... help makers answer questions like: Which categories are becoming more competitive? Which hunters are consistently active? When is the best time to launch? What engagement patterns correlate with successful launches? If you could see any Product Hunt metric or visualization, What Product Hunt metrics

... word-of-mouth from people who barely use the app anymore. They recommend Murror precisely because it helped them move forward and that story is more compelling than any ad. We had to redefine our north star metric. Instead of DAU, we track what we call "resolution moments" instances where a user's journaling pattern around a specific issue shifts from repetitive to exploratory to resolved. That's our real growth number. We learned to be honest in our marketing ... ... already understood wellness or mental health they got it immediately. But for generalist investors, we had to reframe. Instead of saying "our power users barely use us," we'd say: "Our users reach meaningful outcomes faster than any competitor

... system starts optimizing for the wrong thing. Instead of asking Can a human actually work with this? , we continue asking How much did we generate? or How many agents did we spin up today? - are those the success metrics we want? A clear example of this is what we re seeing in AI-generated UIs for landing pages. Tools like Claude (and others) can produce interfaces quickly, but they often converge into a very recognizable template. Same layout patterns, same ... ... relentlessly pushing his team to maximize token usage. His reasoning was interesting: before you can optimize quality, you need to overcome inertia. Most teams are still underusing AI because they default to familiar workflows. Token usage is a crude metric

... about to leave. We had retention data. We had churn data. But it was backwards. You only knew someone churned after they cancelled. By then, it was too late. So we looked for a leading indicator. One metric that predicted churn before it happened. The data We pulled 18 months of usage data from 1,247 accounts. We looked at every action users took in the 30 days before they cancelled. We compared them to users who stayed. The most ... ... days, we asked for feedback on why they stopped coming. Retention improved by 23% in 90 days. Not because we changed the product. Because we started paying attention to the people who were quietly leaving. The lesson Vanity metrics

... Most product teams track acquisition, activation, retention. The usual funnel. We track all of that at Murror too. But there's one metric we started paying attention to that changed how we think about growth entirely: how often users talk about themselves differently after using the product. Not NPS. Not referrals. Not even engagement time. We look at the language shift. When someone starts a session saying "I don't know why I keep doing this" and ends it saying ... ... think I do this because..." -- that's the moment we care about. It's not a conversion. It's a transformation. Here's why this matters for marketing: Traditional growth metrics

... games, adding new mechanics, polishing feel, and shipping fun features. What I hate? App store top charts. After shipping my first mobile game, I got buried in search results. I spent way too many nights manually checking rankings, stalking competitors, rewriting metadata, and still felt lost. The existing ASO tools were either $100 200+/month enterprise dashboards or generic AI that didn't understand my actual data. So I built what I actually wanted. Third Eye Metrics

... been building MetricSync as a solo dev because I kept seeing the same issue: people do not quit nutrition tracking because they hate the goal. They quit because the logging loop gets annoying or stops feeling trustworthy. The current bet is simple: cheaper than CalAI 3 day free trial focused on faster logging and easier corrections trying to make AI nutrition tracking feel useful after week one, not just magical on day one I'd genuinely love feedback from anyone ... ... tried CalAI, MyFitnessPal, or Cronometer. What usually makes you stop trusting a nutrition app first: accuracy, correction friction, feature bloat, or pricing? If you want to poke at it: https://metricsync.download

... Built MetricSync because most AI calorie trackers still feel too rough around the edges when you actually use them every day.   What I wanted was simple: faster logging, better food recognition, and enough detail that I would not have to keep second guessing the numbers.   If you have tried CalAI, MetricSync

... nutrition trackers more than the actual habit. For me the friction was not taking the photo. It was fixing bad entries, losing detail, and paying before I knew if I would stick with it. So I built MetricSync. What I focused on: - cheaper entry point than CalAI - more nutrition detail once you log consistently - a 3 day free trial so people can test it before paying - faster AI food logging without the usual admin feeling Still tuning the product ...

... kept running into the same issue with AI nutrition apps: they looked slick, but the logging was off just enough that I would stop trusting the numbers. So I built MetricSync, an AI nutrition tracker focused on being more accurate in day to day use while still staying cheaper than CalAI. Right now the pitch is simple: - cheaper than CalAI - more features - better accuracy - 3 day free trial It is here if anyone wants to try it: https://www.metricsync.download ...

... built MetricSync after getting annoyed with how clunky most nutrition apps feel once you move past perfect single-item meals. The pitch is simple. It is cheaper than CalAI, gives you more ways to log food with photo, barcode, and natural language input, and we have been pushing hard on better accuracy for messy real meals instead of just clean product shots. There is also a 3 day free trial because nobody should pay before seeing if it actually fits ...

... Been testing AI nutrition trackers hard and kept running into the same thing: CalAI is solid, but I wanted something cheaper with more features and better accuracy on real meals. So I built MetricSync. It is an AI nutrition tracker with a 3 day free trial: www.metricsync.download Main goal was making logging fast without getting garbage estimates back. Right now the pitch is pretty simple: cheaper than CalAI more features better accuracy in my testing If you are actively using ...

... first year of building Murror, we optimized for the same metrics every other app optimizes for: daily active users, session length, screens per visit. The dashboard looked healthy. Usage was growing. We felt good about it. But something was off. Our most engaged users were not our happiest users. People who spent the most time in the app were often the ones who left the harshest feedback. Meanwhile, users who opened the app twice a week for five minutes were ... ... writing us emails about how it changed how they handle difficult conversations. We were measuring activity when we should have been measuring impact. So we ran an experiment. For one quarter, we replaced our engagement metrics

... months figuring out how to track this properly and turn it into a service agencies can actually sell. Not some complicated AI audit. Just a simple monthly report that shows clients where they stand in AI search, how their competitors are doing, and what to do about it. Agencies adding this are charging between $200 and $500 extra per month per client for it. The conversation is easy because the data is new and clients have never seen it before ... ... wrote a free playbook covering the whole thing. What AI visibility actually is. The metrics

... first year of building Murror, DAU was the number we checked every morning. It was the first thing on our dashboard, the first metric in every team meeting, and the number we used to judge whether a feature was working. Then one day we noticed something strange: our DAU was climbing, but our NPS was dropping. People were opening the app more often, but they were less happy with it. Some of our most engaged users were showing signs ... ... compulsive checking" opening Murror out of habit rather than intention. That was a turning point for us. We realized that for a product designed to help people reflect on their emotions, daily active usage was not just a vanity metric

... months, our most requested feature at Murror was a chat function. Users wanted to talk to the AI the way they talk to a friend. It seemed obvious. Every competitor had it. Every feedback form mentioned it. So we built it. And within two weeks, our core metrics

... most important and challenging experiences you ll have as a founder is fundraising. I pitched VCs across London and San Francisco. Some spent 15 seconds on my deck. Some took the call just to diligence a competitor. One told me to ask for more money. Oh, and I found out your pitch is probably being recorded and run through AI. This is the high-impact version of all of those lessons to save you time, energy, and emotional damage. Please ... ... there is more venture money than ever. But it s concentrating into fewer bets. So yes, capital exists. But access to it feels tighter. Which means you are often competing in smaller, more competitive pools, against companies with stronger metrics

... stopped trusting what people said and started tracking what they actually did. The dataset We pulled 12 months of usage data from Rankfender. 1,247 active users. 27 distinct features across 8 modules. We looked at three metrics for each feature: Adoption rate % of users who tried it at least once Retention rate % who used it again after 30 days Request volume how many users asked for it The gap between what people asked for and what they actually used ... ... every day? We tracked the top 10% of accounts by engagement. Their most-used feature was the Workflow Engine. Not because they requested it. Because it saved them time. They built automation for content publishing, citation alerts, and competitor

... Last month, I did something that felt slightly insane. I took our product description, fed it into ChatGPT, and asked it to build a competitor. Not a parody. A real competitor

... Something clicked for me this week reading about what's happening in the advertising industry. AEDEMO 2026 basically declared the end of GRPs those broad reach metrics the ad world has used for decades. A major Spanish broadcaster just launched an AI system that measures actual attention in TV ads: not impressions, not eyeballs-in-the-room, but genuine cognitive engagement. The industry finally admitted it had been measuring what was easy to count, not what actually mattered ... ... tracking mention volume. Spikes up? Something's happening. Stays flat? You're fine. It feels rigorous. It produces charts. It goes in the quarterly deck. But mention volume is to reputation what GRPs were to advertising a proxy metric

... from the community. Overview: Understand Your Brand s AI Position in One Minute The Overview module has a very clear goal: to help users quickly grasp their brand s overall performance and trends within generative engines. 1.1 Four Core Metrics The Overview page focuses on four key metrics

... Flow Metrics is a dashboard built for founders who want a simple way to understand their real business metrics

... Zavion, the founder of Flow Metrics. I m an entrepreneur who has spent the last few years building and operating businesses. During that time I realized something frustrating most founders track their business data across multiple tools and dashboards, but none of them clearly show the real metrics

... three variables that define a day s success. Success used to be measured by hours worked or tasks completed. Now, he started measuring the inputs that truly mattered: Mood, Energy, and Control. These three small metrics were the vital signs of his professional engine. Tracking them daily wasn't monitoring; it was owning the fuel that ran his career. A heartbeat thought by Christian van Gils Use your thermometer for sickness leave prevention: www.pulseboard.nl Talent ...

... regulations have changed the rules: consent banners are required, a meaningful share of users opt out, analytics data becomes incomplete by default. This is not a GA4 bug. It s the reality of a privacy-first web. Practical consequence Metrics should no longer be treated as exact truth they are approximations, influenced by consent and browser behavior. 3. Server-side tracking helps, but introduces new costs and responsibilities Server-side analytics can: reduce dependency on the browser, provide more control ... ... Most products operate somewhere between: best effort compliance and we can t afford full legal and technical certainty . What matters in practice: not every tool fits every risk profile, analytics choices are strategic decisions, not just technical ones. Why alternative