... monthly active user metric can look completely stable while underlying product usage slowly drifts into a passive state. Realizing that logins do not equal active value creation made us completely rethink how we track long term retention. For founders looking past the initial growth phase: what retention metrics

... measured community growth only by counting member signups for the first six months. The numbers seemed good on paper. The discussion boards were quiet. We changed our focus to a metric: the ratio of member-created posts to staff-created posts. We also watched how many questions got an answer from another community member within two hours. The quality of community interactions improved a lot once we stopped chasing vanity registration numbers. Engagement became self-sustaining because community members actually ... ... helped each other. Measure community health by peer-, to-peer interactions of raw headcounts that hide low activity levels. What specific metric

... ChatGPT, Perplexity, or Gemini recommends you when someone asks for the best tools in your category. Build a list of 30 50 prompts your potential customers might actually use. For example: What's the best [category] tool for startups? [Competitor A] vs

... evaluating the metrics on some product recently, and I realized we had a dashboard flooded with different activities: pageviews, clicks, sign-ups, and feature use. However, there was one thing we didn t consider: Did people actually achieve the goal the product was created to help them to achieve? We were counting how people were using the product, but we weren t tracking if their actions led to their desired results. This made me reconsider what kind of metrics

... early days, upvotes were the most important. (They are still in demand now), but people increasingly want comments. Because PH no longer counts only upvotes in the final ranking, but also other things: upvoting, commenting, sharing, etc. Which metric is the most important for you? [As for me, I personally prefer comments over upvotes, where people want to find out more about the product. Writing a comment without AI requires more effort than upvotes, which means that people have ... ... would it still make sense pasted under somebody else's comment? If it would, it is too generic and needs the detail they actually raised. Comment from Shivam Kushwaha(@shivam_kushwaha16): for me comments are the most valuable metric

... been using my website for a while, although not too long, and I understand that SEO takes a long time to make a noticeable difference, but I'm hopeful. I would like to know what the most fundamental metrics are to get an idea of whether I'm on the right track or what I need to improve. If you have your consolidated website , your opinion or if you know about the subject would be very useful. Another point that ...

... 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

... 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 ...

... switching tabs. This changed our whole product philosophy. We realized that solving UX friction and context retention creates far higher user satisfaction than just upgrading to a newer LLM. As builders and product managers: What is one non-obvious metric or user behavior that completely changed how you design your product? How do you balance adding flashy new features versus

... Almost nobody saw them. Views told me the hook worked. Depth told me everything after it didn't. I'd have made exactly the same mistake again if I'd only looked at the first number. What's the metric you nearly missed that changed how you build ...

... 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 ... ... actually cheap. Curious what people here are actually measuring, and whether you've settled on one agent or are still task-switching between them depending on the job. Comment from Stephanie Tang(@stephanie_t0317): Token efficiency is an interesting metric

... accept a strong match, find the right person, and create outreach they would actually send. Comment from Dawid Baranowski(@dawid_baranowski): @satchmo_saunders  highly recommend Matt Lerner's book on growth - delves quite a bit into north star metrics that correspond to aha / activation moments :) Comment from Satchmo Saunders(@satchmo_saunders): The "better guidance inside the product" direction resonates with me. I've been moving the same way this month with my own app. My version of your insight ... ... starts making sense when product paths are SUPER clear and there isn't that much room for improvement based on user feedback in those particular parts of the product imo :) Comment from Hossein Yazdi(@hosseinyazdi): I like this approach. Metrics

... 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

... Claude Sonnet pricing: Input: $3 per million tokens Output: $15 per million tokens (unchanged by compression) Cached input: $0.30 per million tokens (90% discount) My savings come from two sources: Fewer input tokens (compression) More cache hits (prefix stabilization) Metric Before After Daily input tokens 990K 268K Cache hit rate 12% 48% Effective input cost/day $2.90 $0.62 Output cost/day (unchanged) $7.50 $7.50 Daily total $10.40 $8.12 Wait, that is only $68/month savings on raw math. Where does the $200 come ... ... file reads, fewer redundant tool calls, fewer wasted output tokens on re-doing work. Does Quality Actually Stay the Same? I was skeptical too. Here is what I measured over 4 weeks: Code that compiles first try: 78% (before) vs

... 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

... both content and   product work.   2. Google Search Console I basically live in it. For anything SEO, it's the only honest source of truth   about what's actually happening vs. what I assume is happening.   3. Ahrefs keyword and competitor

... agent execution to agent management. The tools that win may not be the ones building better agents, but the ones helping humans supervise dozens of them effectively. Comment from Alexander Bering(@alexanderbering): @aadilghani  The reason "edit vs. tell-it-to-revise 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