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.

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.

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 ]

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!

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.

⏱️ How To Measure UX (https://lnkd.in/e5ueDtZY), a practical guide on how to use UX benchmarking, SUS, SUPR-Q, UMUX-LITE, CES, UEQ to eliminate bias and gather statistically reliable results — with useful templates and resources. By Roman Videnov. Measuring UX is mostly about showing cause and effect. Of course, management wants to do more of what has already worked — and it typically wants to see ROI > 5%. But the return is more than just increased revenue. It’s also reduced costs, expenses and mitigated risk. And UX is an incredibly affordable yet impactful way to achieve it. Good design decisions are intentional. They aren’t guesses or personal preferences. They are deliberate and measurable. Over the last years, I’ve been setting ups design KPIs in teams to inform and guide design decisions. Here are some examples: 1. Top tasks success > 80% (for critical tasks) 2. Time to complete top tasks < 60s (for critical tasks) 3. Time to first success < 90s (for onboarding) 4. Time to candidates < 120s (nav + filtering in eCommerce) 5. Time to top candidate < 120s (for feature comparison) 6. Time to hit the limit of free tier < 7d (for upgrades) 7. Presets/templates usage > 80% per user (to boost efficiency) 8. Filters used per session > 5 per user (quality of filtering) 9. Feature adoption rate > 80% (usage of a new feature per user) 10. Time to pricing quote < 2 weeks (for B2B systems) 11. Application processing time < 2 weeks (online banking) 12. Default settings correction < 10% (quality of defaults) 13. Search results quality > 80% (for top 100 most popular queries) 14. Service desk inquiries < 35/week (poor design → more inquiries) 15. Form input accuracy ≈ 100% (user input in forms) 16. Time to final price < 45s (for eCommerce) 17. Password recovery frequency < 5% per user (for auth) 18. Fake email frequency < 2% (for email newsletters) 19. First contact resolution < 85% (quality of service desk replies) 20. “Turn-around” score < 1 week (frustrated users → happy users) 21. Environmental impact < 0.3g/page request (sustainability) 22. Frustration score < 5% (AUS + SUS/SUPR-Q + Lighthouse) 23. System Usability Scale > 75 (overall usability) 24. Accessible Usability Scale (AUS) > 75 (accessibility) 25. Core Web Vitals ≈ 100% (performance) Each team works with 3–4 local design KPIs that reflects the impact of their work, and 3–4 global design KPIs mapped against touchpoints in a customer journey. Search team works with search quality score, onboarding team works with time to success, authentication team works with password recovery rate. What gets measured, gets better. And it gives you the data you need to monitor and visualize the impact of your design work. Once it becomes a second nature of your process, not only will you have an easier time for getting buy-in, but also build enough trust to boost UX in a company with low UX maturity. [more in the comments ↓] #ux #metrics

6 out of 10 brands I audit struggle to convert visitors. At best, achieving a 1% conversion rate. The culprit? - templated product pages - benefits not highlighted - not-so-intuitive design Making the shopping experience forgetful. A memorable experience is key to converting visitors.  Especially if you drive traffic to product pages. Because when someone is viewing your product, they are likely seeing other brands too. In this example, using Hawaii Coffee's PDP, I've made changes that make the shopping experience memorable and increase the conversion rate. Below are the 8 changes I recommend - 1. Adding a short product description. This should show the brand's personality and tell the shopper something valuable about the product. 2. Using an image that catches attention. This is key. Use an image that represents your brand's personality. 3. Highlighting key selling points of the product. These should be placed before the add-on cart and should be easy to read. 4. Making sure the options are clear. If you're selling different variants or sizes, make sure the user knows which one's best for them. Make this super clear. 5. Highlighting why someone should subscribe and not just purchase one time. Basically, your subscription USPs. Making the above changes gave me more space to: 6. Add a short description that builds trust in the brand and product. Especially for new visitors who are not familiar with you. 7. Add FAQs. These are essential for any product (other than fashion, probably). They are great for SEO and answering all shopper questions. 8. Add USPs with icons. These are reasons why you should trust the brand and why the product is great. Other UX changes I made: - Removed the image thumbnails - Moved price close to the product name - Added the weight next to price to show value - Added service USPs below add-to-cart CTA Found this useful? Let me know in the comments! P.S. I haven't posted on LinkedIn in a while. And it's for a good reason. I was writing my Practical Guide to CRO e-book. Which is launching next week. It includes my processes, tools, techniques – everything you need to become a pro at CRO. If you're interested, comment "e-book" and I'll personally send you a link to buy it. #conversionrateoptimization

Cold digital interactions will destroy your D2C brand. Your beauty product is at risk of failing if you don’t address this. Industry is rooted in enhancing self-image and boosting confidence, goals that are inherently emotional. While online shopping is undeniably convenient, it often comes at the expense of personal interaction. This is especially problematic in the beauty sector, where purchasing decisions are often influenced by sensory experiences, personalized recommendations, and emotional connections. +64% consumers believe brands are losing touch with customer experience. +85% higher sales achieved by brands that emotionally engage. +68% women & 56% men choose beauty products based on how they make them feel. >>Online emotional challenges << →Lack of personalization in online transactions. E-commerce lacks the personal engagement of in-store shopping, such as trying products and consulting with beauty experts. →Absence of sensory experiences. Customers are unable to explore key sensory elements like texture, scent, and application when shopping online. →Overwhelming variety. The sheer number of options online can confuse customers and lead to decision fatigue without proper guidance. >>Strategies to build emotional connections<< →Transform brick and mortar stores into immersive experiences. Redefine your physical stores as experiential hubs where customers can enjoy personalized consultations, interactive product trials, and even beauty treatments. +30% boost in sales is seen in flagship stores that offer immersive experiences. →Retail as entertainment, retailtainment. Host creative pop-up shops, in-store events, and experiential retail activations to engage customers emotionally with unique deco, exclusive product offerings, and hands-on activities. +70% of beauty consumers value in-store experiences over online alternatives. →Leverage influencer trust. Partner with influencers who bring authenticity to your brand by sharing personal stories, reviews, and tutorials. Their relatability and trustworthiness create stronger emotional ties with consumers. +49% customers rely on influencer recommendations for beauty product purchases. →Build community through social media. Use social media to foster continuous engagement through live Q&A sessions, interactive content, user-generated campaigns, and community forums. +72% of beauty consumers discover products through social media. To finish. Despite the challenges of the digital era, the industry is finding ways to close the emotional gap with creative solutions like flagship experiences, influencer collaborations, virtual consultations, and community-driven marketing. Check out my curated collection of visuals to spark your next big idea. Featured brands: Benefit Glossier Kylie Cosmetics Marc Jacobs Molecula Miu Miu Nina Ricci Laneige Rhode #BeautyBusiness #EmotionalConnection #SocialBeauty #ExperientialRetail #InfluencerMarketing

Technology Didn't Disrupt Banking. Behaviour Did. Think about the last time you made a payment at your local grocery store. Chances are, you didn't reach for your wallet. Instead, you reached for your phone, scanned a QR code and payment was done. We often give credit to algorithms and digital apps for transforming how India banks and borrows. But what I have observed over the years in financial services is that technology only unlocks the door. It is the people who decide whether to walk through it. The real turning point wasn't the launch of a new platform or a regulatory push. It was the quiet moment when a kirana owner, a college student, a first-time borrower, decided to trust a screen with their money. That shift in mindset changed everything. I have seen customers evolve from insisting on branch visits for every transaction or taking entries in physical passbooks to now managing loans, investments, and payments entirely from their phones, without a second thought. What truly moved them was not the technology. It was confidence. Familiarity. A friend's recommendation. A seamless experience that didn't let them down the first time. India's financial sector has grown not because we built sophisticated systems but because millions of people gradually chose to believe in them. Technology enables. Trust transforms. The next chapter of financial inclusion won’t be written in code alone. It will be written in the choices that Indians continue to make every day, one transaction at a time.