I made a small platform to collect feedback

Hacker News

Hello everyone! My co-founder and I are developing a mid-sized travel platform. (By the way, we met on cofounder-matching, an excellent platform) In the process of development, we decided to create a feature to collect customer reviews on the site. It's simple enough: You put a rating in stars and write a review. It seemed incredible to me to launch this project separately from the main project because, in my experience of working in companies, it was often done again. I started this project as a test run for Indie Making. Right now, the functionality is pretty modest (as well as the cost); you put in a script, collect data, and analyze it. If you have any ideas on how to improve the project, what to pay attention to, or what functionality is sorely lacking, I will be glad to listen and discuss them. Thank you!

Visit product View on Hacker NewsSep 23, 2024

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3points

4comments

Did not reach leaderboard

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Product HuntOn track for Day 1 leaderboard · Missing: mac, agents, macos

74%74% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

73%73% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

49%49% predicted probability of success on AppSumo, 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

47%47% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

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

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

20%20% 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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