Designing For User Attention

🔮 How To Prioritize UX Work (Framework) (https://lnkd.in/eGQrPm2N), a very practical guide on how to choose and estimate the right level of research and UX work needed for a successful outcome of a project — along with the process to follow and UX estimates to set. Kindly shared by Jeremy Bird. 🤔 Planning is typically done for the delivery phase only. 🤔 Design, research, discovery, ideation are not planned. 🤔 Effort, estimates, roadmaps, capacity are rare for UX work. 🚫 Not every project needs the same level of research/design. ✅ Goal: set realistic expectations for UX work in a timeframe. Jeremy suggests to estimate research and design efforts separately, and across different dimensions: we assess research by mapping Risks and Problem Clarity. And we estimate design effort needed by mapping Risk and Level of Complexity: 🔮 Clarity: Low ↔ High New, unknown problems usually come with a lot of assumptions and very low clarity. Well-known problems with shared understanding in the team and some extensive research have higher degree of clarity. 🔥 Risk: Low ↔ High Some projects are relatively easy to roll back and they don't really affect business-critical workflows (low risk). Others are much more difficult to reverse and operate within users' key journeys (high risk). 🚀 Complexity: Low ↔ High Self-contained projects in well-understood workflows are typically straightforward (low complexity). Some projects that involve many systems, external dependencies, stakeholders scattered across teams with little existing knowledge (high complexity). ✅ We start by defining a problem to solve + business impact. ✅ Then, we shape desired user outcome and success criteria. ✅ Next, we assess design effort and research effort levels. ✅ Run a kickoff meeting to prioritize and decide the scope. ✅ Designers break down UX work, estimate it, add to Jira. Personally, I always find it remarkably difficult to estimate the effort for research or design work. Even after so many years, with 20–30% buffer, I’m often underestimating the little nuances, blockers, constraints and bottlenecks hidden away somewhere between complex dependencies and external stakeholders. One thing is certain though: considering risk early is a very, very effective way to guide UX work in the right direction. High risk always requires some level of research and discovery. And early prioritization helps UX teams focus their effort where they add most value — saving time on resources for projects that deliver value to users and businesses. Finally: I can highly recommend to consider John Cutler's Effort vs. Value curves (https://lnkd.in/evrKJUEy) for prioritization work as well. Much of the work isn’t completed once it's delivered. More often than not, it will significantly add to maintenance costs over time. We better account for it early. #ux #design

I've been thinking a lot about the kind of content brands put into the world. Some of it sparks conversation and strengthens brand connection. Some of it...just fills the feed. Most B2C brands are great at chasing engagement, but not always at building brand meaning. When I mapped it out, the content that matters most always ends up in the upper-right quadrant: High Engagement + High Cultural Relevance / Emotional Impact. 🟩 The Sweet Spot This is content people actually interact with and that strengthens brand connection: • User-Generated Storytelling (not just reviews, but authentic, emotional UGC) • Lifestyle & Aspirational Content (travel inspo, fashion, wellness — fits seamlessly into how people see themselves) • Viral TikTok/Reels Trends (when done authentically and in sync with culture) • Influencer Collaborations (especially when creators embody your brand values) • Community Challenges / Hashtag Activations (identity-driven and participatory) This is where loyalty gets built. Where campaigns outlive algorithms. Where engagement means something. ⸻ 🟧 What to Watch Out For (Low/Low) • Generic Product Ads (feature dumps without story) • Random Sales Promotions (uninspired discount graphics) • Forced Trend-Jacking (when brands hop on memes without fit) 👉 These pieces don’t move the needle on culture or engagement. ⸻ 🟪 The Trap (High Engagement / Low Relevance) • Giveaways / Sweepstakes (quick hits, low equity) • Funny Memes / Low-lift Humor (attention-grabbing but not tied to your brand) • Clickbait-y Hacks (drive views without deepening connection) • Flash Discounts (transactional, not relational) 👉 Yes, these light up the metrics — but they don’t build lasting brand affinity. ⸻ The takeaway? Don’t just chase clicks. Make more content for the upper right: where engagement fuels cultural relevance, and cultural relevance and emotional impact fuels long-term brand love. 𝙄𝙛 𝙮𝙤𝙪 𝙝𝙖𝙫𝙚𝙣’𝙩 𝙨𝙚𝙚𝙣 𝙢𝙮 𝘽2𝘽 𝙢𝙖𝙩𝙧𝙞𝙭, 𝙘𝙝𝙚𝙘𝙠 𝙞𝙩 𝙤𝙪𝙩 𝙝𝙚𝙧𝙚: https://lnkd.in/d7DXQDMB 𝙄’𝙡𝙡 𝙙𝙞𝙫𝙚 𝙙𝙚𝙚𝙥𝙚𝙧 𝙞𝙣𝙩𝙤 𝙘𝙤𝙣𝙩𝙚𝙣𝙩 𝙞𝙣 𝙪𝙥𝙘𝙤𝙢𝙞𝙣𝙜 𝙄𝙣𝙨𝙞𝙙𝙚 𝙎𝙤𝙘𝙞𝙖𝙡 𝙈𝙚𝙙𝙞𝙖 𝙇𝙚𝙖𝙙𝙚𝙧𝙨𝙝𝙞𝙥 𝙣𝙚𝙬𝙨𝙡𝙚𝙩𝙩𝙚𝙧𝙨. 𝙎𝙪𝙗𝙨𝙘𝙧𝙞𝙗𝙚 𝙝𝙚𝙧𝙚: https://lnkd.in/d28dna4K

💡RICE Framework for Feature Prioritization RICE framework is a popular method used in product management to prioritize features, projects, or initiatives. RICE stands for Reach, Impact, Confidence, and Effort, and it's a scoring model that helps teams make data-driven decisions. Why to use RICE: ✔ Balanced perspective: By considering reach, impact, confidence, and effort, it ensures a balanced view. ✔ Transparency: It makes the prioritization process transparent and easy to explain to stakeholders. Quick breakdown of each RICE component: 🍏 Reach: ✔ This measures how many people will be affected by the feature or initiative within a given time period. ✔ It's quantified as the number of users, customers, or sessions. 🍏 Impact: ✔ This evaluates the potential effect the feature will have on each individual user. ✔ Impact is often scored on a scale, such as 0.25 (minimal), 0.5 (low), 1 (medium), 2 (high), and 3 (massive). 🍏 Confidence: ✔ This reflects how certain the team is about their estimates for Reach, Impact, and Effort. ✔ It's usually scored as a percentage (e.g., 100% for high confidence, 80% for medium, and 50% for low). 🍏 Effort: ✔ This assesses the amount of time and resources required to implement the feature. ✔ Effort is typically measured in person-months or the number of "man-hours" needed. 🤓 Calculating the RICE Score RICE Score = Reach × Impact × Confidence / Effort 📕 Practical example Suppose you have three features to prioritize—Feature A, B and C. Feature A: ✔ Reach: 500 users ✔ Impact: 2 (high) ✔ Confidence: 80% ✔ Effort: 4 person-months Feature B: ✔ Reach: 200 users ✔ Impact: 3 (massive) ✔ Confidence: 50% ✔ Effort: 2 person-months Feature C: ✔ Reach: 1000 users ✔ Impact: 1 (medium) ✔ Confidence: 90% ✔ Effort: 6 person-months Let's calculate the RICE scores: Feature A = 500 × 2 × 0.8 / 4 = 200 Feature B = 200 × 3 × 0.5 / 2 = 150 Feature C = 1000 × 1 × 0.9 / 6 = 150 Feature A has the highest RICE score and would be the top priority, followed by Feature B and Feature C. 🛠 Tools: ✔ RICE: Score & Prioritize Template for FigJam (by Nate Greenwall) https://lnkd.in/dHbxaA34 ✔ RICE template for Miro https://lnkd.in/dk_bytET 🖼 RICE Prioriziation method by Powerslides #rice #featureprioritization #design #productdesign #UX #uxdesign #userexperience

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.

🧠Brand growth is stalling cause 84% of the purchases does not come from performance marketing. What we can learn from boys and their toys. As a consumer psychologist, having spent years studying how people perceive, learn, and choose - and how ads shape those processes - I can’t help but see a pattern emerging. A few weeks ago, Andrew Tindall summed it up: ad #spend is rising, but effectiveness keeps falling. Lumen Research, Newsworks, and Peter Field have shown why - over the last decade, budgets have drifted toward low-attention #media. And now, new research from WPP Media and Saïd Business School, University of Oxford adds a deeper layer. 🧠 It turns out 84% of purchase decisions are driven by #priming - not by #performance. In other words, focus has been optimising for the last 16% of decision power, while ignoring the 84% that happens long before someone’s ready to buy. ✨Think about it: how many men end up buying the car brand they played with as boys? That’s not chance. It’s years of emotional #connection and #storytelling with relevant sponsorships and tv ads shaping the neural networks that guide future choices. ✅ That’s exactly where brand-building media like #TV come in. They don’t just capture attention, they create memory structures, build meaning, and fuel long-term preference. So while performance marketing chases #conversions, TV and other high-attention formats build the brand networks that make those conversions possible in the first place. 📊 As the chart below shows, across categories — from #softdrinks to #financialservices - 84% of purchases are made with a priming bias. Always in-tune, navigating with curiosity and Zeal. #branduplift #shortermism #longtermism #consumerpsychology #customerjourney #adindustry #adresearch #adimpact #automotive #fmcg #cpg #telco #retail #personalcare #otc #babycare #qsr #quickservicerestaurant #oralcare #consumerelectronics #luxury Adding people keen on ad effectiveness and proving the value of TV Lindsey Clay, Elliott Millard Laura Baehr Jacqueline Freeman Anna Lujanen

𝗧𝗼𝗽-𝗱𝗼𝘄𝗻 𝗮𝘁𝘁𝗲𝗻𝘁𝗶𝗼𝗻 𝗶𝘀 𝘁𝗵𝗲 𝗵𝗮𝗿𝗱𝗲𝘀𝘁 𝗮𝘁𝘁𝗲𝗻𝘁𝗶𝗼𝗻 𝘁𝗼 𝘄𝗶𝗻 𝗶𝗻 𝗮𝗱𝘃𝗲𝗿𝘁𝗶𝘀𝗶𝗻𝗴. 𝘉𝘶𝘵 𝘵𝘩𝘦𝘳𝘦 𝘢𝘳𝘦 𝘮𝘰𝘮𝘦𝘯𝘵𝘴 𝘸𝘩𝘦𝘯 𝘪𝘵'𝘴 𝘺𝘰𝘶𝘳𝘴 𝘧𝘰𝘳 𝘵𝘩𝘦 𝘵𝘢𝘬𝘪𝘯𝘨. Most ads are served to people who are mentally somewhere else. Commuting. Navigating. Waiting. Their brain has a goal — and that goal has nothing to do with your message. In the brain, the prefrontal cortex actively suppresses everything irrelevant to what it's currently pursuing. Your ad isn't being ignored. It's being filtered out at a neuropsychological level. That's the default state of advertising. Passive exposure to an occupied mind. 𝘽𝙪𝙩 𝙩𝙤𝙥-𝙙𝙤𝙬𝙣 𝙖𝙩𝙩𝙚𝙣𝙩𝙞𝙤𝙣 𝙝𝙖𝙨 𝙖 "𝙬𝙚𝙖𝙠𝙣𝙚𝙨𝙨": 𝙞𝙩 𝙛𝙤𝙡𝙡𝙤𝙬𝙨 𝙜𝙤𝙖𝙡𝙨. 𝘼𝙣𝙙 𝙜𝙤𝙖𝙡𝙨 𝙖𝙧𝙚 𝙥𝙧𝙚𝙙𝙞𝙘𝙩𝙖𝙗𝙡𝙚. The brain isn't searching for ads, but it is searching for things. And when your message aligns with what someone is already looking for, you're no longer interrupting. You're matching. That shift changes everything about how the brain processes what it sees. A few contexts where this plays out: • 𝗔𝗶𝗿𝗽𝗼𝗿𝘁𝘀 — people are actively deciding: hotel, food, transport, and currency. Purchase intent is live and high. An ad here enters a primed system, not a passive one. • 𝗦𝗵𝗼𝗽𝗽𝗶𝗻𝗴 𝗲𝗻𝘃𝗶𝗿𝗼𝗻𝗺𝗲𝗻𝘁𝘀 — the goal is acquisition. The brain is already in evaluation mode. Contextual relevance at the right physical moment intercepts an active decision, not a passive scroll. • 𝗠𝗼𝗿𝗻𝗶𝗻𝗴 𝘁𝗶𝗺𝗶𝗻𝗴 + 𝗳𝗼𝗼𝗱/𝗰𝗼𝗳𝗳𝗲𝗲 — hunger and caffeine-seeking are genuine active goals at 8 am. The right message at that moment isn't an interruption. It's an answer. • 𝗡𝗲𝗮𝗿 𝗮 𝗰𝗼𝗺𝗽𝗲𝘁𝗶𝘁𝗼𝗿 — someone in proximity to a rival is already in a consideration state. That's not reach. That's relevance at exactly the right cognitive moment. Most attention strategy asks: how do we get noticed? The better question is: 𝙬𝙝𝙚𝙣 𝙞𝙨 𝙤𝙪𝙧 𝙖𝙪𝙙𝙞𝙚𝙣𝙘𝙚 𝙖𝙡𝙧𝙚𝙖𝙙𝙮 𝙡𝙤𝙤𝙠𝙞𝙣𝙜 𝙛𝙤𝙧 𝙪𝙨, 𝙖𝙣𝙙 𝙖𝙧𝙚 𝙬𝙚 𝙩𝙝𝙚𝙧𝙚? Read more here: https://lnkd.in/e7eJJSRU #attention #neuromarketing #designforattention #topdownattention #mindstates

RICE doesn't work for AI products. Neither does WSJF. Or MoSCoW. Or any framework built for deterministic software. Here's what works instead: The problem with old frameworks: → RICE assumes you can estimate "Impact" before launch → AI features don't have predictable impact. They have confidence intervals → A recommendation engine might work 92% of the time. Or 67%. You won't know until it's live → Old frameworks treat features as binary: shipped or not shipped. AI features are on a spectrum of "how well does it work" The new way to prioritize AI features: 1. Confidence-gated prioritization → Before you estimate impact, estimate model confidence → How confident are you that the model can do this task at production quality? → High confidence (90%+): Prioritize like a normal feature → Medium confidence (70-89%): Build an MVP, test with real users, measure → Low confidence (below 70%): Prototype only. Don't roadmap it 2. Cost-per-inference ranking → Every AI feature has a running cost → A feature that costs $0.50 per user per month needs 10x the business impact of one that costs $0.05 → Add "cost per inference" as a column in your prioritization spreadsheet → Most PMs skip this. Their CFO doesn't 3. Eval-readiness scoring → Can you measure if this feature is working? → Do you have golden datasets to test against? → If you can't eval it, you can't improve it → Features without evals are features you'll regret launching 4. Fallback quality assessment → What happens when the model fails? → Is the fallback graceful (show nothing) or catastrophic (show wrong info)? → Features with catastrophic fallbacks need higher confidence thresholds before shipping Stop forcing AI products into frameworks designed for a different era. Build new ones. → AI roadmap guide: https://lnkd.in/dsTdiPhC → AI evals for PMs: https://lnkd.in/dnjU2QV9 What framework do you use for AI prioritization?

Most product managers prioritize features the wrong way. AI can fix that. Here are 3 powerful AI prompts to revolutionize your workflow. Here are 3 AI prompts that will change how you rank features based on user needs and business impact: 1️⃣ Comprehensive Feature Analysis: A deep dive into each feature's potential impact and alignment with goals. 💡 Prompt: "Analyze the following features: {feature_list}. For each feature, provide a detailed assessment of its potential impact on user satisfaction, retention, and revenue growth. Consider our current user base demographics, market trends, and competitive landscape. Prioritize these features based on their alignment with our Q4 goal of improving user retention by 15%. Finally, rank the features in order of priority and explain the rationale behind this ranking." 2️⃣ User Feedback Synthesizer: AI powered analysis of user pain points and feature requests. 💡 Prompt: "Aggregate and analyze customer feedback from the following sources: {feedback_sources} (e.g., app store reviews, customer support tickets, user interviews, NPS surveys). Identify the top 5 recurring themes or pain points mentioned by users. For each theme, provide specific examples of user quotes or data points. Rank these themes based on frequency of mention and severity of impact on user experience. Then, map each theme to potential feature improvements or new feature ideas. Prioritize these feature ideas based on their potential to address user pain points, estimated development effort, and alignment with our product strategy. Share a detailed rationale for your prioritization, including any potential risks or trade-offs to consider." 3️⃣ Development Effort Estimator: A comprehensive analysis of resource requirements. 💡 Prompt: "Estimate the development effort for implementing {feature_name} in our {product_type}, considering our team of 10 engineers and 8-week timeline. Break down the implementation into key components or stages (e.g., design, frontend development, backend development, testing, deployment). For each component, estimate the number of engineer-days required, potential technical challenges, and any dependencies on other systems or third-party integrations. Consider our team's expertise and any learning curve associated with new technologies. Identify any potential bottlenecks or risks that could impact the timeline. Suggest strategies to mitigate these risks, such as parallel development tracks or phased rollout approaches. Provide a confidence level (low, medium, high) for each estimate and explain the reasoning. Finally, give a range estimate for the total development time (best case, expected case, worst case) and suggest any features or scope that could be adjusted to fit within the 8-week timeline if necessary." Product Managers, these AI prompts are designed to enhance your decision making, not replace it. Use them to gain data-driven insights, then apply your expertise to make the final call.

The world of research methods and statistics is incredibly rich, and MaxDiff is one of the most effective tools for understanding user preferences in UX surveys. It works by asking participants to choose the most and least important items from small sets, which leads to better discrimination between options and avoids common biases like rating everything as “important.” MaxDiff is especially useful when you have a moderately long list (say 10 to 25 items) and need to prioritize features, messages, or user needs based on relative importance. For example, if you're designing a new dashboard and want to know which of 18 possible widgets users value most, MaxDiff helps you rank them clearly without overwhelming your participants. However, while MaxDiff is powerful, it's not always the best fit for every type of decision or dataset, and that's where other methods come in. Conjoint Analysis is ideal when you want to understand trade-offs between product attributes. Choice-Based Conjoint (CBC) presents users with different combinations, like price, battery life, and brand, and asks them to pick their preferred product. It’s commonly used in pricing and feature optimization. If you're testing whether users would pay more for extra storage or better battery life, conjoint can model those choices in realistic terms. Unlike MaxDiff, it handles interactions between variables. Pairwise Comparison simplifies decision-making when dealing with very long lists. Users compare two items at a time, which lowers cognitive load. For instance, if a food delivery app wants to sort through 30 service improvements, showing one pair at a time makes choices easier. The drawback is that the number of comparisons grows quickly, making surveys longer. Ranked Choice Voting asks participants to sort all options from most to least preferred. It works best with short lists, like evaluating six homepage designs. It gives clear order and is easy to interpret, but becomes unreliable and fatiguing with longer lists. MaxDiff was designed to handle this exact issue by presenting only subsets. Points Allocation, or constant sum, lets respondents distribute a fixed number of points (e.g., 100) across items based on perceived importance. It’s helpful when you want to understand not just order, but how much more one item matters than another. For example, if a user gives 60 points to delivery cost and 15 to tracking features, that says a lot. But this method gets harder to use accurately as the number of items increases. MaxDiff remains a strong choice for prioritizing longer lists when time and attention are limited. It’s especially helpful in feature prioritization and message testing. While it doesn’t model attribute combinations like conjoint, it’s faster, cleaner, and more intuitive for many use cases. In the end, no method is best for every situation. The right choice depends on your research goal, the complexity of the items, and the effort you expect from users.

Most brand marketers would agree: Not all impressions are equal. But how often do we really pressure-test how we measure attention? A new study from Nicole Hartnett, Dr Virginia Beal, Rachel Kennedy, and the team at the Ehrenberg-Bass Institute asks an important question: 👉 Which attention metrics actually tell us if people are paying attention — not just looking? The researchers tested 8 popular attention measures — including eye tracking, skin conductance, and facial coding — against EEG (the gold standard) and self-reported attention. Here’s what they found: ❤️ Heart rate (specifically, heart rate slowing) was the most reliable scalable indicator of real attention — matching EEG results 👀 Eye tracking (“eyes on screen”) often flagged low-attention ads as high attention, because looking ≠ processing 😊 Facial coding (smiles) captured emotional response but wasn’t a consistent signal of conscious engagement The key takeaway: most attention metrics in advertising today measure presence, not processing. And those aren’t the same thing. So how do you know which kind of attention your campaign actually needs? Not all advertising depends on deep attention to work. For well-known brands, repeated exposure — even at low attention — can still reinforce memory structures and keep the brand easy to recall when buyers enter a purchase situation. But when a campaign is trying to: ✅ Introduce new brand associations ✅ Reinforce distinctive assets that trigger brand recall ✅ Expand mental availability across more buying situations — attention matters more. Because buyers need to process the message clearly enough for it to stick. If your plan relies on people remembering what was said (not just who showed up), then it depends on cognitive attention — not just presence. Which means it matters how you’re measuring it: 👁️ Eyes on screen → presence (but not necessarily attention) 😊 Smiling → emotional reaction (but not necessarily engagement with the message) 💓 Heart rate slowing → cognitive attention and deeper message processing The study confirmed what many of us have felt: higher attention leads to stronger recall, recognition, and brand choice. Low attention can still play a role — but it works differently, and only under the right conditions. Full study here: https://lnkd.in/gyusZf2w