Data-Driven Layout Decisions

Instead of thinking of them as retail “floors,” retailers should think of them as under-optimized P&L levers. Leading retailers treat space like a living system, not a static planogram. Jamie Lawrence’s post makes the case for moving from instinct to intelligence. Here is how I see the next step. >>Start with signals: combine POS, traffic, dwell, basket, returns, and local demand into a single store–SKU–week truth set. If the data is not unified, the layout will always be a guess. >>Design in hours: treat planograms as code. Use digital twins, AR, and lightweight simulation to test flows and focal points before a single shelf moves. >>Put agents on the loop: agentic AI should watch real-time movement and sales, propose layout changes with expected lift and cost, then execute within guardrails. Human review for brand, compliance, and labor impact. Machines for iteration speed. >>Measure what finance cares about: - gross margin dollars per square foot - attachment and conversion - markdowns avoided - inventory turns and working capital released - labor hours per change

AI is no longer just decorating rooms. It’s redesigning how we live. AI can now rethink rooms, floors, and entire layouts—turning bold ideas into build-ready designs. Would you do floor like that? The data behind the shift: • 30–50% faster design cycles using generative layout tools • 100+ layout permutations generated from a single brief • Up to 20–30% improvement in space utilization • 10–25% energy savings when airflow, lighting, and thermal paths are simulated early • 40% fewer late-stage design changes thanks to digital testing What’s fundamentally different? AI treats floor plans like software systems: Pedestrian movement is simulated before construction Natural light and ventilation are optimized virtually Furniture, walls, and utilities are stress-tested digitally Cost, carbon footprint, and materials are optimized in parallel This enables: Smaller homes that feel larger Offices designed around productivity and wellbeing Buildings that adapt over time instead of aging poorly The biggest myth? AI replaces architects and designers. Reality: AI handles complexity and permutations. Humans focus on vision, culture, emotion, and identity. The future of architecture isn’t just smart. It’s generative, data-driven, and human-centric. #AI #Architecture #Design via @Visual Spaces Lab #PropTech #GenerativeAI #FutureOfLiving #SmartBuildings #Innovation

🍱 How To Design Effective Dashboard UX (+ Figma Kits). With practical techniques to drive accurate decisions with the right data. 🤔 Business decisions need reliable insights to support them. ✅ Good dashboards deliver relevant and unbiased insights. ✅ They require clean, well-organized, well-formatted data. ✅ Often packed in a tight grid, with little whitespace (if any). 🚫 Scrolling is inefficient in dashboards: makes comparing hard. ✅ Start with the audience and decisions they need to make. ✅ Study where, when and how the dashboard will be used. ✅ Study what metrics/data would support user’s decisions. ✅ Explore how to aggregate, organize and filter this data. ✅ More data → more filters/views, less data → single values. 🚫 Simpler ≠ better: match user expertise when choosing charts. ✅ Prioritize metrics: key insights → top left, rest → bottom right. ✅ Then set layout density: open, table, grouped or schematic. ✅ Add customizable presets, layouts, views + guides, videos. ✅ Next, sketch dashboards on paper, get feedback, iterate. When designing dashboards, the most damaging thing we can do is to oversimplify a complex domain, or mislead the audience. Our data must be complete and unbiased, our insights accurate and up-to-date, and our UI must match users’ varying levels of data literacy. Dashboard value is measured by useful actions it prompts. So invest most of the design time scrutinizing metrics needed to drive relevant insights. Bring data owners and developers early in the process. You will need their support to find sources, but also clean, verify, aggregate, organize and filter data. Good questions to ask: 🧭 What decisions do you want to be more informed on? (Purpose) 😤 What’s the hardest thing about these decisions? (Frustrations) 📊 Describe how you are making these decisions? (Sources) 🗃️ What data helps you make these decisions? (Metrics) 🧠 How much detail is needed for each metric? (Data literacy) 🚀 How often will you be using this dashboard? (Value) 🎲 What constraints should we know about? (Risks) And, most importantly, test dashboards repeatedly with actual users. Choose key tasks and see how successful users are. It won’t be right at first, but once you get beyond 80% success rate, your users might never leave your dashboard again. ✤ Dashboard Patterns + Figma Kits: Data Dashboards UX: https://lnkd.in/eticxU-N 👍 dYdX: https://lnkd.in/eUBScaHp 👍 Ethr: https://lnkd.in/eSTzcN7V Orange: https://lnkd.in/ewBJZcgC 👍 Semrush: https://lnkd.in/dUgWtwnu 👍 UKO: https://lnkd.in/eNFv2p_a 👍 Wireframing Kit: https://lnkd.in/esqRdDyi 👍 [continues in comments ↓]

Recent debate in the world of design finds ourselves confused between design as personal choice or the product of a well calculated UX strategy. It’s tempting to lean on aesthetics that feel “right” or ideas that align with personal taste. But when designing for business, it's crucial to look beyond what we like and focus on what works. Here’s why aligning design choices with KPIs and UX metrics drives results. Imagine designing a user interface based purely on color schemes we love or animations that feel fun. While personal style brings creativity to the table, it often lacks a strategic focus. For example, a designer might feel that an intricate navigation system looks sleek. But if UX metrics reveal high abandonment rates at navigation points, that “cool” design is clearly not resonating with users. Here, usability should trump aesthetics every time. KPIs (Key Performance Indicators) and UX metrics – like conversion rates, task success rates, or time-on-task – are not just data points. They’re our users’ voices, telling us what they need and expect. When a design aligns with these metrics, it speaks directly to user behavior and business objectives. This is where real value is created. Let’s prioritize intuitive, data-driven design that serves the user and meets business goals. Personal taste may spark inspiration, but data is what drives sustainable impact. Design that’s user-centered, measurable, and flexible isn’t just visually appealing; it’s strategically valuable. So, next time you face a design decision, ask yourself: Is this about personal taste, or does it align with key metrics? The answer might just change the way you design. 💡 #DesignThinking #UserExperience #UXMetrics #KPIs #ProductDesign

𝗧𝗵𝗶𝘀 𝗯𝘂𝗶𝗹𝗱 𝗳𝗿𝗼𝗺 Kelsey Oehrke 𝗶𝘀 𝗮 𝗴𝗿𝗲𝗮𝘁 𝗲𝘅𝗮𝗺𝗽𝗹𝗲 𝗼𝗳 𝗵𝗼𝘄 𝘁𝗼 𝘁𝘂𝗿𝗻 𝗼𝗻𝗲 𝘁𝗮𝗯𝗹𝗲 𝗶𝗻𝘁𝗼 𝗮 𝗳𝘂𝗹𝗹 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻 𝗰𝗼𝗰𝗸𝗽𝗶𝘁 𝗳𝗼𝗿 𝗽𝗿𝗼𝗳𝗶𝘁: 𝗖𝗮𝘁𝗲𝗴𝗼𝗿𝘆 ↔ 𝗟𝗼𝗰𝗮𝘁𝗶𝗼𝗻 𝘁𝗼𝗴𝗴𝗹𝗲 𝗼𝗻 𝘁𝗵𝗲 𝗹𝗲𝗳𝘁 Instead of 6 different tabs, a simple switch lets you re-use the same layout to answer totally different questions:  • “𝘞𝘩𝘪𝘤𝘩 𝘤𝘢𝘵𝘦𝘨𝘰𝘳𝘪𝘦𝘴 𝘮𝘢𝘬𝘦 𝘶𝘴 𝘮𝘰𝘯𝘦𝘺?”  • “𝘞𝘩𝘪𝘤𝘩 𝘭𝘰𝘤𝘢𝘵𝘪𝘰𝘯𝘴 𝘢𝘳𝘦 𝘱𝘶𝘭𝘭𝘪𝘯𝘨 𝘵𝘩𝘦𝘪𝘳 𝘸𝘦𝘪𝘨𝘩𝘵?” 𝗞𝗣𝗜 𝗰𝗮𝗿𝗱𝘀 𝘁𝗵𝗮𝘁 𝗳𝗿𝗮𝗺𝗲 𝘁𝗵𝗲 𝘀𝘁𝗼𝗿𝘆 Sales, profit, and orders each get:  • YTD value  • YoY change  • A tiny time-series to show direction, not just a number 𝗥𝗮𝗻𝗸𝗲𝗱 𝘁𝗮𝗯𝗹𝗲 𝘁𝗵𝗮𝘁 𝗱𝗼𝗲𝘀 𝘁𝗵𝗲 𝗿𝗲𝗮𝗹 𝘄𝗼𝗿𝗸 For each sub-category you see: YTD profit  • YoY % and $ change  • Monthly trend sparkline  • Current month performance 𝗧𝗵𝗮𝘁 𝗰𝗼𝗺𝗯𝗼 𝗹𝗲𝘁𝘀 𝗮 𝗺𝗮𝗻𝗮𝗴𝗲𝗿 𝗾𝘂𝗶𝗰𝗸𝗹𝘆 𝗮𝗻𝘀𝘄𝗲𝗿:  • Who’s over-performing? Double down.  • Who’s sliding? Fix pricing, promo, or inventory.  • Where is profit growing but orders aren’t (or vice versa)?  • No hunting through filters. No “export to Excel.” Just seeing the signal. 𝘐𝘧 𝘺𝘰𝘶’𝘳𝘦 𝘣𝘶𝘪𝘭𝘥𝘪𝘯𝘨 𝘛𝘢𝘣𝘭𝘦𝘢𝘶/𝘗𝘰𝘸𝘦𝘳 𝘉𝘐 𝘥𝘢𝘴𝘩𝘣𝘰𝘢𝘳𝘥𝘴 𝘧𝘰𝘳 𝘭𝘦𝘢𝘥𝘦𝘳𝘴𝘩𝘪𝘱, 𝘴𝘵𝘶𝘥𝘺 𝘵𝘩𝘪𝘴 𝘭𝘢𝘺𝘰𝘶𝘵: One table, layered metrics, small multiples, and a clear path to action. Nice work, Kelsey Oehrke 👏 — this is the kind of design that actually changes decisions, not just decorates a wall.

I used to spend hours drawing blueprints as an architect. Now AI is making this skill obsolete. The data behind the shift: → 30–50% faster design cycles using generative layout tools → 100+ layout permutations generated from a single brief → 20–30% improvement in space utilization → 10–25% energy savings when airflow, lighting, and thermal paths are simulated early → 40% fewer late-stage design changes thanks to digital testing What's fundamentally different? AI treats floor plans like software systems: → Pedestrian movement simulated before construction → Natural light and ventilation optimized virtually → Furniture, walls, and utilities stress-tested digitally → Cost, carbon footprint, and materials optimized in parallel This enables: → Smaller homes that feel larger → Offices designed around productivity and wellbeing → Buildings that adapt over time instead of aging poorly The biggest myth? AI replaces architects and designers. Reality: AI handles complexity and permutations. Humans focus on vision, culture, emotion, and identity. The future of architecture isn't just smart. It's generative, data-driven, and human-centric. ---- ♻️ Repost if your network needs to see this transformation ➕ Follow me (Basia Kubicka) for more AI insights 🔔 Subscribe to my newsletter for deep dives: https://air-scale.kit.com/ Opinions expressed are my own and do not represent the views, policies, or positions of my employer.

UNIQLO isn’t “decorating” stores. It’s running live experiments 🎉🎉🎉 #Uniqlo is using AI and granular customer data to constantly redesign store layouts so shoppers find what they want faster and end up buying more. How Uniqlo “reads” the store ✳️ Heat-mapping movement: Cameras and sensors track where customers slow down, which tables they ignore, what corners they walk past, and which colours catch the eye. !! ✳️ Journey data, not just sales: Uniqlo looks at dwell time, walking paths, try-ons, and conversion from “noticed” to “picked up” to “bought,” turning the store into a living dataset. !! What gets redesigned ✳️ Product placement: Best-selling colours and hero SKUs are shifted into “hot zones” with maximum footfall, while low-attention products are moved to eye level or end-caps to lift visibility. ✳️ Layout and fixtures: Tables, shelves, mannequins, and even aisle flow are re-arranged using patterns from thousands of observed shopping journeys. Data points powering decisions ✳️ In-store behaviour: Foot traffic heat maps, dwell time by zone, pick-up and try-on rates, fitting room conversion, and skipped areas. ✳️ Omnichannel signals: App and web impressions, clicks, page views, add-to-cart events, and purchase history are linked to profiles, then fed back into store assortment and displays. ✳️ Contextual data: Local weather, regional sales history, and even social media trends guide what to stock where and how prominently to feature it. Impact on buying ease Impact: ✳️ Less search, more discovery: Shoppers see relevant sizes, colours, and seasonal items in the natural path of movement, reducing choice paralysis and “where is this?” friction. ✳️ Higher attention → higher sales: By aligning layout with real behaviour instead of gut feel, Uniqlo reports smoother flow, better visibility for high-margin items, and reduced excess inventory.

𝗧𝗵𝗲 𝗺𝗼𝘀𝘁 𝗲𝘅𝗽𝗲𝗻𝘀𝗶𝘃𝗲 𝘀𝘁𝗼𝗿𝗮𝗴𝗲 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻 𝗺𝗼𝘀𝘁 𝘁𝗲𝗮𝗺𝘀 𝗿𝗮𝗿𝗲𝗹𝘆 𝗿𝗲𝘃𝗶𝘀𝗶𝘁: 𝗽𝗮𝗿𝘁𝗶𝘁𝗶𝗼𝗻 𝗹𝗮𝘆𝗼𝘂𝘁. A bad partition strategy doesn't throw errors. It inflates cloud bills: silently, every query, every day. 𝗧𝗵𝗿𝗲𝗲 𝗮𝗻𝘁𝗶-𝗽𝗮𝘁𝘁𝗲𝗿𝗻𝘀 𝘁𝗵𝗮𝘁 𝗰𝗼𝘀𝘁 𝗿𝗲𝗮𝗹 𝗺𝗼𝗻𝗲𝘆: → 𝗜𝗻𝗴𝗲𝘀𝘁-𝗸𝗲𝘆 𝗽𝗮𝗿𝘁𝗶𝘁𝗶𝗼𝗻𝗶𝗻𝗴: Partitioning by batch_id, file_name, or Kafka offset often forces analytical queries into a wide scan. You pay scan cost for ingestion decisions. → 𝗢𝘃𝗲𝗿-𝗽𝗮𝗿𝘁𝗶𝘁𝗶𝗼𝗻𝗶𝗻𝗴: Partitioning on high-cardinality fields like user_id or order_id can create thousands of tiny files. Metadata overhead grows, query planners slow down, and cloud storage charges per-request costs on every file open. → 𝗔𝗿𝗿𝗶𝘃𝗮𝗹-𝘁𝗶𝗺𝗲 𝗱𝗿𝗶𝗳𝘁: Partitioning by when data arrived instead of when the event happened. Late-arriving data lands in the wrong partition, and reprocessing becomes unreliable. 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗲𝘀 𝘁𝗵𝗮𝘁 𝘀𝗰𝗮𝗹𝗲: → 𝗥𝗲𝗮𝗱-𝗽𝗮𝘁𝗵 𝗽𝗮𝗿𝘁𝗶𝘁𝗶𝗼𝗻𝗶𝗻𝗴: Partition on the columns that dominate WHERE clauses: date, region, tenant. Start with time. Add one dimension only when query patterns prove it's needed. → 𝗖𝗹𝘂𝘀𝘁𝗲𝗿𝗶𝗻𝗴 / 𝗭-𝗼𝗿𝗱𝗲𝗿𝗶𝗻𝗴: Use sorting within partitions for secondary filters. Handles high-cardinality fields like customer_id without exploding partition counts. → 𝗛𝗶𝗱𝗱𝗲𝗻 𝗽𝗮𝗿𝘁𝗶𝘁𝗶𝗼𝗻𝗶𝗻𝗴: Table formats like Iceberg manage partition layout transparently. Users query with filters; the engine prunes partitions under the hood. No more manual WHERE year=2026 AND month=03. 𝗧𝗵𝗲 𝗿𝘂𝗹𝗲: Partition by how data is read, not how it arrives. Review at 10x growth. Storage layout is an architecture decision, not a DBA afterthought. What partitioning decision would you approach differently today? #DataEngineering #DataArchitecture #CloudCost

Most dashboards answer questions. This one prevents bad ones from being asked in the first place. Look at how it's built. → The goal tracking bars at the top aren't decorative. They set the ceiling before you look at anything else. Every number below them gets read relative to a target, not in isolation. That one design choice eliminates half the "so what" conversations in a review meeting. → Profit ratio sits next to sales in the customer table. Not in a separate report. Not one click away. Right there, on the same row. The person running that meeting cannot talk about revenue without seeing margin in their peripheral vision. That is a behavioral nudge built into the layout. → Delivery time by region is filterable by shipping class. That is not a reporting feature. That is an escalation path. The moment something looks wrong, the next question is already answerable inside the same view. → The year-over-year monthly comparison uses two dot colors, not two separate charts. Small call. Saves three slides in every quarterly deck. → The customer detail table is drillable to 704 records but opens at 50. Enough context to spot patterns. Not so much that the meeting derails into individual account debates. This is what decision infrastructure looks like at the execution level. Not what data you show, how you arrange it so the right conversation happens automatically. Great work, Murilo Cremon! If you want a framework for designing dashboards around decisions before you open any tool, grab my free Dashboard Canvas interview template: https://gum.co/u/h70xrlfv ♻️ Repost if you've sat through a dashboard review that raised more questions than it answered. 🔔 Follow Nick Valiotti for more on data infrastructure that actually drives decisions.

Confessions of a Data Scientist: Today, I spent two hours optimizing a visualization that could have been a table 🫠 *BUT* before you @ me, here's why this matters for cognitive load and decision-making speed in real-world applications: Short answer: Your brain can spot geographical and seasonal patterns 60,000X faster in colors than in numbers. (Yes, that's a real cognitive psychology stat, fight me) Long answer: I analyzed Brazilian e-commerce data to prove a point about regional-seasonal buying patterns, and the results were pretty neat. Swipe to see both visualizations → The first shows daily ordering patterns (spot those lunch breaks!), while the second reveals how seasonal buying behavior varies across Brazil's diverse regions. What you're seeing: - Clear daily patterns showing peak ordering times (you can actually see Brazil's lunch breaks in the data!) - Regional variations that flip traditional seasonal expectations (because Brazil spans multiple climate zones) - Some states showing completely opposite seasonal patterns from their neighbors - Hidden patterns that would've been buried in a 168-cell table (24 hours × 7 days) The technical breakdown + code for fellow data nerds: https://lnkd.in/gx4upkux Business impact (AKA here is what this visualization can help with): - Optimal customer service staffing (those 2PM spikes need coverage!) - Region-specific inventory timing - Targeted marketing campaign scheduling - Data-driven fulfillment center capacity planning P.S. Yes, I used a colorblind-friendly palette. And yes, I spent an extra hour making sure the color scale perfectly represented the percentage differences. Some hills are worth dying on. 🎨 #ConfessionsOfADataScientist #DataVisualization #DataScience #Python #Analytics