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I built a simple user survey tool for SaaS

Hacker News

I built a simple user survey tool for SaaS

Hi all, I spent the last little while on building Survser, which is a simple and affordable in-app survey solution for SaaS apps. I started working on Survser because I found that most of the solutions on the market are too expensive for smaller projects. Some feature highlights: - Fully customizable survey appearance to match your brand - Flexible survey triggers let you ask the right question at the right time to collect contextual feedback - A growing selection of ready made survey templates to quickly get you started with best practices of user research Hope you like it as much as I liked building it! If there is anything you would like to know, I'm here to answer.

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Did not reach leaderboard

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Indie HackersFits the IH revenue-focused audience · Strong signals: started · Missing: supports, reddit linkedin, podcasting
68%68% 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.
TrustMRRFits verified-revenue profile · Strong signals: apps · Missing: mobile apps, ios, personal
53%53% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Product HuntOn track for Day 1 leaderboard · Strong signals: apps, user, context · Missing: mac, agents, macos
52%52% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
48%48% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released
46%46% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Acquire.comPre-revenue stage for this audience · Strong signals: saas · Missing: arr, mrr, revenue
15%15% 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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