Visual Design Principles for User Experience

Product managers & designers working with AI face a unique challenge: designing a delightful product experience that cannot fully be predicted. Traditionally, product development followed a linear path. A PM defines the problem, a designer draws the solution, and the software teams code the product. The outcome was largely predictable, and the user experience was consistent. However, with AI, the rules have changed. Non-deterministic ML models introduce uncertainty & chaotic behavior. The same question asked four times produces different outputs. Asking the same question in different ways - even just an extra space in the question - elicits different results. How does one design a product experience in the fog of AI? The answer lies in embracing the unpredictable nature of AI and adapting your design approach. Here are a few strategies to consider: 1. Fast feedback loops : Great machine learning products elicit user feedback passively. Just click on the first result of a Google search and come back to the second one. That’s a great signal for Google to know that the first result is not optimal - without tying a word. 2. Evaluation : before products launch, it’s critical to run the machine learning systems through a battery of tests to understand in the most likely use cases, how the LLM will respond. 3. Over-measurement : It’s unclear what will matter in product experiences today, so measuring as much as possible in the user experience, whether it’s session times, conversation topic analysis, sentiment scores, or other numbers. 4. Couple with deterministic systems : Some startups are using large language models to suggest ideas that are evaluated with deterministic or classic machine learning systems. This design pattern can quash some of the chaotic and non-deterministic nature of LLMs. 5. Smaller models : smaller models that are tuned or optimized for use cases will produce narrower output, controlling the experience. The goal is not to eliminate unpredictability altogether but to design a product that can adapt and learn alongside its users. Just as much as the technology has changed products, our design processes must evolve as well.

Want to create better dataviz? Before you call your next data visualization complete, make sure its passes these three tests: 1. The Spartan Test: Strip it down. Ruthlessly assess every element in your chart. If removing something doesn’t change the message, it’s clutter. Clear visuals build trust — give your audience only what they need. 2. The Peek Test: Look away for 5 seconds, then glance back at your visual. Where does your eye go first? Chances are, that’s where your audience will focus too. Adjust until attention is drawn to the key insight. 3. The Colleague Test: Think it’s perfect? Share it with a colleague who hasn’t seen the analysis. Provide minimal context and give them 10-15 seconds to interpret. Ask what they take away — does it match your intent? Nail these three, and your data visualization will not just look good — it will communicate clearly and effectively. Three passing grades means it's ready to be presented. Art+Science Analytics Institute | University of Notre Dame | University of Notre Dame - Mendoza College of Business | University of Illinois Urbana-Champaign | University of Chicago | D'Amore-McKim School of Business at Northeastern University | ELVTR | Grow with Google - Data Analytics #Analytics #DataStorytelling

Most plots fail before they even leave the notebook. Too much clutter. Too many colors. Too little context. I have a stack of visualization books that teach theory, but none of them walk through the tools. In Effective Visualizations, I aim to fix that. I introduce the CLEAR framework—a simple checklist to rescue your charts from confusion and make them resonate: Color: Use color sparingly and intentionally. Highlight what matters. Avoid rainbow palettes that dilute your message. Limit plot type: Just because you can make a 3D exploding donut chart doesn’t mean you should. The simplest plot that answers your question is usually the best. Explain plot: Add clear labels, titles. Remove legends! If you need a decoder ring to read it, you’re not done. Audience: Know who you’re talking to. Executives care about different details than data scientists. Tailor your visuals accordingly. References: Show your sources. Data without provenance erodes trust. All done in the most popular language data folks use today, Python! When you build visuals with CLEAR in mind, your plots stop being decorations and start being arguments—concise, credible, and persuasive.

While it can be easily believed that customers are the ultimate experts about their own needs, there are ways to gain insights and knowledge that customers may not be aware of or able to articulate directly. While customers are the ultimate source of truth about their needs, product managers can complement this knowledge by employing a combination of research, data analysis, and empathetic understanding to gain a more comprehensive understanding of customer needs and expectations. The goal is not to know more than customers but to use various tools and methods to gain insights that can lead to building better products and delivering exceptional user experiences. ➡️ User Research: Conducting thorough user research, such as interviews, surveys, and observational studies, can reveal underlying needs and pain points that customers may not have fully recognized or articulated. By learning from many users, we gain holistic insights and deeper insights into their motivations and behaviors. ➡️ Data Analysis: Analyzing user data, including behavioral data and usage patterns, can provide valuable insights into customer preferences and pain points. By identifying trends and patterns in the data, product managers can make informed decisions about what features or improvements are most likely to address customer needs effectively. ➡️ Contextual Inquiry: Observing customers in their real-life environment while using the product can uncover valuable insights into their needs and challenges. Contextual inquiry helps product managers understand the context in which customers use the product and how it fits into their daily lives. ➡️ Competitor Analysis: By studying competitors and their products, product managers can identify gaps in the market and potential unmet needs that customers may not even be aware of. Understanding what competitors offer can inspire product improvements and innovation. ➡️ Surfacing Implicit Needs: Sometimes, customers may not be able to express their needs explicitly, but through careful analysis and empathetic understanding, product managers can infer these implicit needs. This requires the ability to interpret feedback, observe behaviors, and understand the context in which customers use the product. ➡️ Iterative Prototyping and Testing: Continuously iterating and testing product prototypes with users allows product managers to gather feedback and refine the product based on real-world usage. Through this iterative process, product managers can uncover deeper customer needs and iteratively improve the product to meet those needs effectively. ➡️ Expertise in the Domain: Product managers, industry thought leaders, academic researchers, and others with deep domain knowledge and expertise can anticipate customer needs based on industry trends, best practices, and a comprehensive understanding of the market. #productinnovation #discovery #productmanagement #productleadership

In my former life, I was a graphic designer. I spent years obsessing over layouts, grids, color palettes, and the tiny details that make a design feel right. When I moved into learning design, I realized those same skills gave me an edge. The PARC principles I had been using for years—Proximity, Alignment, Repetition, and Contrast—translated perfectly into creating clearer, more engaging learning experiences. Proximity Group related content so learners instantly understand what belongs together. Alignment Position elements with purpose. Consistency in placement makes content easier to follow and trust. Repetition Repeat visual cues like colors, fonts, and layouts. Predictability helps learners focus on the message instead of figuring out the interface. Contrast Highlight what matters most. Use size, color, and whitespace to create a clear visual hierarchy. This simple system works in both worlds—graphic design and learning design—because it’s all about reducing friction, improving clarity, and guiding attention. What principles have you borrowed from another field that’s improved the way you create learning experiences?

How do you figure out what truly matters to users when you’ve got a long list of features, benefits, or design options - but only a limited sample size and even less time? A lot of UX researchers use Best-Worst Scaling (or MaxDiff) to tackle this. It’s a great method: simple for participants, easy to analyze, and far better than traditional rating scales. But when the research question goes beyond basic prioritization - like understanding user segments, handling optional features, factoring in pricing, or capturing uncertainty - MaxDiff starts to show its limits. That’s when more advanced methods come in, and they’re often more accessible than people think. For example, Anchored MaxDiff adds a must-have vs. nice-to-have dimension that turns relative rankings into more actionable insights. Adaptive Choice-Based Conjoint goes further by learning what matters most to each respondent and adapting the questions accordingly - ideal when you're juggling 10+ attributes. Menu-Based Conjoint works especially well for products with flexible options or bundles, like SaaS platforms or modular hardware, helping you see what users are likely to select together. If you suspect different mental models among your users, Latent Class Models can uncover hidden segments by clustering users based on their underlying choice patterns. TURF analysis is a lifesaver when you need to pick a few features that will have the widest reach across your audience, often used in roadmap planning. And if you're trying to account for how confident or honest people are in their responses, Bayesian Truth Serum adds a layer of statistical correction that can help de-bias sensitive data. Want to tie preferences to price? Gabor-Granger techniques and price-anchored conjoint models give you insight into willingness-to-pay without running a full pricing study. These methods all work well with small-to-medium sample sizes, especially when paired with Hierarchical Bayes or latent class estimation, making them a perfect fit for fast-paced UX environments where stakes are high and clarity matters.

Still treating your product detail page like a digital flyer? ❌ That “good enough” mindset is quietly killing your revenue. 👇 𝗟𝗲𝘁’𝘀 𝘁𝗮𝗹𝗸 𝗽𝗿𝗼𝗼𝗳: A client came to Blikket frustrated—tons of traffic, but conversion rates stuck at 3.5%. We overhauled their product detail page using 3 core UX shifts: → 𝗜𝗻𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻 𝗵𝗶𝗲𝗿𝗮𝗿𝗰𝗵𝘆: Focused on ONE headline, one primary CTA.  → 𝗩𝗶𝘀𝘂𝗮𝗹 𝗰𝗹𝗮𝗿𝗶𝘁𝘆: Stripped distracting banners, highlighted product benefits above the fold. → 𝗙𝗿𝗶𝗰𝘁𝗶𝗼𝗻𝗹𝗲𝘀𝘀 𝗽𝗮𝘁𝗵: Reduced variants, surfaced key reviews with icons (not endless text). 𝗥𝗲𝘀𝘂𝗹𝘁: Conversion rate jumped to 𝟳.𝟭% in 30 days. That’s a 𝟭𝟬𝟬% 𝗹𝗶𝗳𝘁—not from guesswork, but from focused UX changes. ✅ Stop stuffing. Start prioritizing what the buyer actually needs. Where’s your biggest UX blocker right now? Drop your pain point below—let’s troubleshoot. https://lnkd.in/gedvrCnM #eCommerce #UXDesign #CRO #ProductPages

8 out of 10 analysts struggle with delivering impactful data visualizations. Here are five tips that I learned through my experience that can improve your visuals immensely: 1. Know Your Stakeholder's Requirements: Before diving into charts and graphs, understand who you're speaking to. Tailor your visuals to match their expertise and interest levels. A clear understanding of your audience ensures your message hits the right notes. For executives, I try sticking to a high-level overview by providing summary charts like a KPI dashboard. On the other hand, for front-line employees, I prefer detailed charts depicting day-to-day operational metrics. 2. Avoid Chart Junk: Embrace the beauty of simplicity. Avoid clutter and unnecessary embellishments. A clean, uncluttered visualization ensures that your message shines through without distractions. I focus on removing excessive gridlines, and unnecessary decorations while conveying the information with clarity. Instead of overwhelming your audience with unnecessary embellishments, opt for a clean, straightforward line chart displaying monthly trends. 3. Choose The Right Color Palette: Colors evoke emotions and convey messages. I prefer using a consistent color scheme across all my dashboards that align with my brand or the narrative. Using a consistent color scheme not only aligns with your brand but also aids in quick comprehension. For instance, use distinct colors for important data points, like revenue spikes or project milestones. 4. Highlight Key Elements: Guide your audience's attention by emphasizing critical data points. Whether it's through color, annotations, or positioning, make sure your audience doesn't miss the most important insights. Imagine presenting a market analysis with a scatter plot showing customer satisfaction and market share. By using bold colors to highlight a specific product or region, coupled with annotations explaining notable data points, you can guide your audience's focus. 5. Tell A Story With Your Data: Transform your numbers into narratives. Weave a compelling story that guides your audience through insights. A good data visualization isn't just a display; it's a journey that simplifies complexity. Recently I faced a scenario where I was presenting productivity metrics. Instead of just displaying a bar chart with numbers, I crafted a visual story. I started with the challenge faced, used line charts to show performance fluctuations, and concluded with a bar chart illustrating the positive impact of a recent strategy. This narrative approach helped my audience connect emotionally with the data, making it more memorable and actionable. Finally, remember that the goal of data visualization is to communicate complex information in a way that is easily understandable and memorable. It's both an art and a science, so keep experimenting and evolving. What are your go-to tips for crafting effective data visualizations? Share your insights in the comments below!

The Amazon e-commerce landscape is evolving at a rapid pace. Here's what we're observing: 👀 𝕀𝕟𝕔𝕣𝕖𝕒𝕤𝕖𝕕 𝕟𝕖𝕖𝕕 𝕗𝕠𝕣 𝕤𝕥𝕖𝕝𝕝𝕒𝕣 𝕧𝕚𝕤𝕦𝕒𝕝 𝕔𝕠𝕟𝕥𝕖𝕟𝕥: With the competitive landscape constantly shifting and customer demands rising, top-notch product images and creatives are crucial. Businesses must frequently update visuals to maintain conversion rates. 👀 𝕊𝕥𝕣𝕠𝕟𝕘 𝕖𝕞𝕡𝕙𝕒𝕤𝕚𝕤 𝕠𝕟 𝕧𝕚𝕤𝕦𝕒𝕝 𝕚𝕟𝕗𝕠𝕣𝕞𝕒𝕥𝕚𝕠𝕟: Shoppers, especially Prime members, heavily rely on visual cues, with visual content driving 74% of their purchase decisions. Superior creatives are essential in launching and promoting products effectively. 👀 ℍ𝕚𝕘𝕙 𝕔𝕠𝕟𝕧𝕖𝕣𝕤𝕚𝕠𝕟 𝕣𝕒𝕥𝕖𝕤 𝕗𝕠𝕣 𝕢𝕦𝕚𝕔𝕜-𝕕𝕖𝕔𝕚𝕤𝕚𝕠𝕟 𝕝𝕚𝕤𝕥𝕚𝕟𝕘𝕤: Customers expect listings that can be quickly navigated and understood even in distracted settings. Successful listings use strategic image placement and minimalistic design to enhance conversion rate. 👀 𝔸+ ℂ𝕠𝕟𝕥𝕖𝕟𝕥 𝕥𝕣𝕒𝕟𝕤𝕗𝕠𝕣𝕞𝕒𝕥𝕚𝕠𝕟: Transitioning to premium A+ content with interactive elements and cohesive design is paramount. Static, text-heavy formats are becoming obsolete. 👀 ℂ𝕠𝕟𝕥𝕚𝕟𝕦𝕒𝕝 𝕠𝕡𝕥𝕚𝕞𝕚𝕫𝕒𝕥𝕚𝕠𝕟 𝕗𝕠𝕣 𝕔𝕠𝕞𝕡𝕖𝕥𝕚𝕥𝕚𝕧𝕖𝕟𝕖𝕤𝕤: Regular updates and innovations in image strategy, including the use of manager experiments, are necessary to stay ahead in evolving niches. 👀𝔼𝕝𝕖𝕧𝕒𝕥𝕚𝕟𝕘 𝕔𝕦𝕤𝕥𝕠𝕞𝕖𝕣 𝕖𝕩𝕡𝕖𝕣𝕚𝕖𝕟𝕔𝕖 𝕥𝕙𝕣𝕠𝕦𝕘𝕙 𝕚𝕞𝕒𝕘𝕖𝕣𝕪: Creative and strategic image planning, focusing on customer needs and information flow, is key. Brands are urged to adopt minimalist designs and leverage AI to create compelling visuals. This market demands agility and innovation. With persistent efforts and cutting-edge tools, sellers can enhance their market standing and conversion rates. What other insights do you have to add to this list?

In the vast ocean of digital marketing, your landing page is the lighthouse guiding potential customers to your shores. We recently embarked on a journey with a client to revamp their landing page, and the results were nothing short of spectacular. We witnessed a whopping 143% increase in their conversion rate, soaring to 4.18%. So, how did we achieve this transformation? 1) Crystal Clear Headline: The first thing visitors see should instantly convey your value proposition. We crafted a headline that was not only compelling but also easy to read and understand. It's the digital equivalent of a firm, confident handshake. 2) Review Count Front and Center: Social proof is a powerful tool. By placing the review count high up, visible immediately on both mobile and PC, we leveraged the power of community validation. When potential customers see that others have benefited, they're more likely to trust your offering. 3) Above the Fold Magic: The "fold" is the point where users need to scroll to see more. Everything above this point should be your prime real estate. We ensured that the most crucial information, call-to-action buttons, and engaging visuals were positioned here for immediate impact. 4) Consistent and Intuitive Design: A cohesive color scheme, clear fonts, and intuitive navigation can make the difference between a bounce and a conversion. We streamlined the design to ensure a seamless and pleasant user experience. 5) Engaging Visuals with Context: While high-quality images and videos are essential, they need to be more than just eye candy. We selected visuals that not only resonated with the brand but also told a story, adding depth to the user's journey. 6) Trust Indicators: Beyond reviews, we incorporated trust badges, testimonials, and certifications. These elements further cemented the brand's credibility and made users feel secure in their decision to engage. A landing page is more than just a digital storefront; it's a narrative, a promise, and an invitation. By focusing on the user's experience and journey, we were able to transform clicks into conversions. If your landing page isn't delivering the results you desire, perhaps it's time for a makeover.