... Calculating SaaS metrics is my weekend hobby, same as our family budget and the mortgage plan, which is a strange thing to admit. My conclusion so far: almost every number I report has a definition I can move, and the flattering one always survives. Nobody lies. The definition just drifts. MRR: annual contracts divided by twelve, correct, and setup fees in the same total, not correct. Nobody decided that, both arrive as money in the same month. LTV over ...

... honestly changed how we build. Every feature debate started with "does this get someone to value faster?" and it worked, activation went from 5% to over 50% (we wrote a blog post about it here ). But a single metric warps things too. Activation can climb while retention quietly slips, and I've caught myself ignoring real signals just because they weren't the number. So I keep flip-flopping between: One north star everyone recites 2 or 3 metrics

... experiment on the other side 2,400 people talking to sycophantic and non-sycophantic versions. The people getting the flattery rated it more trustworthy and said they were more likely to come back. Return rate. Trust. The exact metrics you stare at in your dashboard. And those same people walked away more convinced they were in the right and less willing to apologize or repair the actual relationship they'd come in about. Worse, they couldn't tell the difference ... ... uncomfortable and true. If my product optimizes that friction away, I've built a very sticky way to make people slightly worse. So we design against the default on purpose, and it costs us some of the warm-glow metrics

... Spent a chunk of this month checking metrics I assumed mattered and found out they didn't. Checked AI Overview traffic on three real sites in Search Console. Zero clicks, zero impressions, across all three. The AI search panic did not show up in the data at all, backlinks and normal indexing did all the actual work. Checked how far my own X replies traveled. Follower count never moved the number once. What decided reach was entirely which post ... ... smaller posts. Here on Product Hunt the surprising one is Contributor. It sits at 0 out of 5 no matter how many comments I write, because it only counts the room reacting to me, not me posting. Three different metrics

... ScrapeOps Proxy Aggregator, we've integrated around 50 proxy providers into a single tool. We continuously test and benchmark them to determine which providers offer the best performance at the lowest cost for different target websites. The main performance metric we've settled on is average success latency: How quickly does a provider return a successful response? Success rate or latency alone doesn't tell the full story. A provider might respond quickly but have a low success rate. Another ... ... these factors together. It helps us measure how much successful throughput we can send through a provider, how reliably it performs over time, and how much that successful traffic actually costs. We then combine this with pricing to calculate metrics

... lifetime views, and we're a data company (embarrassing to admit). What actually got read was a Slack message every Monday with the numbers already in it. Zero clicks required. So I've mostly changed my mind: if a metric matters, push it to where people already are. We leaned into this hard enough that we launched dashboard subscriptions today, so clearly I've picked a side. But I get the counterargument. Push turns into noise fast, people filter ... ... email by week 3, and pull at least guarantees that whoever's looking actually wanted to know. Where did you land? Do the metrics

... separating activity from impact . It's surprisingly easy to optimize for numbers that always go up. It's much harder to optimize for numbers that can go down: Weekly active users Returning customers Feature adoption Customer satisfaction Retention Those metrics are uncomfortable because they tell the truth. So now, whenever I review a week of work, I ask one question before looking at anything else: If I hid every metric

... Sorry, I'm a PH launch rookie. Launched yesterday but my metrics and dashboard have not updated since then, and I know I'm getting upvotes and comments, per notifications ...

... comparing, the "efficient" tool flips. Zero-shot capability and token frugality don't always come from the same tool. On raw benchmark performance, Claude Opus 4.6 sits around 80.8% and GPT-5.5 powering Codex around 88.7% on the metrics one source tracked, but Claude Code holds an 88.6% SWE-bench Verified score by another measure. The benchmarks genuinely don't agree with each other depending on methodology, which is its own lesson. Price-per-output tells a different story than ... ... want to know whether the cheap run needed follow ups, because thats exactly where the ranking flips. did any of the tests you found report retries at all? Comment from Stephanie Tang(@stephanie_t0317): Token efficiency is an interesting metric

... didn't need to open them again for a while. That "not needing it" was the sign it worked, not the sign it failed. That's the part I think we get backwards as builders. When your success metric is "how dependent is the user," your incentives slowly rotate away from solving their problem. A tool that genuinely cleared your plate would see you open it less. But a product measured by daily engagement learns the opposite lesson. Keep ... ... loop open, manufacture a reason to come back, leave a little something unfinished. Not because anyone's evil. Because that's what the metric

... repetitive cases, but keep high-intent users and weird edge cases human. The goal isn t zero automation, it s earning the right to automate something we actually understand. Comment from Hossein Yazdi(@hosseinyazdi): I like this approach. Metrics are much more useful when they're tied touser success instead of just activity. Tracking whether someone actually reached a meaningful outcome gives you clearer signals on what to improve than watching signups or pageviews alone. Comment from Ivan Semenov(@ivan ... ... actually send. Comment from Dawid Baranowski(@dawid_baranowski): @omri_ben_shoham1  manual work for the win! Comment from Dawid Baranowski(@dawid_baranowski): @satchmo_saunders  Appreciate you man! For us, we're still deciding on the north star metric

... what the actual job entails. Developers have noticed. Only 12% of developers use LinkedIn as their main source of technical information, according to a Stack Overflow survey. Most are on the platform purely because they feel they have no alternative, not because they find it genuinely useful. The Fake Everything Problem LinkedIn has a trust problem that is getting dramatically worse, not better. In the second half of 2024, LinkedIn identified and removed 80.6 million fake accounts at the point ... ... solve count. No way to surface your dev.to articles or Medium posts as actual content rather than buried links. No integration with your YouTube channel showing your technical talks. No live GitHub contribution graph. No product showcase with real metrics

... 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

... Cursor: coding Supabase: backend and auth Vercel: hosting GitHub: version control Google Search Console: SEO Campix: my own project, camp software, yes I'm biased Comment from Hans Desjarlais(@ismaelyws): Gmail Calendly Claude Code Google Docs & Sheets VS Code Comment from Bill Davis(@billspaced): @nolan_vu  Slack, not because I love it but because everything dies without it. YESSSSSSSSSSSSS Comment from Bill Davis(@billspaced): @riya_pariyar   @benfleming   @asapx  same question... never heard ... ... solo founder, these are the five tools I use almost every day: ChatGPT VS

... 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

... 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

... 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

... 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

... 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