Prototype Feedback Integration

People teams are always expected to do things perfectly. They’re expected to come up with a preened policy, a polished performance process, a precise career path framework. And this perfection-expectation can be an absolute mare to wrestle with when you’re trying to work like a product team. Especially when it comes to prototyping. Because that’s the messy stuff. The shitty first draft. The first pancake. The scrappy sketch. And it's also gold dust when it comes to getting feedback. “Oh feedback?” you say. “But we have no problem getting feedback from our teams. In fact, when are we not getting feedback of some kind?!” Sure. But I'm guessing most of that feedback is reactive. Not invited, structured, or tied to something you’re testing, am I right?! So how can you confidently share a very-much-not-finished prototype and still feel in control of what you learn from it? Enter: Pitch it, Break it, Build it, Fix it (anyone else hear Daft Punk when they read that out loud?) A simple card to help you capture and test people experience ideas with more confidence, and fewer perfectionist spirals (well, at least we can work our way up to that, eh?!) Here’s how it works: → Pitch it Explain what the idea is and why it matters. Who’s it for? What problem does it solve? What’s the most basic version you could test? → Break it Invite your team to poke holes in it. Where might it fail? What’s unclear? What wouldn’t land, and why? → Build it Now rebuild the idea with them, based on what you’ve learned. What still holds up? What needs to evolve? How could it become more workable, testable, useful? → Ship it Work with your team to get it in front of real users, fast, light, and focused. What’s the smallest, real-world test you could run? How will you know what’s working? Use it in your team retro, a 1-1 session, or when shaping a new idea with stakeholders. It works best when you keep it low-fi, short, and curious. 👇 Grab the card below and give it a spin. Your first pancake is waiting, I can’t wait to see what you cook up. 🥞 #PeopleOps #Prototyping #Innovation ___________ Hi 👋 I'm Alicia, co-founder of The Future Kind. I’m a facilitator, designer & systems thinker working with leaders and people teams to build innovation cultures and make work work. Want to know more? Follow along or DM me, I love to hear form you. 💌

I’ve been using Cursor to communicate product thinking visually - a quick prototype can speak louder than ten PRDs. But the true game changer I've found is using AI to scale customer understanding. Back at Notion, our team used Enterpret across every stage of building product: 1. Strategy & Roadmapping We brought together feedback from Zendesk, Slack, app store review, social media, Gong, and more. Enterpret automatically categorized themes—top requests, bugs, positive signals—and surfaced them in clean, usable dashboards. Before that, synthesizing feedback was a manual, messy process. PMs spent hours hopping across tools and teams just to find signal. 2. Project Scoping & Validation Once we aligned on priorities, we used Enterpret to dig deeper: What exactly were users asking for? What did they mean? It surfaced quotes, summarized needs, and even helped us identify users for UXR or early testing. The Wisdom feature let us ask questions like: - “What are the top security asks from IT admins?” - “Which integrations do paid customers request most often?” …and get real answers, fast. 3. Post-Launch Sentiment & Closing the Loop After GA, we’d track how sentiment shifted. Did we actually solve the right problems? Who originally asked for the feature—and did we follow up with them personally? Enterpret made that easy, especially for teams without a dedicated UXR or Product Ops teammates. It helped us act faster and more confidently—anchored in real customer signal. If you're trying to bring all your customer signals into one place and move faster with real insight, happy to walk you through how Enterpret works in practice. Feel free to book a quick demo here: https://lnkd.in/e53YWhnv

Not all early feedback is created equal. How to prioritize without panic. One of the things I always keep pushing is getting early feedback on your game prototype, and pushing for early feedback or Closed Alpha tests. The biggest feedback I get on that is "but if we do that, we will get thousands of pieces of feedback, 500 are 'Urgent' and 100 are 'game-breaking', and we haven't even shown them the game properly yet - where would we even start if we did this??" When building a live game for the first time, a 'panic-driven' approach to early player feedback is dangerous and sets your team up for failure. What you need to do is be ruthless in your prioritization. Fixing the 'wrong' things first can be just as damaging as fixing nothing. Here’s how you overcome the challenge of prioritization: ⚠️ Every bug feels like an "all-hands-on-deck" crisis. ➡️ Prioritize by "impact x frequency." A "game-breaking" bug that 0.1% of players can sometimes trigger is less urgent than a "minor" UI bug that 100% of your players hit in the first 10 seconds of the tutorial. ⚠️ Your most engaged vets are complaining about the "endgame grind." ➡️ Fix the "leaky bucket" first. Your FTUE (First-Time User Experience) is your #1 priority, full stop. You cannot service your 100-hour veterans if 50% of your new players (who you paid a CPI for) are churning in the first 10 minutes. ⚠️ The "loudest" complaint on Discord is dominating the conversation. ➡️ Validate with data before it hits the backlog. Is this a "loud minority" of 20 people, or is your telemetry showing a real, widespread behavioural change? The "loudest" is almost never the "most important." ⚠️ A bug is blocking logging in or monetization (e.g., the "Buy" button is broken or players cannot access the game on Xbox). ➡️ This is what we called "can't play / can't pay --> P0 --> drop everything and fix it now". Any bug that stops a player from being able to play or from giving you money or accessing what they paid for gets fixed now. This is the business. ⚠️ Feedback is vague and un-actionable (e.g., "The game feels laggy" or "The fun just stops"). ➡️ Don't dismiss it, but don't act on it. Put this in a "needs more investigation" bucket. Look for correlating data. ("Aha, players say it 'feels bad' and our data shows a 40% drop-off after Mission 3. Let's investigate Mission 3."). ⚠️ Your team has a feature roadmap, but feedback is pulling them in another direction. ➡️ Balance your backlog. You must run two parallel workstreams: "New Feature Development" (your long-term roadmap) and "Live Issues & Iteration" (the feedback). You need to budget time for both every single sprint, or you will never get ahead. A good live game strategy isn't reactive; it's a disciplined, data-informed process of triage and execution. This takes time to make it second nature for you and the team - practice it early, at any opportunity you can get!

"AI Prototypes are the new PRDs," everyone says. They're how we jam with teammates, align leaders, and rally excitement. But if you're only showing your prototypes off internally, you're missing the point. Tools like Bolt, Lovable, and Cursor are incredible. What used to take weeks and an engineer or two, now takes minutes. You can easily explore dozens of “what ifs,” compare countless variations, and polish tiny interaction details. Amazing, yes! But here’s the danger: without real user feedback, you’re just optimizing for demo wow. Product teams need to treat AI prototypes as the starting point, not the finish line. As questions, not answers. Next time you’re tempted to stop at the demo candy, try one of these instead: 1️⃣ Exploring a new idea? Prototype your best “wow” moments. Get users’ honest reactions. Listen, don’t pitch. 2️⃣ Testing usability? Build core workflows, including error states. Watch where people stumble. 3️⃣ Comparing options? Let users see. But don't just go with which one people "like" best, but which solution best meets your goals. A few tips: 💡 Tools like Maze or Optimal let you recruit and test in hours. Even a few participants will teach you something new (or at the very least, build your confidence). 💡 With AI prototyping, iteration loops are supersonic. Set a weekly or fortnightly cadence of testing within your team. 💡 Don't forget to prototype and test on multiple devices, like mobile. 💡 Match the form factor to reality: SaaS flows might be fine for unmoderated tests on desktop. But if your product is used in other contexts—for example hospitals or classrooms—get out of the building and test there too. In the AI era, the best PMs won't be the ones who vibe code the fastest, or the flashiest internally. They’re the ones who actually turn that speed into learning, confidence, and shipping speed for their users. #AIprototyping #vibecoding

What if customers didn’t just give you feedback… but actually built your product? A few months ago, I was talking to a founder who said something like this: Founder: “I want to rebuild our website. I want to send the website to target users, have them go through the onboarding, record their thoughts. Then I want to send the entirety of the feedback to my AI coding agent to rebuild the website.” Me: “But don’t you want to go through the feedback first?” Founder: “No. It would take me forever to read through all the transcripts and I don’t want to miss anything. Also I think my customers will be able to say what needs to change better than I can.” Me: “That’s interesting, I’d love to hear how it goes.” The next day this founder launched a new website using this exact approach. Since then, I’ve seen a bunch of our customers do this sort of thing. At first, I thought it was strange. Over time, I’ve come to think there’s something noteworthy going on. As a former PM, I’ve spent most of my career singularly focused on one thing: bringing the voice of the customer into every decision. In practice, this involves doing lots of customer interviews, synthesizing feedback across various sources, and trying (and often failing) to rally the team around the customer. One of the things I learned: unfiltered feedback is always better. Simply bringing a customer to a team meeting always worked better than creating a doc with the “synthesized” learnings. With AI coding agents, it’s suddenly trivial to take feedback from 100 customers and have all of it incorporated. No lossy filter. No PM interpretation. I decided to run a quick experiment: build an app where I had zero involvement whatsoever, but instead put target users in the driver’s seat. The process: • Asked Lovable to create a prototype for a missing pet app • Used Voicepanel to send it to 20 people (10 cat owners, 10 dog owners) • Asked them to share detailed feedback based on real missing pet experiences • Sent all the raw feedback back to Lovable to iterate • Repeated this process a couple of times Total time spent: 30 minutes. As someone who can obsess over product details, I found this exercise quite liberating. A few learnings: • Most testers rated the v1 prototype a 5/5 on their likelihood to use. Turns out Lovable is pretty good at prototyping! But also turns out you can’t rely on rating scales - the qualitative data told a completely different story. • Testers shared their detailed stories and what this app would actually need to do to be useful. v2 of the prototype incorporated community social proof, self-help guidance, microchip tracking, map view, and more. • Dog and cat owners, not surprisingly, want different things. The v3 prototypes diverged significantly despite using the exact same prompts at each step. Your target customer matters! Is anyone out there sending customer feedback directly to their AI coding agents? How’s it going?

Customer discovery via functional prototypes + PostHog is night & day better than the old school way of asking for feedback on Figma mockups. Here's why: I get to observe actual user behavior instead of asking the user to guess how they might use my product. My favorite example of why this matters comes from a Sony Walkman user study. They asked a bunch of people what they thought about a yellow walkman and they said "so sporty! not boring like the black one!". And yet, when they were given the opportunity to take a walkman home after the study, everyone picked the black one. We learned a lot more from user behavior than we did expressed preferences. Here's my setup for now observing user behavior from prototypes: 1. Create a functional prototype in your favorite prototyping tool (Bolt, Lovable, Reforge Build, Magic Patterns, Claude Code) 2. Ask the prototyping tool to integrate PostHog analytics 3. Ask the prototyping tool to instrument key user actions in PostHog Then you get all of these ways of observing actual behavior: - DAUs \ WAUs \ retention curves - I can actually see if people come back and use my prototype instead of taking their word for it - Action metrics dashboards - I can see what actions people are taking vs not - Post-usage survey - I can add a built-in pop-up survey to ask the user a question about the experience after they have engaged with the prototype - Session replays - I can see exactly where people are clicking and how they are using the product to identify usability issues - Heatmaps - I can see what part of my design is working across all sessions I'd never go back to testing with just a mockup after this.

"we need to analyze all this user feedback" = every startup's famous last words before drowning in spreadsheets... tip: everything changed when we connected Replit to our google workspace (it changed how we build products) context: we launch news monthly. feedback pouring in through google forms, sheets, docs. the usual chaos. traditional approach would be: → export everything → manual categorization → long meetings debating priorities → specs for developers → wait weeks for prototypes what we did instead: logged into replit, connected our google workspace (one click, no api keys), and gave their agent this prompt in plain english: "pull all user feedback from our google drive, identify the top requested features, create working prototypes for each, and rank them by user impact" the agent analyzed the feedback + built actual working prototypes. here's what the agent created: → testable prototypes for top 3 features → priority matrix based on mention frequency → implementation notes for our dev team → automated pipeline for new feedback from feedback to working prototype in under 30 minutes. the prototypes aren't perfect. but they're real. users can try them. we can iterate based on actual usage, not assumptions. we're saving time on analysis obviously.... but more importantly we're compressing the entire feedback-to-feature cycle with an agent that actually builds. imagine: every piece of user feedback automatically turns into something they can actually touch and test. no interpretation layers. no priority debates. just tell the agent what you want in plain english. that's what happens when you stop trying to optimize workflows and start eliminating them entirely (which i've written many times about before) i'd encourage you to go test it out... you can build apps & automations on top of your data with connectors (it's with replit agent 3) who else is tired of the feedback → meeting → spec → build cycle? #productmanagement #buildinpublic #startuplife #automation #replit

Your AI SDR algorithm needs sales domain feedback for updates. How can you effectively integrate it? Discover how to effectively integrate sales domain feedback into your AI SDR algorithm. Enhance performance with real-time analysis and robust MLOps strategies. Domain Feedback Integration Strategy Data Collection and Processing A multi-disciplinary approach integrating customized algorithms and statistical methods alongside machine learning technologies is essential for processing sales domain feedback. The platform should systematically centralize, collect, and structure data from diverse sources, including Excel files, SQL databases, and other relevant sales data formats. Adaptive Assessment Implementation Implement real-time analysis algorithms to evaluate sales performance metrics and patterns. The system should use machine learning algorithms to analyze responses and patterns, enabling dynamic adjustments based on sales-specific attributes. This allows for continuous assessment and refinement of the SDR algorithm's performance. MLOps Integration Feedback Loop Optimization Establish a comprehensive feedback mechanism that: Processes sparse rewards effectively Adapts to domain-specific requirements Implements real-time updates based on performance metrics The integration should prioritize scalability and maintainability while ensuring the system can efficiently handle daily operational requirements. Regular monitoring and evaluation of the algorithm's performance will help identify areas for improvement and necessary adjustments to maintain optimal effectiveness. #entrepreneurship #startups #sales

Steve Jobs kept rejecting the Mac calculator. Every day, the developer would incorporate his feedback. Every day, Jobs would find something new to criticise. The background was too dark. The buttons were too big. The lines were the wrong weight. It went on for days. Then the developer did something clever. Instead of going away and guessing again, he built a tool that let Jobs adjust every visual parameter himself: button sizes, line thickness, background patterns. All controlled through pull-down menus. Jobs sat down with it for ten minutes. Made his choices. Done. That design shipped with the Mac in 1984. It stayed virtually unchanged for 17 years. Here's what strikes me about that story: the problem was never Jobs's taste. It was the communication gap between what he wanted and what he could articulate. Once he could interact directly with the design rather than describe it in words, the answer came quickly. I see the same pattern in KPI design all the time. When a consultant (or a data team, or a senior manager) disappears into a room and returns with a finished KPI framework, the feedback cycle can be brutal. People push back. Revisions multiply. Weeks pass. And even when something is finally agreed, the people who'll be measured by it feel little ownership over it. Contrast that with what happens in a well-run KPI Tree workshop. When people are actively involved in building the tree, debating which drivers matter, and shortlisting the measures themselves, the output doesn't just arrive faster. It arrives with buy-in already built in. The same principle applies when designing dashboards. Showing stakeholders a finished dashboard and asking for feedback is a bit like Jobs being asked to approve someone else's calculator. People struggle to articulate what's wrong. Put them in a room with a prototype and let them interact with it, and you'll hear things like "that metric should be here, not there" and "I need to see this by region, not total." Specific, actionable, useful. The lesson from a ten-minute design session in 1982 still holds: you get better outcomes, faster, when the person who has to live with the result is involved in creating it. Not as a reviewer. As a participant. How much of your KPI or dashboard design process actually involves the people who'll use it? #KPIs #kpiblackbelt #performancemanagement #MeasureWhatMatters #Leadership

Building a product isn’t just about solving a problem - it’s about ensuring you solve the right problem, in a way that resonates with your users. Yet, so many products miss the mark, often because the feedback from the people who matter most - users - isn’t prioritized. The key to a great product lies in its alignment with real user needs. Ignoring feedback can lead to building features that no one uses or overlooking pain points that drive users away. In fact, 42% of startups fail because their products don’t address a genuine market need ( source: CB Insights). Starting with a Minimal Desirable Product (MDP) can help. This isn’t about launching the simplest version of your idea, but about delivering something functional that still brings delight - encouraging users to engage and share their insights. How to Integrate Feedback Effectively - Observe User Behavior: Watch how users interact with your product. Are there steps where they hesitate or struggle? Their actions often tell you more than their words. - Ask the Right Questions: Use surveys and interviews to go beyond surface-level feedback. Open-ended questions can reveal frustrations or desires you hadn’t anticipated. - Iterate, Don’t Hesitate: Apply feedback to refine your product. Prioritize changes that align with user needs and eliminate features that don’t serve a purpose. - Keep Listening: The market evolves, and so do user preferences. Regularly revisiting feedback ensures your product stays relevant. The Hidden Cost of Ignoring Feedback A study from Harvard Business Review shows that 35% of product features are never used, and 19% are rarely used. That’s not just a waste of resources - it’s a missed opportunity to deliver real value. Let’s be honest: integrating feedback is hard work. It’s not a one-time task but an ongoing commitment. Negative feedback can be tough to hear, but it’s often where the biggest opportunities for improvement lie. Great products are never built in isolation. How do you incorporate user feedback into your product journey? #innovation #technology #future #management #startups