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See Who's Visiting Your Website: Videos, LinkedIns and AI Insights

Hacker News

See Who's Visiting Your Website: Videos, LinkedIns and AI Insights

Hey everyone, I'm working on a tool called Zipy that combines user session recording and error monitoring. We've found it incredibly helpful for tracking website visitors. We also discovered that pairing Zipy with RB2B ( https://www.rb2b.com/ ), a service that identifies website visitors' LinkedIn profiles, provides some really valuable insights into our audience. We think this integration could be beneficial for other startups as well, so we're happy to share it with you for free! We'd love for you to try it out and let us know if you find it useful and if it would be worthwhile for us to continue developing it. Here's a quick look at the benefits of integrating Zipy and RB2B: 1.Session Recordings: See exactly how visitors interact with your website. 2. LinkedIn Profiles: Identify your US-based website visitors on LinkedIn (for applicable visitors). 3. AI-Powered Insights: Get an AI-generated summary of each session, providing insights into visitor intent. Look forward to hearing your thoughts so I can improve the insights further.

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
86%86% 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: user, recordings · Missing: mac, agents, macos
81%81% 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
44%44% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: video · Missing: mobile apps, ios, personal
35%35% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: lua, ide, io · Missing: https docs, excited, just released
33%33% 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 · Missing: arr, mrr, revenue
11%11% 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
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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