Customer Experience

AI handled 75% of customer chats at Klarna… and they still brought humans back. Why? Because speed isn’t the same as quality! Because customers noticed the difference. And it wasn’t good. Speed? Great. Empathy? Missing. Trust? Slipping. After a year of leaning heavily on AI, they’re rehiring human support agents. Real people. Not because AI failed—but because it wasn’t enough. AI can answer your question. But only a human can make you feel heard. Klarna is now hiring in rural areas and among student communities—betting on empathy, not just efficiency. This should be a wake-up call. You can automate tasks. But relationships? They still need people! This is why the future isn’t human vs AI. It’s human with AI. And the companies who get that balance right? They’ll win customer loyalty, and talent, faster than any chatbot ever could.

🗺️ AirBnB Customer Journey Blueprint, a wonderful practical example of how to visualize the entire customer experience for 2 personas, across 8 touch points, with user policies, UI screens and all interactions with the customer service — all on one single page. AirBnB Customer Journey (Google Drive): https://lnkd.in/eKsTjrp4 Spotify Customer Journey (High-res): https://lnkd.in/eX3NBWbJ Now, unlike AirBnB, your product might not need a mapping against user policies. However, it might need other lanes that would be more relevant for your team. E.g. include relevant findings and recommendations from UX research. List key actions needed for next stage. Add relevant UX metrics and unsuccessful touchpoints. That last bit is often missing. Yet customer journeys are often non-linear, with unpredictable entry points, and integrations way beyond the final stage of a customer journey map. It’s in those moments when things leave a perfect path that a product’s UX is actually stress tested. So consider mapping unsuccessful touchpoints as well — failures, error messages, conflicts, incompatibilities, warnings, connectivity issues, eventual lock-outs and frequent log-outs, authentication issues, outages and urgent support inquiries. Even further than that: each team could be able to zoom into specific touch points and attach links to quotes, photos, videos, prototypes, design system docs and Figma files. Perhaps even highlight the desired future state. Technical challenges and pain points. Those unsuccessful states. Now, that would be a remarkable reference to use in the beginning of every design sprint. Such mappings are often overlooked, but they can be very impactful. Not only is it a very tangible way to visualize UX, but it’s also easy to understand, remember and relate to daily — potentially for all teams in the entire organization. And that's something only few artefacts can do. Useful resources: Free Template: Customer Journey Mapping, by Taras Bakusevych https://lnkd.in/e-emkh5A Free Template: End-To-End User Experience Map (Figma), by Justin Tan https://lnkd.in/eir9jg7J Customer Journey Map Template (Figma), by Ed Biden https://lnkd.in/evaUP4kz Free Figma/Miro User Journey Maps Templates https://lnkd.in/etSB7VqB User Journey Maps vs. Service Blueprints (+ Templates) https://lnkd.in/e-JSYtwW UX Mapping Methods (+ Miro/Figma Templates) https://lnkd.in/en3Vje4t #ux #design

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

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.

In today's digital age, leveraging celebrity brand ambassadors has become a popular strategy for businesses, including startups. As someone who's been a brand ambassador for various companies over the years and dabbled in startups myself, I've seen firsthand the ups & downs of this approach. People often ask if it's always beneficial to have a celebrity endorse your products or services. I’ll break it down to the most important things to consider. Visibility - Celebrities bring a massive following, offering increased visibility & reach to a wider audience that may have been difficult to engage otherwise. This exposure could enhance brand recognition & create positive associations in consumers' minds. Credibility -  The right kind of celebrity could inject a dose of credibility into your brand. Consumers may in turn perceive your product as reliable, particularly important for startups aiming to build a solid reputation & carve out a slice of the market. Engagement - Some celebrities are able to forge personal connections with their community. By aligning your startup with a celebrity, you may be tapping into that emotional connection & that community may be more likely to show interest in your brand. Costs - Engaging a celebrity ambassador comes at a price. Even if you opt for an equity-based deal, you still need to allocate valuable resources to amplify the association, potentially diverting funds from other key areas of requirement. Authenticity - The alignment between the celebrity & your product must seem genuine. If the partnership feels like a misfit or forced, the results can be counter productive. Today's consumers are evolved & can sense inauthenticity from a distance. Sustenance - While celebrities can generate a buzz in the short term, building interest & loyalty requires consistent effort & a solid value offering that goes beyond the celebrity association. Your product still needs to deliver exceptional value beyond the initial buzz.. Relevance - Ensure the celebrity aligns with the startup's target audience, values & offerings. The endorsement should make sense within the startup's brand identity & goals. Budget - Assess whether the startup can afford the associated costs, especially including the ongoing marketing efforts. Do not assume that bringing a celebrity on board itself is going to win you the war. It’s just a head start. Long-Term Strategy - A well-crafted partnership should naturally integrate into your overall marketing & branding strategy & solidify your position & bring sustained growth. Timing - Most importantly, remember, spending so much in early stages, or early dilution in equity can have long-term consequences, so ask yourself if you’re really ready at this stage. Ultimately, the decision to engage a celebrity brand ambassador should be based on your unique circumstances & goals. Hopefully this will help some make an informed decision. #BrandAmbassadors #CelebrityEndorsements #InfluencerMarketing

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

Trust at scale has always been the hardest thing to build in business. Word of mouth was the original mechanism. One person tells another, credibility transfers, trust builds slowly. It worked, but it was a limited mechanism you couldn't control. What's changed today is the infrastructure. Reach, repeated visibility to a large audience, is now one of the most powerful trust-building tools available to any founder or business. I am not saying being seen is the same as being trusted, but trust requires repeated exposure before it forms. The people and businesses that maintain high engagement at scale on their social media are the ones that showed up repeatedly, with a clear point of view, long before the numbers got impressive. Trust is a perception built over time through repeated signals: what you say, what you stand for, what you consistently show up for. Reach accelerates that process. Every post is another data point for your audience to evaluate whether your judgment is worth following. Enough of those data points, delivered consistently, and reach becomes evidence that you are someone worth trusting. The people and businesses who understand this aren't just building audiences. They're building credibility that makes everything else, fundraising, hiring, selling, structurally easier. #rajshamani #figuringout

Getting on the shelf is just the first step; staying there and driving sales is where real brand-building happens. Placement matters. A product tucked away in the wrong section or out of a consumer’s direct line of sight is a missed opportunity. Visibility drives recall, and recall drives purchase. But distribution isn’t just about logistics—it’s about relationships. Strong ties with distributors and retailers don’t just ensure availability; they create brand advocates on the ground. A retailer who trusts a brand is far more likely to recommend it to the most important person in the chain - our customer. Most importantly, availability is non-negotiable. A product that isn’t on the shelf when a consumer looks for it is a lost sale, and over time, a lost consumer. Consistency in supply, smart demand forecasting, and last-mile efficiency are what turn a stocked shelf into a successful brand. In India, where kirana stores remain the backbone of everyday shopping, offline distribution is not just a sales channel; it’s a trust-builder, a credibility marker, and the key to reaching millions of consumers where they actually shop.

Polling vs Webhooks As systems grow more complex, choosing the right update strategy becomes crucial. Let me break down the two primary approaches that define real-time data synchronization: Polling: The Traditional Approach • Client periodically requests updates • Predictable but resource-intensive • Full control over request timing • Higher latency, higher costs at scale Webhooks: The Modern Push System • Server notifies client of changes • Event-driven and efficient • Near real-time updates • Better resource utilization Concrete Implementation Examples: Polling Works Best For: 1. Payment status checks 2. Order tracking systems 3. Basic monitoring tools 4. MVP implementations 5. Systems with predictable update patterns Webhooks Excel In: 1. Payment processing (PayPal) 2. Repository events (GitHub) 3. CRM integrations (Salesforce) 4. E-commerce inventory updates 5. Real-time messaging systems Key Decision Factors: - Update frequency requirements - Infrastructure complexity tolerance - Development team expertise - System scalability needs - Budget constraints Currently implementing these in production? Both approaches have their place. The key is matching the solution to your specific requirements rather than following trends.

Data is everything in product design. Without data, we open ourselves up to: - Biases - Opinions - Confusion - Misalignment When we are data-informed and that data is accurate, we can truly make educated product decisions. I like to think of data in two layers: a) What’s happening and b) Why it’s happening. Let’s break it down. What’s happening: - Business data tells us how the business is doing - Marketing/sales data tells us where our customers come from - Retention data tells us when and why customers are leaving us - Engagement data tells us how customers are using our product Why it’s happening: - User research gives us rich insight into why something is happening - Voice of the customer data shows us how customers talk about our product - Usability scores show us how people perceive our product or feature experience in a measurable way - Product market fit & satisfaction scores give us a simple and actionable metric to track and improve over time In terms of accessing that data, methodologies vary, but generally speaking, I always advise the following: 1. Get access to growth and retention data through business dashboards. 2. Get access to product data through your product analytics tool. 3. Set up a cadence to gather customer reviews & comments, either manually or via automated tools. 4. Set up a cadence to speak to your users continuously to answer the why. 5. Set up a recurring survey to track satisfaction and usability. If you don’t have the data structure for any of the above, speak to your product and data team to see if you can change that. If not, rely on the data that you can actually get. PS: The list of metrics is indicative: Actual metrics will differ greatly from one company to another and largely depend on the industry, niche, as well as your data infrastructure and setup. — If you found this useful, consider reposting ♻️ How are you collecting and using data in your design process? What else are you tracking?