Reviewcheck – automatically generated customer feedback reports

Hacker News

Hi HN, Over the past 4 months I built Reviewcheck on the side. Reviewcheck collects reviews for a business from different platforms such as Google, Trustpilot and Facebook and automatically generates a report with actionable insights to improve. I actually had the idea when I was in a restaurant that received quite some reviews from customers, but hadn’t changed anything in the last 10 years. I think (and hope haha) there are a lot of similar businesses that collect online reviews but aren’t really leveraging them because they do not have the time or do not want to pay a monthly subscription. An example report can be found at https://www.reviewcheck.co/example-report . Since the possibilities for this report are endless, I am curious to hear what you are missing. General feedback or questions on how it was built are also very welcome of course. Thanks a lot for taking the time to check it out!

Visit product View on Hacker NewsMay 13, 2024

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3points

Did not reach leaderboard

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

70%70% 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 · Missing: mac, agents, macos

58%58% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released

52%52% 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: platform, reviews · Missing: plus, intuitive, host

46%46% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

29%29% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

25%25% 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.

Incorrect prediction on native model

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