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.

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.

Last week, a customer said something that stopped me in my tracks: “Our data is what makes us unique. If we share it with an AI model, it may play against us.” This customer recognizes the transformative power of AI. They understand that their data holds the key to unlocking that potential. But they also see risks alongside the opportunities—and those risks can’t be ignored. The truth is, technology is advancing faster than many businesses feel ready to adopt it. Bridging that gap between innovation and trust will be critical for unlocking AI’s full potential. So, how do we do that? It comes down understanding, acknowledging and addressing the barriers to AI adoption facing SMBs today: 1. Inflated expectations Companies are promised that AI will revolutionize their business. But when they adopt new AI tools, the reality falls short. Many use cases feel novel, not necessary. And that leads to low repeat usage and high skepticism. For scaling companies with limited resources and big ambitions, AI needs to deliver real value – not just hype. 2. Complex setups Many AI solutions are too complex, requiring armies of consultants to build and train custom tools. That might be ok if you’re a large enterprise. But for everyone else it’s a barrier to getting started, let alone driving adoption. SMBs need AI that works out of the box and integrates seamlessly into the flow of work – from the start. 3. Data privacy concerns Remember the quote I shared earlier? SMBs worry their proprietary data could be exposed and even used against them by competitors. Sharing data with AI tools feels too risky (especially tools that rely on third-party platforms). And that’s a barrier to usage. AI adoption starts with trust, and SMBs need absolute confidence that their data is secure – no exceptions. If 2024 was the year when SMBs saw AI’s potential from afar, 2025 will be the year when they unlock that potential for themselves. That starts by tackling barriers to AI adoption with products that provide immediate value, not inflated hype. Products that offer simplicity, not complexity (or consultants!). Products with security that’s rigorous, not risky. That’s what we’re building at HubSpot, and I’m excited to see what scaling companies do with the full potential of AI at their fingertips this year!

So, here’s a quick story about how I managed to take our app ratings at airtel from a 3.2 to a solid 4.3 in just 30 days. I was on a call with our account executive at MoEngage where we were discussing the RFM model. If you’re not familiar, RFM stands for Recency, Frequency, Monetization—it’s basically a way to understand customer behavior based on how often they use the app, how recently they’ve been active, and if they’ve made any purchases. After the call, I started thinking—how can we use this data beyond just targeting users for offers or notifications? And then it clicked: we could use this to improve our app ratings. Here’s what I did next: instead of showing the app rating prompt to everyone (which was clearly not working), I decided to get more specific. I created a segment of users who were really engaged—people who were listening music for at least 20-30 minutes a day and opening the app 5-6 times daily. These were our power users, the ones who were already loving the app. But I didn’t just stop there. I made sure the rating prompt would only pop up after an “aha moment,” like after they listened to five songs or changed their hello tune. I wanted to catch them at a high point when they were already feeling good about their experience. Plus, we capped the prompt to only show up once a week, so we weren’t bombarding them. And guess what? It worked! By focusing on the users who were most likely to give us positive feedback, we managed to take our ratings from 3.2 to 4.3 in just a month. It was all about understanding who to ask, when to ask, and how to make that moment feel seamless.

🧑🏼 How To Design Better Personas In UX (https://lnkd.in/eGPXmPNZ), a step-by-step guide to reduce decoration and add meaningful data to make personas more helpful and effective. Neatly put together by Slava Shestopalov. ✅ We need to know who users are and what they need to do. ✅ We can use both personas and Jobs-to-Be-Done for that. 🤔 They serve different purposes and focus on different things. ✅ Jobs-to-Be-Done focuses on user needs and outcomes. ✅ Personas focus on users, their behavior and mental model. ✅ Useful personas emerge from profound user research. ✅ They help visualize users, their goals and motivation. 🚫 Don’t focus on demographics to avoid stereotypes. ✅ Include the way of thinking, background, “a day in life”. ✅ Always add at least one persona with a disability. ✅ Add a story, pain points and how they use your product. ✅ List user’s habits/products they use daily, often and rarely. ✅ Finally, add needs, wants and fears mentioned by users. ✅ Then, prioritize key points for each role in your team. We often speak about personas being an outdated tool, successfully replaced by Jobs-to-Be-Done. Yet often in practice they are compatible. Both move the focus to user needs, yet they shed light onto user from different perspectives. Knowing how users think, behave and feel is as important as what they do. As Page Laubheimer noted, personas help remove box-checking mentality. They tell a story of the customer, what their environment is, what their habits are, the tools they use daily — and give product teams a way to think about users in a much more approachable and tangible way. Ultimately, use what works for you and for your team: just make sure that the user details aren’t invented, and root in actual research with actual customers. Useful resources: Personas vs. Jobs-to-Be-Done, by Page Laubheimer https://lnkd.in/eHA2Ft4J A Guide To Building Personas For UX, by Maze https://lnkd.in/ehCzACZW Personas for UX, Product, and Design Teams, by UserInterviews https://lnkd.in/eeE3pVUK A Simple Guide To Personas, by Rikke Friis Dam, Yu Siang Teo https://lnkd.in/eRA52v5m Five-Steps Framework for Building Better Personas, by Nikki Anderson, MA https://lnkd.in/eGWpqkdz Fixing User Personas, by Jordan Bowman https://lnkd.in/eDPCr63Q Personas Make Users Memorable, by Aurora Harley https://lnkd.in/eh-PYMxc A Closer Look At Personas (A Series), by Mo Goltz https://lnkd.in/eGqbr9wy https://lnkd.in/eBDsSsaR #ux #design #research

Stuck in an endless loop of client changes? Lost track of what revision this constitutes? Yeah. Been there. Done that. The secret? It's not about saying no. It's about saying yes to the right things upfront. Every project that goes sideways starts the same way: Vague agreements. Fuzzy boundaries. Good intentions. Six weeks later you're bleeding money and everyone's frustrated. Here's my framework after 30 years of running two 8-figure businesses: The SOW is your salvation. Not some boilerplate template. A real document that covers: • Exact deliverables (not "design work" but "3 homepage concepts, 2 rounds of revisions") • Hours of operation ("We respond M-F, 9-5 PST. Weekend requests get Monday responses") • Revision rounds spelled out ("Round 1 includes up to 5 changes. Round 2 includes 3.") • Feedback cycles defined ("48-hour turnaround for client feedback or the project may be delayed or additional fees may be incurred") But here's what most people miss— Don't work on client notes immediately. Client sends 37 pieces of feedback at 11pm Friday? Producer sends conflicting notes from the CEO? Marketing wants one thing, sales wants another? Stop. Collect everything first. Resolve the conflicts. Get on the phone and discuss it with your client to get alignment. Separate the "have to haves" from the "nice to haves". Then present unified changes. "Based on all feedback received, here are the 8 changes we'll implement. This constitutes revision round 2 of 3." Watch how fast the random requests stop. No extra work that goes unappreciated. No more feelings of being taken advantage of. Communicate before the crisis, prevents the crisis from happening. "Just so you know, we're entering round 2. You have one more included. After that, it's $X per additional round." No surprises. No awkward money conversations. No resentment. Scope creep isn't a them problem. It's a you problem. And that's good news, because that means you are in control. They're not trying to take advantage. They just don't know where the boundaries are because you never drew them. Draw the lines early. Communicate them clearly. Everyone wins. What's your most painful scope creep story? What boundary would've prevented it? Small Business Builders #projectmanagement #clientmanagement #businessgrowth

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?

My biggest takeaways from Jason Cohen: 1. “Too expensive” is never the real reason customers cancel. They already saw your pricing and decided to buy, so something else changed. When customers cite price, dig deeper—the actual reason might be changing needs, integration issues, or feature gaps. 2. Ask “What made you cancel?” instead of “Why did you cancel?” Jason tested both phrasings and saw response quality double with the “what made you” version. The first version directs attention to the product or situation and invites one-word deflections like “budget.” 3. Most companies undercharge because they just guessed at pricing and never revisited it. One founder selling to enterprise charged $300 per year, and Jason advised switching to $300 per month. Signups stayed exactly the same. When you 12x your price and conversion doesn’t budge, you’re not even close to finding the right number yet. 4. Pricing selects your market more than it signals value. When your product costs too little, larger companies assume it can’t be serious: not mature enough, no governance policies, inadequate support. Raise prices and you don’t necessarily lose customers; you enter a different market segment where your price signals credibility. The founder who went from $300 per year to $300 per month and saw no change in signups—he just shifted who was buying. 5. Your churn rate sets a ceiling on your business that most founders underestimate. The math is simple: divide your monthly new customers by your monthly cancellation rate, and that’s the maximum number of customers you will ever have. This is why logo churn is the first metric to examine when growth stalls. 6. Onboarding is the highest-leverage lever to reduce churn. Small improvements in the first 30 days compound into retention gains over the customer lifetime. When you don’t know where to start on retention, start with onboarding. 7. Positioning can allow you to charge an order of magnitude more without changing your product. The same exact product can command higher prices depending on how you frame it. “Cut your ad costs in half” caps what customers will pay—they’ll only give you a fraction of the savings you drive them. While “double your leads” aligns with what executives actually want and opens a much higher budget. CEOs reward growth; they merely acknowledge cost savings. 8. Sometimes the right answer is accepting that not growing is OK. If you’ve optimized churn, pricing, NRR, and channels, maybe growth has natural limits. Bootstrap founders reach a point where healthy annual dividends make further scaling optional. The question “Do you need to grow?” deserves honest examination—not because stasis is fine but because growth at all costs can destroy what made the company good.

The Brutal Truth About Consumer Trust in Home Care Why do some brands inspire trust effortlessly while others struggle to convince consumers? Home care isn’t like beauty or food, where customers instinctively check labels. For decades, legacy brands have relied on familiarity over transparency—building trust through big advertising spends rather than real ingredient disclosures. But that’s changing. Consumer trust is now shifting toward brands that disclose, educate, and take a stand. 1️⃣ The Parle-G Effect: Legacy Trust vs. New-Age Transparency For years, people have trusted brands like Surf Excel, Vim, and Harpic—not because they knew what was inside, but because they were always there on shelves and TV screens. This is the "Parle-G effect"—familiarity breeds trust. But today, trust is no longer inherited; it’s earned. The rise of brands like Kapiva (Ayurveda transparency), The Whole Truth (ingredient honesty) shows how modern brands build trust differently—by being upfront about what’s inside. 2️⃣ The Johnson & Johnson Shock: When Legacy Trust Breaks For decades, J&J was the gold standard for baby care. But lawsuits over talcum powder contamination with asbestos shattered consumer confidence worldwide. Even in India, brands like Mother Sparsh surged because young parents started reading labels—they no longer assumed safety just because a product was from a heritage brand. 3️⃣ The Patanjali vs. FSSAI Scandal: Why Trust Must Be Backed by Proof Consumers initially believed in Patanjali’s “natural” positioning. But repeated quality violations (like the recent FSSAI crackdown on misleading claims) eroded trust. The lesson? Trust cannot be built on slogans alone. If a brand claims toxin-free, natural, or safe—it must prove it consistently. 4️⃣ The Decathlon & Ikea Strategy: Trust Through Radical Transparency Decathlon shares detailed product breakdowns—how much polyester is used, where a product is made, and even the carbon footprint. Customers trust them because they don’t have to “guess” what they’re buying. Ikea lists every material, every environmental impact, and even assembly instructions upfront. No surprises. Just facts. In home care, Koparo is taking the same approach—putting ingredients front and center. Not just saying "toxin-free," but explaining why certain ingredients matter for better or worse (like the bioaccumulation of harmful chemicals in traditional cleaners). So What’s Next for Consumer Trust in Home Care? ✅ Brands that educate will win over brands that advertise. ✅ Ingredient transparency will become a non-negotiable (just like food labels). ✅ Consumers will demand not just safe products—but proof of safety. At Koparo, we’re all in on radical transparency. No vague claims. No marketing gimmicks. Just home care that’s safe, effective, and backed by science. The real question is—do you know what’s inside your cleaning products? #ToxinFree #Koparo #HomeCareRevolution 🚀