Cross-Platform UX Strategies

Over the last year, I’ve seen many people fall into the same trap: They launch an AI-powered agent (chatbot, assistant, support tool, etc.)… But only track surface-level KPIs — like response time or number of users. That’s not enough. To create AI systems that actually deliver value, we need 𝗵𝗼𝗹𝗶𝘀𝘁𝗶𝗰, 𝗵𝘂𝗺𝗮𝗻-𝗰𝗲𝗻𝘁𝗿𝗶𝗰 𝗺𝗲𝘁𝗿𝗶𝗰𝘀 that reflect: • User trust • Task success • Business impact • Experience quality    This infographic highlights 15 𝘦𝘴𝘴𝘦𝘯𝘵𝘪𝘢𝘭 dimensions to consider: ↳ 𝗥𝗲𝘀𝗽𝗼𝗻𝘀𝗲 𝗔𝗰𝗰𝘂𝗿𝗮𝗰𝘆 — Are your AI answers actually useful and correct? ↳ 𝗧𝗮𝘀𝗸 𝗖𝗼𝗺𝗽𝗹𝗲𝘁𝗶𝗼𝗻 𝗥𝗮𝘁𝗲 — Can the agent complete full workflows, not just answer trivia? ↳ 𝗟𝗮𝘁𝗲𝗻𝗰𝘆 — Response speed still matters, especially in production. ↳ 𝗨𝘀𝗲𝗿 𝗘𝗻𝗴𝗮𝗴𝗲𝗺𝗲𝗻𝘁 — How often are users returning or interacting meaningfully? ↳ 𝗦𝘂𝗰𝗰𝗲𝘀𝘀 𝗥𝗮𝘁𝗲 — Did the user achieve their goal? This is your north star. ↳ 𝗘𝗿𝗿𝗼𝗿 𝗥𝗮𝘁𝗲 — Irrelevant or wrong responses? That’s friction. ↳ 𝗦𝗲𝘀𝘀𝗶𝗼𝗻 𝗗𝘂𝗿𝗮𝘁𝗶𝗼𝗻 — Longer isn’t always better — it depends on the goal. ↳ 𝗨𝘀𝗲𝗿 𝗥𝗲𝘁𝗲𝗻𝘁𝗶𝗼𝗻 — Are users coming back 𝘢𝘧𝘵𝘦𝘳 the first experience? ↳ 𝗖𝗼𝘀𝘁 𝗽𝗲𝗿 𝗜𝗻𝘁𝗲𝗿𝗮𝗰𝘁𝗶𝗼𝗻 — Especially critical at scale. Budget-wise agents win. ↳ 𝗖𝗼𝗻𝘃𝗲𝗿𝘀𝗮𝘁𝗶𝗼𝗻 𝗗𝗲𝗽𝘁𝗵 — Can the agent handle follow-ups and multi-turn dialogue? ↳ 𝗨𝘀𝗲𝗿 𝗦𝗮𝘁𝗶𝘀𝗳𝗮𝗰𝘁𝗶𝗼𝗻 𝗦𝗰𝗼𝗿𝗲 — Feedback from actual users is gold. ↳ 𝗖𝗼𝗻𝘁𝗲𝘅𝘁𝘂𝗮𝗹 𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴 — Can your AI 𝘳𝘦𝘮𝘦𝘮𝘣𝘦𝘳 𝘢𝘯𝘥 𝘳𝘦𝘧𝘦𝘳 to earlier inputs? ↳ 𝗦𝗰𝗮𝗹𝗮𝗯𝗶𝗹𝗶𝘁𝘆 — Can it handle volume 𝘸𝘪𝘵𝘩𝘰𝘶𝘵 degrading performance? ↳ 𝗞𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹 𝗘𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆 — This is key for RAG-based agents. ↳ 𝗔𝗱𝗮𝗽𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗦𝗰𝗼𝗿𝗲 — Is your AI learning and improving over time? If you're building or managing AI agents — bookmark this. Whether it's a support bot, GenAI assistant, or a multi-agent system — these are the metrics that will shape real-world success. 𝗗𝗶𝗱 𝗜 𝗺𝗶𝘀𝘀 𝗮𝗻𝘆 𝗰𝗿𝗶𝘁𝗶𝗰𝗮𝗹 𝗼𝗻𝗲𝘀 𝘆𝗼𝘂 𝘂𝘀𝗲 𝗶𝗻 𝘆𝗼𝘂𝗿 𝗽𝗿𝗼𝗷𝗲𝗰𝘁𝘀? Let’s make this list even stronger — drop your thoughts 👇

🧪 Atomic Design: Building UI Systems That Scale Designing great interfaces isn’t just about making screens look good — it’s about building a system that stays consistent, scalable, and easy to maintain as your product grows. That’s where Atomic Design by Brad Frost comes in — a brilliant methodology that helps UX/UI specialists create robust, modular design systems — not just isolated pages. Here’s how it breaks down: 🔹 Atoms – The smallest building blocks of UI: buttons, inputs, labels. 🔹 Molecules – Groups of atoms forming small functional components (e.g., a search bar with label + input + button). 🔹 Organisms – Larger interface sections made of molecules & atoms, like headers or cards. 🔹 Templates – Page-level layouts that arrange organisms & define content hierarchy. 🔹 Pages – Fully realized screens with real content where the user experience is validated. ✨ Why it matters: Atomic Design gives teams a shared design language, ensures consistency across screens, and allows for scalable growth — so you spend less time fixing inconsistencies and more time improving the user experience. 💬 Whether you're designing a startup MVP or a global product, thinking in systems (not screens) is the fastest way to build cohesive, future-proof designs. ❤️ Save this post for your next design sprint. 🔁 Share with your design team and start speaking the same visual language today. #UXDesign #UIDesign #AtomicDesign #DesignSystems #ComponentDesign #ScalableUI #ProductDesign #UXStrategy #AtomicDesignMethodology #DesignThinking

Exciting research from Snap Inc.'s engineering team! Just came across their paper on Universal User Modeling (UUM) that's revolutionizing how they handle cross-domain user representations. The team at Snap has developed a framework that learns general-purpose user representations by leveraging behaviors across multiple in-app surfaces simultaneously. Rather than building separate user models for each surface (Content, Ads, Lens, etc.) and combining them post-hoc, UUM directly captures collaborative filtering signals across domains. Their approach formulates this as a cross-domain sequential recommendation problem, processing user interaction sequences of up to 5,000 events and using sliding windows of 800-length subsequences to balance computational efficiency with capturing long-range dependencies. The architecture leverages transformer-based self-attention mechanisms to model these sequences, with a clever design that projects feature vectors from different domains into a shared latent space before applying multi-head attention layers. The results are impressive! After successful A/B testing, UUM has been deployed in production with significant gains: - 2.78% increase in Long-form Video Open Rate - 19.2% increase in Long-form Video View Time - 1.76% increase in Lens play time - 0.87% increase in Notification Open Rate They're also exploring advanced modeling techniques like domain-specific encoders and self-attention with information bottlenecks to address the challenges of imbalanced cross-domain data. This work demonstrates how sophisticated user modeling can drive substantial engagement improvements across multiple recommendation surfaces within a large-scale social platform.

💡Combining Design Thinking, Lean UX, and Agile A combination of Design Thinking, Lean UX, and Agile methodologies offers a powerful approach to product development—it helps balance user-centered design with efficient concept validation and iterative product development. 1️⃣ User-centered foundation (Design Thinking): Begin by understanding the needs, emotions, and problems of the end-users. ✔ Start by conducting user research to identify and understand user needs. ✔ Gather insights through direct interaction with users (e.g., through interviews, surveys, etc.). Spend time understanding users' behavior, focusing on "why" rather than "what" they do. ✔ After gathering research, prioritize the most critical user insights to guide your design focus. Create a 2x2 matrix to prioritize insights based on impact (high vs low business impact) and feasibility (easy vs hard to implement) ✔ Begin brainstorming potential solutions based on these prioritized insights and formulate a hypothesis. Encourage cross-functional collaboration during brainstorming sessions to generate diverse ideas. 2️⃣ Hypothesis-driven testing (Lean UX): Lean UX helps quickly validate key assumptions. It fits perfectly between Design Thinking's ideation and Agile's development processes, ensuring that critical hypothesis are validated with users before actual development started. ✔ Formulate a testable hypothesis around a potential solution that addresses the user needs uncovered in the Design Thinking phase. ✔ Conduct experiment—develop a Minimum Viable Product (https://lnkd.in/dQg_siZG) to test the hypothesis. Build just enough functionality to test your hypothesis—focus on speed and simplicity. ✔ Based on the experiment's outcome, refine or revise the hypothesis and repeat the cycle. 3️⃣ Iterative product development (Agile): Once the Lean UX process produces validated concepts, Agile takes over for incremental development. Agile's iterative sprints will help you continuously build, test, and refine the concept. Agile complements Lean UX by providing the structure for frequent releases, allowing teams to adapt and deliver value consistently. ✔ Break down work into small, manageable chunks that can be delivered iteratively. ✔ Embrace iterative development—continue refining your product through iterative build-measure-learn sprints. Keep the user feedback loop tight by involving users in sprint reviews or testing sessions. ✔ Gather user feedback after each sprint and adapt the product according to the findings. Measure user satisfaction and track usability metrics to ensure improvements align with user needs. 🖼️ Design thinking, Lean UX and Agile better together by Dave Landis #UX #agile #designthinking #productdesign #leanux #lean  

10 Things I Learned Working with EY, BCG, and IBM on UX at Scale I never planned to work on UX at a scale where one insight could affect thousands of users across continents. But EY taught me structure. BCG taught me clarity. IBM taught me scale. Together they shaped the way I design and think as a UX Researcher and Design Manager. Here are the lessons I wish someone had told me earlier. 1. Enterprise UX is not glamorous. It is impact driven. At EY I learned that most wins come from hidden workflows nobody talks about. One navigation change can save a team hours every week. 2. Research beats assumptions every single time. Task analysis. Personas. Heuristics. Accessibility reviews. These were not deliverables for me. They were decision making tools. They kept projects grounded when opinions were loud. 3. The real bottleneck is usually not the interface. It is the workflow. At BCG we rebuilt internal tools to eliminate duplication and complexity. Result was a 90 percent reduction in repeat work and 30 percent faster proposal creation. Scale comes from removing friction. 4. Consistency protects the experience. Multiple teams. Multiple countries. One unified product. Consistency reduces learning curves and increases trust. 5. Accessibility is not optional. It is foundational. Following Section 508 and WCAG at EY and IBM changed how I see interfaces. Inclusive design is efficient design. 6. UX at scale requires negotiation not perfection. You balance deadlines. Teams. Engineering constraints. Good UX is often the most realistic option that still respects the user. 7. Research insights are only valuable if people act on them. I learned to translate research into business terms. Retention. Revenue. Efficiency. That is how UX earns a seat at the strategy table. 8. Small ideas can shift entire ecosystems. Like a content checklist that increased traffic by 5 percent. Or a simple “Download Font” button that reduced issues from 50 percent to 11 percent. Tiny changes. Large ripples. 9. Service design is the missing piece in many organisations. Airline journeys. EdTech classrooms. Global M&A teams. When you study real journeys end to end the gaps become obvious. 10. The bigger the organisation the more important empathy becomes. People do not resist technology. They resist uncertainty. Design becomes the bridge between human comfort and system capability. Key reminders I carry even today • Users do not care about your process. They care about outcomes. • Enterprise UX is slow but the impact is deep. • Accessibility is everyone’s responsibility. • Research is the fastest way to alignment. • The best designs disappear into the workflow.

Traditional usability tests often treat user experience factors in isolation, as if different factors like usability, trust, and satisfaction are independent of each other. But in reality, they are deeply interconnected. By analyzing each factor separately, we miss the big picture - how these elements interact and shape user behavior. This is where Structural Equation Modeling (SEM) can be incredibly helpful. Instead of looking at single data points, SEM maps out the relationships between key UX variables, showing how they influence each other. It helps UX teams move beyond surface-level insights and truly understand what drives engagement. For example, usability might directly impact trust, which in turn boosts satisfaction and leads to higher engagement. Traditional methods might capture these factors separately, but SEM reveals the full story by quantifying their connections. SEM also enhances predictive modeling. By integrating techniques like Artificial Neural Networks (ANN), it helps forecast how users will react to design changes before they are implemented. Instead of relying on intuition, teams can test different scenarios and choose the most effective approach. Another advantage is mediation and moderation analysis. UX researchers often know that certain factors influence engagement, but SEM explains how and why. Does trust increase retention, or is it satisfaction that plays the bigger role? These insights help prioritize what really matters. Finally, SEM combined with Necessary Condition Analysis (NCA) identifies UX elements that are absolutely essential for engagement. This ensures that teams focus resources on factors that truly move the needle rather than making small, isolated tweaks with minimal impact.

We do not experience the world in neat, discrete categories, yet much of UX research still measures behavior as if we do. Real experiences exist in the gray zone where satisfaction, trust, confusion, effort, and motivation overlap rather than fall into clean categories. When we compress this psychological complexity into Likert scales or binary outcomes, we lose the intensity and uncertainty that often signal early friction and churn. Most classic UX metrics summarize what users select, not what they actually feel. A single satisfaction score can hide hesitation, mixed emotions, and declining confidence, even though these blended states drive real behavioral change. By forcing fluid cognition into rigid buckets, we frame experience as static when in reality it is continuously evolving. Fuzzy logic approaches UX measurement differently by modeling experience as degrees of membership instead of fixed categories. Using membership functions, telemetry and survey inputs become graded psychological states in which multiple conditions coexist at once. Cognitive load, trust, frustration, and engagement are not treated as on–off switches but as overlapping mental states, allowing UX researchers to detect subtle tensions long before they appear as abandonment or negative feedback. Traditional regression assumes linear relationships and independence between variables, while ANOVA struggles to integrate many experiential dimensions into a single coherent signal. Fuzzy inference systems naturally combine correlated inputs into holistic experience indices, and through defuzzification these blended psychological states become continuous, actionable metrics such as friction levels or churn risk scores that support proportionate design responses instead of blunt thresholds. You might think Likert scales already work like fuzzy logic because they use graded numbers, but they are fundamentally different. Likert forces users to choose a single category, compressing mixed emotions into one number. When we later average scores or run regressions, we treat those values as if they represent continuous psychological intensity, even though the underlying uncertainty has already been removed at the moment of response. Fuzzy logic does the opposite. It preserves uncertainty instead of eliminating it, allowing users to belong partially to multiple psychological states at the same time. A person can be modeled as 70% satisfied, 20% neutral, and 10% confused simultaneously, rather than being forced into selecting whichever single box feels closest. Fuzzy logic does not replace traditional statistics, but it fills the gap where human psychology is layered, nonlinear, and ambiguous. Likert tells us which box users pick, classical statistics compare group averages, but fuzzy logic models how experience actually unfolds inside the mind, enabling UX research to move from static description toward psychologically grounded prediction and adaptive design.

A new report from FT Strategies and smartocto reveals how newsrooms are increasing relevance, engagement and revenue by focusing on why readers consume news rather than what journalists think is important If you are looking to implement the user needs model, here are the 4 key steps: 🧱 Set the foundations Training staff to understand user needs helps them view content creation from the audience’s perspective. Implementing analytics tools allows you to track how content meets specific user needs. Ongoing monitoring of metrics like page views, attention time and article reads provides valuable feedback for adjusting your strategy. 💎 Define your most relevant user needs Select the needs that align with your brand and audience profile to create content that resonates more deeply. For example, lifestyle publications might focus on inspiring readers, while business publications might prioritise providing perspective. Establishing clear key performance indicators ensures you can measure impact in alignment with your overall goals. 📊 Track and monitor article performance Create a structured tagging system aligned with user needs to assess which content types resonate most with your audience. Use dashboards and notifications to maintain a broad view of content performance across different categories. Generate comprehensive reports that track improvements over time rather than focusing solely on standalone metrics. 🧪 Develop data-driven experiments Form hypotheses about which combinations of user needs, topics and formats work best for your specific audience. Test adjustments to your content mix based on engagement data to refine your approach. Set monthly goals for content output aligned with your key performance indicators to track progress consistently. Read the full piece here: https://lnkd.in/emqbeE8h

🧩 Design Systems: The Blueprint for Consistent UX 🛠️ 🔍 Introduction: A design system is much more than just a collection of UI elements—it's a comprehensive guide that ensures consistency across all aspects of a product's user experience. By establishing a unified design language, a design system helps teams create cohesive and scalable interfaces that users will love. 📈 Benefits of Design Systems: 1) Consistency: Design systems ensure a uniform look and feel across all platforms, enhancing user trust and familiarity. 2) Efficiency: With predefined components and guidelines, design teams can work faster, reducing the need to recreate elements from scratch. Scalability: As your product grows, a design system allows you to scale effortlessly, ensuring new features integrate seamlessly with existing ones. 📚 Key Components of a Design System: 1) Style Guides: Define the visual elements like colors, typography, and spacing that make up your brand’s identity. 2) Component Libraries: A collection of reusable UI components—buttons, forms, and navigation elements—that maintain consistency across different screens and applications. 3) Patterns and Guidelines: Best practices for interaction design, including how users should navigate and interact with your product. 🔧 Implementation and Maintenance Tips: 1) Start Small: Begin with the most critical components and gradually build out the system. 2) Collaborate Across Teams: Involve designers, developers, and product managers to ensure the design system meets everyone’s needs. 3) Regular Updates: Keep your design system up-to-date as your brand evolves, ensuring it remains relevant and effective. 🔍 Conclusion: A robust design system is a long-term investment that pays off by enhancing consistency, speeding up design processes, and making your product more scalable. It’s not just a tool—it’s the foundation for delivering a seamless user experience that stands the test of time. Ready to build a design system that scales with your product? Start laying the foundation today! #DesignSystems #UXDesign #Consistency #Scalability #DesignThinking

We've all heard the same customer journey model: Attract → convert → retain A linear, finite sequence. And that’s what we expected to analyse in our subscription benchmarking. But what we found was very different. When we looked at the NYT, Figma, Miro, Calm, Masterclass (and 95 others), we noticed that conversion almost never happens in one step. It happens through micro-conversions, each with its own goal. And more importantly, even conversion itself seems secondary. The real priority for those companies is to create value for their user as a first goal. To build engagement. To make the product feel indispensable before asking for money. The journey we observed doesn’t look like a line. It looks like a loop, reaching back to engage at every moment. Attract → Convert at level 0 (ex: account) → Onboarding & discovery → Engage → Convert at level 1 (trial or cheap offer) → Onboarding & discovery → Engage deeper → Convert at level 2 (full price, bundle)… and it keeps going. Max Moné dives into the topic in his 3rd episode of the Subscription Moodboard series on Audiencers: https://lnkd.in/eUYNvKmn