Simple and privacy-friendly analytics for websites and SaaS

Hacker News

Hey everyone, I (Waqar) along with my two other co-founders (Amad and Azhar), have built a no-code analytics tool that provides simple website analytics and SaaS product usage insights in a single place. Before building Usermaven, we tried Mixpanel, Google Anlaytics and a couple other solutions for my SaaS businesses ( https://contentstudio.io and https://replug.io ) but unfortunately, all of the analytics tools fell short in one way or another. They were overly complex, hard to configure, and sometimes inaccurate. Every time we made a change to our website or product, we had to ask developers to update tracking setup. This was time-consuming and often got put on the back burner, so we never got the meaningful insights we were looking for. And, if we forgot to track something, there was no retroactive data available. That why we built Usermaven with events autotracking, cookieless mode, pixel whitelabeling (to bypass adblockers) and pre-built reports. We have a free plan upto 1 million events per month and it includes both the web and product analytics. Sign-up for free on our website ( https://usermaven.com ) and let us know what you think. Thanks for reading - would love to hear your feedback!

Visit product View on Hacker NewsJan 27, 2023

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Actual performance

3points

Did not reach leaderboard

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

87%87% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Product HuntOn track for Day 1 leaderboard · Strong signals: google, user, single · Missing: mac, agents, macos

69%69% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: month, google, way · Missing: mobile apps, ios, personal

44%44% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released

42%42% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoMay struggle as an AppSumo deal · Strong signals: friendly · Missing: plus, platform, intuitive

30%30% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: saas, active · Missing: arr, mrr, revenue

12%12% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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