a carrier pigeon* Sound familiar? 😅 After 20+ years in sales, I've watched this communication gap grow wider every year. But here's what both sides are missing: It's not about choosing ONE channel. It's about understanding WHICH channel works WHEN. The most successful reps I've seen? They've cracked the code: **First 24 hours:** • Email → Sets professional tone • LinkedIn → Shows you've done homework • Text → Only if they've given permission **Days 2-5:** • Phone call → NOW it's time (they know who you are) • Voice note → Personal touch that stands out • Video message → Shows real effort **The truth?** Your manager's right - calls DO convert better. You're also right - cold calling blind is dead. The magic happens when you warm them up FIRST. Think of it like dating: You wouldn't propose on the first date. So why are we calling strangers without context? **My top 3 strategies that actually work:** 1. The "Permission Play" End every email with: "Would a quick call tomorrow at 2pm work to discuss?" (They expect it now = higher answer rate) 2. The "Multi-Touch Warm-Up" Email → LinkedIn view → Call within 48 hours (They recognize your name = 3x more likely to answer) 3. The "Context Creator" Reference their LinkedIn post before calling "Saw your post about X, had a thought..." (You're not a stranger = conversation not pitch) Here's the brutal truth: Managers: Your reps aren't lazy. They're adapting to how buyers ACTUALLY buy in 2025. Reps: Your manager isn't wrong. The phone still closes more deals than any other channel. Bridge the gap. Use both. Win more. What's your take - Team Phone or Team Omnichannel? P.S I'm running a FREE 6-week LinkedIn Social Selling Bootcamp starting Monday 15th Sept, grab a free spot here https://lnkd.in/eVmxsMbM

Over the last year, nearly every FMCG executive I’ve spoken to whether sitting in Chicago, Paris, or São Paulo has echoed the same challenge: “We need to get closer to the consumer, faster.” Global brand, local nuance the future of FMCG growth depends on how well your leadership understands the street, not just the spreadsheet. It’s no longer enough to run a global playbook and hope for local resonance. Why? Because the center of gravity in FMCG has shifted. 84% of FMCG companies are now increasing local decision autonomy in key growth markets. (Bain FMCG Operating Model Report, 2023) → That means your CMO can’t be the only one with a finger on the pulse. → Your regional GM can’t just execute HQ strategy. → And your global leaders can’t lead with assumptions they need cultural fluency and operational humility. In other words: local-for-local is not just a supply chain shift. It’s a leadership shift. The most successful candidates weren’t those who had rotated through five global hubs. They were the ones who could… → Read the cultural nuances of consumer behavior in that specific region → Navigate the regulatory quirks that could derail a product launch → Influence global teams while building trust with local retailers → Speak the language literally and commercially They understood the street not just the spreadsheet. And they had the rare ability to connect what’s happening on the ground with what needs to be shifted at the center. These are the leaders FMCG needs now. → Strategists who don’t just adapt to the market, they anticipate it. → Operators who don’t wait for HQ they build and test in-market. → Connectors who know when to push back and when to align. Because in today’s world, speed and relevance win. And that doesn’t come from waiting for global sign-off. It comes from empowering the right local leaders. Here’s where I see many companies trip up: They treat “local” as junior. As operational. As reactive. The truth? Your next competitive edge may be a GM in Manila, a Marketing Director in Lagos, or a Commercial Lead in Warsaw who’s trusted enough to build strategy from the ground up. That’s what global FMCG companies are starting to understand and what we’re helping them solve for in every executive search we run. Not just global leaders who can work across regions…but local leaders who can lead across functions, cultures, and expectations while driving growth with urgency and empathy. This is the new face of global FMCG. Not centralized, but coordinated. Not rigid, but responsive. Not top-down, but built from the middle out. #ExecutiveSearch #FMCGLeadership #GlobalGrowth #ConsumerGoods #TalentStrategy #LeadershipHiring

On this fine Friday, allow this VC to pitch you a business idea born out of personal frustration. I’m a dedicated DAU (only because we don’t yet track Hourly Active Users), and yet these models barely know me. At this point, I feel like a traveling salesman from the 1800s, lugging a little suitcase of context from one AI to the next: “Here are my preferences. My priorities. My professional history. Please remember me.” And I do it. Because when you feed these tools the right context, they’re remarkable. But that potential - so close you can practically taste it - remains just out of reach. Instead, we’re stuck in a Groundhog Day loop of contextless first dates with memoryless machines. The intelligence is there. It’s the memory that fails you. What we need is a Personal Memory Vault: 🔐 Secure, encrypted, private by default 🔄 Portable across models - plug-and-play with any LLM, agent, or assistant 🧩 Composable and modular - you choose what to share, with whom, and for how long 📜 Versioned and auditable - full transparency into how and when your data is used We can build: 🔑 APIs that give developers secure, permissioned access to memory bundles - like professional context, health profiles, or travel preferences 📈 A user dashboard to track which apps have access, what they know, and when they’ve used it 💵 A freemium model for consumers - and licensing options for apps or agents that want to deliver memory-powered experiences Think: a personal OS that updates passively through your interactions. Plaid, but for context - storing a growing, contextual understanding of your life. Yes, it’s about making AI more useful. But it’s more about control. We need the memory layer to be model agnostic and platform independent, because Big Tech’s next move is obvious: offer “personalized memory” - and use it to lock you in tighter than ever. Sam Altman has already said he wants ChatGPT to remember your whole life. Look, I’m an OpenAI fan. But I reserve the right to change my mind the moment Google DeepMind, Anthropic, or xAI drops something better. What I don’t want is to spend my weekends migrating my digital soul like it’s an IBM mainframe in 1986. I don’t want a hundred context engines. I don’t want to be platform-loyal out of sunk-cost guilt. I just want to stop going on first dates with my own data. I’m just a girl, standing in front of an AI, asking it to remember her. 💔

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

“We hired you 3 months ago? Why has our churn not dropped yet?” That’s a real quote that a CCO I know recently heard from their CEO. Too often, I witness the following 4 act play: Act 1: “We have a big churn problem” Act 2: “Let’s hire a Chief Customer Officer” Act 3: “Why is churn still high?” Act 4: “We didn’t really need a Chief Customer Officer anyways” Putting aside the title, the issue is that Chief Customer Officers OWN operations and INFLUENCE the rest of the company. If I had to list the levers in reducing churn across companies that I’ve experienced, they’d go in descending order: * Product-market fit * Balance of desire for growth with aligning to the Ideal Customer Profile * Product stickiness * Competitive dynamics * Pricing * Product functionality and quality * Post-sales operations “Wait - did Nick say that post-sales operations don’t matter?” Of course not. All I’m saying is that rethinking onboarding, hiring #CustomerSuccess Managers, streamlining support, etc. can only get you so far. Putting numbers on it… - If your Gross Retention is < 80%, I’ve found that strong Chief Customer Officers can reduce churn by 3-5 points, since there is a lot of low hanging fruit. - If your GRR is between 80 and 90%, it’s probably closer to a 1-2 point reduction potential. - If your GRR is above 90%, a 1 point churn drop is massive. What about the rest? The biggest churn drops come from things like the below, which CCOs can identify and then partner with colleagues to implement: * “Customers that use feature [X] have 10 points less churn” => Product: Make feature X easier to deploy * “Clients that buy from us that use [integrated system Y] churn at a high rate” => Marketing: Avoid outbound efforts to [Y] audience * “Our pricing model is causing churn because it becomes unaffordable at high volumes” => Product Marketing: Rethink the high end of the pricing curve The CCO role isn’t just about being a detective and solving churn on your own. It’s also about being a search light - shining visibility onto how the rest of the company can reduce churn.

The digital bank is an outdated concept. Fast being replaced by the intelligent bank. The only question is how soon banks can manage the transition. Let’s take a look. I have broken down the main elements that make up the transition to the intelligent bank: 1. From transactional to predictive banking: digital banking enabled 24/7 self-service, but intelligent banking takes it further by predicting customer needs. AI-driven models analyse real-time data to offer personalised financial insights, proactive credit offerings, and automated investment recommendations. 2. AI-powered risk & fraud management: traditional risk assessment relied heavily on historical data. Intelligent banks use AI and machine learning to detect fraud in real time, identify suspicious patterns and prevent threats before they occur. 3. Hyper-personalisation: instead of generic offers, intelligent banks use AI to tailor financial products to individual customers (mass personalisation). 4. Seamless omni-channel experience: customers no longer interact with banks through a single channel. Intelligent banking ensures that a user can start a transaction on a mobile app, continue it via a chatbot, and complete it with a human advisor. All while maintaining a seamless, connected experience. 5. Autonomous banking operations: intelligent banks optimise back-office processes using cloud and AI automation, reducing human errors and significantly improving efficiency. Functions such as loan approvals, compliance checks, and reconciliation are increasingly self-regulated by AI-driven workflows.   Banks are in a time race. They not only need to move from digital to intelligent but also do it fast.   In doing so technology is the biggest dependency. One of the most interesting approaches I have seen on how to best support banks in this transition is Huawei's 4-Zero model, which is based on 4 main pillars:   1. Zero Downtime → Instant Readiness AI-powered predictive maintenance and cloud resilience ensure 24/7 availability, allowing banks to deploy and scale AI solutions without service disruptions. 2. Zero Wait → Faster Customer Experiences AI-driven real-time processing eliminates delays in transactions, approvals, and customer interactions, making banking services ultra-responsive. 3. Zero Touch → Reduced Operational Burden End-to-end automation using AI and machine learning removes manual intervention in processes like KYC, loan approvals, and compliance, freeing up resources for AI innovation. 4. Zero Trust → Seamless AI Integration AI-driven security frameworks continuously validate access, ensuring trust and compliance while enabling banks to integrate AI-powered services without increasing risk. The era of intelligent banking isn’t a distant future - it’s happening now. Banks will not be able to transform in months but getting a head start can make a difference. Opinions and graphics: Panagiotis Kriaris  #HuaweiMWC  #RAAS  #IntelligentFinance

The Voice Stack is improving rapidly. Systems that interact with users via speaking and listening will drive many new applications. Over the past year, I’ve been working closely with DeepLearning.AI, AI Fund, and several collaborators on voice-based applications, and I will share best practices I’ve learned in this and future posts. Foundation models that are trained to directly input, and often also directly generate, audio have contributed to this growth, but they are only part of the story. OpenAI’s RealTime API makes it easy for developers to write prompts to develop systems that deliver voice-in, voice-out experiences. This is great for building quick-and-dirty prototypes, and it also works well for low-stakes conversations where making an occasional mistake is okay. I encourage you to try it! However, compared to text-based generation, it is still hard to control the output of voice-in voice-out models. In contrast to directly generating audio, when we use an LLM to generate text, we have many tools for building guardrails, and we can double-check the output before showing it to users. We can also use sophisticated agentic reasoning workflows to compute high-quality outputs. Before a customer-service agent shows a user the message, “Sure, I’m happy to issue a refund,” we can make sure that (i) issuing the refund is consistent with our business policy and (ii) we will call the API to issue the refund (and not just promise a refund without issuing it). In contrast, the tools to prevent a voice-in, voice-out model from making such mistakes are much less mature. In my experience, the reasoning capability of voice models also seems inferior to text-based models, and they give less sophisticated answers. (Perhaps this is because voice responses have to be more brief, leaving less room for chain-of-thought reasoning to get to a more thoughtful answer.) When building applications where I need a more control over the output, I use agentic workflows to reason at length about the user’s input. In voice applications, this means I end up using a pipeline that includes speech-to-text (STT) to transcribe the user’s words, then processes the text using one or more LLM calls, and finally returns an audio response to the user via TTS (text-to-speech). This, where the reasoning is done in text, allows for more accurate responses. However, this process introduces latency, and users of voice applications are very sensitive to latency. When DeepLearning.AI worked with RealAvatar (an AI Fund portfolio company led by Jeff Daniel) to build an avatar of me, we found that getting TTS to generate a voice that sounded like me was not very hard, but getting it to respond to questions using words similar to those I would choose was. Even after much tuning, it remains a work in progress. You can play with it at https://lnkd.in/gcZ66yGM [At length limit. Full text, including latency reduction technique: https://lnkd.in/gjzjiVwx ]