UX And Behavioral Economics

🔮 UX Metrics and KPIs Cheatsheet (Figma) (https://lnkd.in/en9MK4MD), a helpful reference sheet for UX metrics, with formulas and examples — for brand score, desirability, loyalty, satisfaction, sentiment, success, usefulness and many others. Neatly put together in one single place by fine folks at Helio Glare. To me personally, measuring UX success is focused around just a few key attributes — how successful users are in completing their key tasks, how many errors users experience along the way and how quickly users get through onboarding to first meaningful success. The context of the project will of course request specific, custom metrics — e.g. search quality score, or brand score, or engagement score or loyalty — but UX metrics are all around delivering value to users through their successes. Here are some examples: 1. Top tasks success > 80% (for critical tasks) 2. Time to complete top tasks < Xs (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 a 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 > 30% (usage of a new feature per user) 10. Feature retention rate > 40% (after 90 days) 11. Time to pricing quote < 2 weeks (for B2B systems) 12. Application processing time < 2 weeks (online banking) 13. Default settings correction < 10% (quality of defaults) 14. Relevance of top 100 search queries > 80% (for top 5 results) 15. Service desk inquiries < 35/week (poor design → more inquiries) 16. Form input accuracy ≈ 100% (user input in forms) 17. Frequency of errors < 3/visit (mistaps, double-clicks) 18. Password recovery frequency < 5% per user (for auth) 19. Fake email addresses < 5% (newsletters) 20. Helpdesk follow-up rate < 4% (quality of service desk replies) 21. “Turn-around” score < 1 week (frustrated users -> happy users) 22. Environmental impact < 0.3g/page request (sustainability) 23. Frustration score < 10% (AUS + SUS/SUPR-Q) 24. System Usability Scale > 75 (usability) 25. Accessible Usability Scale (AUS) > 75 (accessibility) Each team works with 3–4 design KPIs that reflect the impact of their work. 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. [continues in comments ↓] #ux #design

In 2018, Michael Kirchler and Stefan Palan ran two real-world field experiments. 𝟭. 𝗜𝗰𝗲 𝗰𝗿𝗲𝗮𝗺 𝗲𝘅𝗽𝗲𝗿𝗶𝗺𝗲𝗻𝘁 (𝗼𝗻𝗲-𝗼𝗳𝗳 𝗶𝗻𝘁𝗲𝗿𝗮𝗰𝘁𝗶𝗼𝗻𝘀) — Customers ordered ice cream under three conditions: * Normal: just ordering * Compliment: “You have the best ice cream in town” * Tip: a small tip given before preparation 𝟮. 𝗗𝗼𝗻𝗲𝗿 𝗸𝗲𝗯𝗮𝗯 𝗲𝘅𝗽𝗲𝗿𝗶𝗺𝗲𝗻𝘁 (𝗿𝗲𝗽𝗲𝗮𝘁𝗲𝗱 𝗶𝗻𝘁𝗲𝗿𝗮𝗰𝘁𝗶𝗼𝗻𝘀) — Customers returned to the same vendor for 5 consecutive days, again under: * Normal * Compliment * Tip 𝗞𝗲𝘆 𝗙𝗶𝗻𝗱𝗶𝗻𝗴𝘀: 𝟭. 𝗖𝗼𝗺𝗽𝗹𝗶𝗺𝗲𝗻𝘁𝘀 𝘄𝗼𝗿𝗸 — 𝗶𝗺𝗺𝗲𝗱𝗶𝗮𝘁𝗲𝗹𝘆 * Salespeople gave more food after receiving a compliment. * Effect is statistically strong and consistent across settings. 𝟮. 𝗧𝗶𝗽𝘀 𝗮𝗹𝘀𝗼 𝘄𝗼𝗿𝗸 — 𝗯𝘂𝘁 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁𝗹𝘆 Tips triggered larger immediate increases in food weight. However, once you account for the cost of the tip, customers often did not get full value back. 👉 𝗠𝗼𝗻𝗲𝘆 𝗯𝘂𝘆𝘀 𝗿𝗲𝗰𝗶𝗽𝗿𝗼𝗰𝗶𝘁𝘆, 𝗯𝘂𝘁 𝗶𝘁’𝘀 𝗲𝘅𝗽𝗲𝗻𝘀𝗶𝘃𝗲. 𝟯. 𝗧𝗵𝗲 𝗺𝗼𝘀𝘁 𝗶𝗺𝗽𝗼𝗿𝘁𝗮𝗻𝘁 𝗶𝗻𝘀𝗶𝗴𝗵𝘁: 𝘁𝗶𝗺𝗲 * Compliments compound over time * Each visit with a compliment led to more generosity than the last. * Roughly +5g more per visit in the doner experiment. * Tips do not compound * The effect of tipping stayed flat (or even weakened). * No learning, no escalation. 👉 𝗜𝗺𝗺𝗮𝘁𝗲𝗿𝗶𝗮𝗹 𝗴𝗶𝗳𝘁𝘀 𝗰𝗿𝗲𝗮𝘁𝗲 𝗮 𝗿𝗲𝗶𝗻𝗳𝗼𝗿𝗰𝗶𝗻𝗴 𝗿𝗲𝗹𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗹𝗼𝗼𝗽. 𝗠𝗼𝗻𝗲𝘁𝗮𝗿𝘆 𝗴𝗶𝗳𝘁𝘀 𝗱𝗼𝗻’𝘁. In sum, money answers: “Is this worth my time?” Recognition answers: “Am I valued here?” Monetary rewards influence behavior. They don’t necessarily build engagement and commitment.

When I worked on Google Pay, we had the opportunity of having Daniel Kahneman, better known as the author of “Thinking Fast and Slow” advising us. By focusing on creating delight, we outpaced other payment apps with only ¼ the marketing spend, and only ¼ as many features as the competition. Here’s how we did it. Nobody at the time thought of payments as an activity that could be fun, or delightful. It was just something that you had to get done, and be over with as quickly, and cheaply as possible. That’s why all the fintech companies were competing to provide more features, better integrations, lower fees, and faster payments. But eventually, everyone starts hawking the same features, and margins start trending to zero. Worse, Google Pay was a latecomer to the payments space! We had nowhere near the same number of features that other payment apps offered. We knew that we couldn’t possibly compete on rational factors alone. Instead, we set out to design the most delightful payments experience of all. In the words of Marie Kondo, we wanted to create moments that could “spark joy” for users. 𝟭) 𝗗𝗲𝗰𝗶𝘀𝗶𝗼𝗻 𝗔𝗳𝗳𝗲𝗰𝘁 𝗧𝗵𝗲𝗼𝗿𝘆 Unexpected good outcomes feel better to our brains. Each time someone paid with Google Pay, they would get a reward in the form of a virtual scratch card. It would reveal either a variable cashback amount or discounts. We turned payments from a painful, dull activity into a delightful surprise. This not only kept people coming back to Google Pay for the dopamine hits, but they even started creating video tutorials on YouTube to tell people about it. 𝟮) 𝗥𝗲𝗰𝗶𝗽𝗿𝗼𝗰𝗶𝘁𝘆 Payments are embedded within human relationships, like splitting a bill from a night out with friends. People feel obligated to return favors. So, we designed a referral program in which both the referrer and the new user, would get a reward for using the app to make payments to a new contact. Existing users started to nudge everyone in their friends and family circles to start sending money to each other using Google Pay. We turned Google Pay into one of the most prominent mobile payment apps in India, and even other countries like Canada, and the US. I’m trying to do the same for personal finance. Today, tracking your net worth, spending, investments, and planning for certain financial goals is dull, overwhelming, and a hassle. I believe there’s a better way. Why can’t it be as delightful as tracking your workouts, or leveling up a character in a video game? If you’re interested to know more, check out our beta program in the comments below!

Today, I came across Rapido's “Walk & Save” feature, which gently nudges users to walk a short distance to a more accessible pickup point & thus rewards them with a visible fare reduction. While it may seem like just another discount mechanism, a closer examination reveals a rich amalgamation of product and design thinking at play. ✅ Product Principle → Empower the User by Offering Choice and Tangible Incentives ↳By surfacing pickup alternatives (e.g., “Nearby Pickup – Walk & Save ₹113” vs. “Current Location – ₹127”), Rapido empowers users with a clear trade-off: walk a little, pay a little less, and maybe even get picked up faster. ↳This is an embodiment of the “user-in-the-loop” principle, where the user, not the system, makes the value-driven choice. ✅User Pain Point → Solving for Unpredictable Wait Times and Inaccurate Pickups ↳Users often deal with delayed rides due to hard-to-reach (or crowded) pickup points, leading to frustration and extended wait times. ↳By nudging users to more optimal pickup spots (where rides can reach them quickly), Rapido is directly addressing both sides of the marketplace: drivers save time/costs, and users get faster, cheaper rides. ✅ Product Design Principle in play 👇 1. Nudges to Drive Win-Win Behavior ↳ The design doesn’t force; rather, it nudges users with subtle labels like “Walk & Save.” ↳Savings are made explicit and prominent, anchoring the benefit both mentally and visually. ↳The current flow gives agency without guilt; the “Continue from Current Pickup” option still remains, ensuring the user never feels trapped. 2. Frictionless, Contextual Comparison ↳Both options (Nearby Pickup vs Current Location) are compared side-by-side: cost, route, and context are transparent. ↳The product minimizes cognitive load, so the user sees what they save and exactly where they need to walk, making decision-making effortless. 3. Micro-Moment Optimization ↳Designers capitalize on the “micro-moment” just before confirming a ride, leveraging user attention to drive engagement with this feature. ↳The UI aligns with behavioral science: show the cumulative benefits of small changes in routine behavior. What are your learnings from this feature? Share in comments. #UXDesign #Rapido #ProductManagement #MobileUX #UserExperience #DesignThinking #ProductManager #ProductDesign #UIUX #Design 

I think we’re measuring the wrong stuff… and it’s quietly killing momentum. 2026 has to be the year we fix it. Impressions. Clicks. MQLs. “Engagement.” The real game is happening in DMs, Slack threads, forwarded newsletters, and meetings. Here are 6 metrics I’d focus on in 2026 GTM (and why they matter). 1) Conversations → conversions What it is: Of the conversations your content starts, how many turn into a real next step (intro, meeting, opp). Why it matters: Content doesn’t “generate leads.” It generates conversations. Pipeline comes from what you do next. How to track: Tag every inbound convo (DM/email/reply) and mark the outcome: no fit / nurture / meeting / opp. 2) REAL ICPs engaging with content What it is: Not “engagement.” Engagement from the right people (titles, seniority, company tier, intent). Why it matters: 1 CFO at a target account > 1,000 random likes. How to track: Maintain an ICP list (titles + account tiers) and measure: % of engagers who match ICP of target accounts engaged per week repeat ICP engagers (X touches in 30 days) 3) Brand mentions inside ICP-relevant conversations What it is: How often your brand comes up when your ICP is discussing the problem you solve (not when you post). Why it matters: This is the difference between “content that performs” and a brand that gets recommended. How to track: Collect signals: customer calls (“we heard about you from…”), community moderators, partner chatter, dark social screenshots, and sales intel. Even a simple monthly “mention log” works. 4) Conversation velocity What it is: The speed from publish → first qualified conversation, and from convo → meeting. Why it matters: Velocity is the earliest indicator your messaging is landing. If it’s slow, you’re not sharp enough yet. How to track: time-to-first-ICP-convo after a post/report time-to-meeting after first touch “conversation depth” score (comment → DM → problem share → meeting ask) 5) Brand + category position What it is: Are you being associated with a clear “lane” (category/point of view) or just “a vendor who posts”? Why it matters: In 2026, positioning is distribution. If people can’t summarize your POV in one sentence, you’re invisible. How to track: Quarterly “message recall” check: ask prospects/customers: “What do we do?” “What do we believe?” “What are we known for?” 6) Dark social + word-of-mouth What it is: The off-platform sharing that actually drives deals: forwards, screenshots, Slack drops, “my friend sent me this.” Why it matters: A huge percentage of B2B buying happens in private. If your GTM can’t see dark social, you’re flying blind. How to track: “How did you find us?” (mandatory field) inbound screenshots / Slack mentions private replies after posts If your 2026 GTM dashboard doesn’t include conversations, ICP quality, dark social, and category position, it’s going to keep optimizing for attention… while someone else captures intent.

#LinkedIn is saturated with people selling “growth hacks.” The uncomfortable truth: no one actually understands the algorithm end-to-end. Most advice is recycled folklore—outdated tests, anecdotal wins, or short-lived spikes mistaken for strategy. Based on direct observation across thousands of posts in 2025–2026, the algorithm consistently rewards three things: relevance, demonstrated expertise, and genuine conversation within your professional graph. Not viral reach. Not theatrics. You don’t need to stand out to everyone. You need to stand out to the people who matter in your niche. LinkedIn evaluates your content primarily against your 1st- and 2nd-degree network, shared industries, and topical authority—not the entire platform. Growth is contextual, not global. What actually moves the needle: 1. Comments now outperform original posts. Thoughtful comments (15+ words) from relevant professionals often generate 2–5× the reach of likes. One recent comment crossed 60K impressions while the original post stayed under 100 likes. Comments drive dwell time, signal credibility, and travel deeper into niche feeds. → Five to ten substantive comments per day in your domain will outperform random posting. 2. Depth beats volume. Every time. The algorithm tracks engagement quality: long comments, threaded discussion, saves, and shares with context. Ten real conversations outperform 500 drive-by reactions. Engagement bait (“Comment YES”) is now neutral at best—and often penalized. 3. Consistency matters—but only within a clear niche. Two to five posts per week is sufficient. What matters is topical focus. Stick to your lane. Authority signals compound when your content reinforces a coherent expertise narrative. Text posts and carousels routinely outperform flashy formats if they trigger real discussion. 4. Design for conversation, not applause. Strong opening lines and experience-backed insights win. Ask questions that invite expertise, not agreement. Respond quickly—especially in the first hour. Early interaction materially boosts distribution. 5. Reciprocity is not optional. Engage first. The algorithm favors mutual visibility within professional clusters. When respected peers comment on your posts, distribution expands—organically and predictably. 6. Dwell time is a hard metric. Optimize for it. External links suppress reach. If you must share one, place it in the comments. Native text, documents, and carousels consistently generate longer session time—and better reach. 7. Your profile is part of the algorithm. Headline, About section, and experience shape how LinkedIn classifies you. A fuzzy profile leads to fuzzy distribution. Authority attracts authority. Bottom line: LinkedIn growth in 2026 is not about gaming the system. It’s about being useful, credible, and consistent in your corner of the ecosystem. Quality compounds. Noise disappears. #LinkedInGrowth #PersonalBranding #ContentStrategy #ProfessionalVisibility

Quick exercise: "User engagement is down 20%" Add context ↓ "...among power users" Add more context ↓ "...after we simplified the advanced features" Add wisdom ↓ "We optimized for new users at the cost of power user productivity" This is the power of context in product decisions. It transforms: DATA: Raw numbers without meaning "Engagement dropped by 20%" INFORMATION: Numbers with identified relationships "Power user engagement dropped after the UI refresh" KNOWLEDGE: Patterns that tell a story "Every time we simplify advanced features, we see power user drop-off" WISDOM: Principles that guide decisions "Sustainable growth requires balancing simplicity for new users with power user productivity" Without context, data is just noise. With context, data drives informed decisions. The best product decisions aren't made by those with the most data, but by those who best understand its context. #ProductManagement #Leadership #ProductStrategy

Measurement obsession is creating significant gaps in our marketing understanding. I recently observed a company significantly reduce their paid social budget after their last-click attribution model suggested Google search was driving all meaningful conversions. The pipeline showed a marked decline as a result. The reason was straightforward: social had been priming their audience before they searched, but this connection wasn't visible in their outdated attribution model. This pattern is more common than typically acknowledged: • 64% of marketing leaders express scepticism about their tracking data reliability • 42% of the buying decision process occurs before tracking systems detect intent • The average B2B buying committee consists of 5.4 stakeholders • Only 5% of your addressable market is actively purchasing at any given time Most prospect journeys happen in unmeasurable channels: private WhatsApp conversations, Slack communities, LinkedIn DMs, and professional networks where buyers exchange perspectives. This measurement gap is particularly evident in mature B2B categories with higher annual contract values. As sale complexity increases, attribution systems capture proportionally less of the complete journey. Practical approaches to address this: • Develop content that stimulates genuine conversation, reaching your ideal customer profile before active purchase intent • Implement intent-based and behavioural signals to help your sales team prioritise meaningfully engaged prospects • Utilise brand tracking metrics such as share of voice to better understand your brand's presence prior to measurable touchpoints • Account for what you can measure while acknowledging the limitations Marketing strategies that focus exclusively on immediate, attributable ROI often miss critical engagement points. Your brand exists in unmeasured spaces—in professional conversations and prospect consideration—long before formal engagement. Balance short-term metrics with long-term brand development. This isn't about abandoning measurement but rather complementing it with a more complete view of market engagement.

Imagine this: you’re filling out a survey and come across a question instructing you to answer 1 for Yes and 0 for No. As if that wasn't bad enough, the instructions are at the top of the page, and when you scroll to answer some of the questions, you’ve lost sight of what 1 and 0 means. Why is this an accessibility fail? Memory Burden: Not everyone can remember instructions after scrolling, especially those with cognitive disabilities or short-term memory challenges. Screen Readers: For people using assistive technologies, the separation between the instructions and the input field creates confusion. By the time they navigate to the input, the context might be lost. Universal Design: It’s frustrating and time-consuming to repeatedly scroll up and down to confirm what the numbers mean. You can improve this type of survey by: 1. Placing clear labels next to each input (e.g., "1 = Yes, 0 = No"). 2. Better yet, use intuitive design and replace numbers with a combo box or radio buttons labeled "Yes" and "No." 3. Group the questions by topic. 4. Use headers and field groups to break them up for screen reader users. 5. Only display five or six at a time so people don't get overwhelmed and bail out. 6. Ensure instructions remain visible or are repeated near the question for easy reference. Accessibility isn’t just a "nice to have." It’s critical to ensure everyone can participate. Don’t let bad design create barriers and invalidate your survey results. Alt: A screen shot of a survey containing numerous questions with an instructing you to answer 1 for Yes and 0 for No. The instruction is written at the top and it gets lost when you scroll down to answer other questions. #AccessibilityFailFriday #AccessibilityMatters #InclusiveDesign #UXBestPractices #DigitalAccessibility

Founders, are you building product correctly? As founders, it’s easy to get pulled into thinking about how our products might look in a slick promotional video, imagining all the ways they could "wow" an audience. But here’s the reality: sustainable success is rooted in solving real problems, not just creating marketable moments. A flashy demo might generate short-term buzz, but what keeps users coming back is a product that fits their lworkflows and goals. If we’re building for the customer, our focus has to shift from "How will this feature look?" to "How will this feature help?" Here are a few actionable steps for founders to make sure their product development stays grounded in customer value: 1. Talk to Your Users Regularly: This sounds obvious, but it’s surprising how often it’s overlooked. Get into the habit of scheduling regular conversations with both loyal customers and recent adopters. Ask open-ended questions that let you uncover not just what users want but why they want it. 2. Focus on Solving Pain Points, Not Adding Bells and Whistles: It’s tempting to add features because they seem cool or have a high "wow factor" in demos. But before committing, ask yourself: does this feature directly address a specific pain point? Is it making the product better or just flashier? 3. Design with Iteration in Mind: Building a product isn’t about getting it perfect the first time; it’s about continual improvement. Make sure your team has room to iterate, experiment, and adjust based on feedback—don’t lock them into something just because it looked good in a marketing draft. 4. Measure Success Through Customer Retention, Not Just Acquisition: A flashy feature may attract first-time users, but a product that truly solves problems will keep them coming back. Focus your KPIs and metrics on retention and user satisfaction, not just on the top of the funnel. 5. Think Like Your User, Not Just Like a Founder: It’s easy to fall in love with your own ideas, but users ultimately decide whether your product thrives. Ground yourself in their perspective: what’s essential to them, what frustrates them, and how your product can make a meaningful difference. At the end of the day, the best marketing doesn’t come from a video—it comes from a product that meets needs so well that users feel compelled to share it. Build for impact, not for optics.