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LeadCraft – Hyper targeted prospecting with 360° lead summaries

Hacker News

LeadCraft – Hyper targeted prospecting with 360° lead summaries

Hey HN, We’re thrilled to introduce our new prospecting tool LeadCraft to y’all! Have you found researching prospects to be tedious? LeadCraft streamlines prospect research, without having to manually sift through multiple sources across the web like LinkedIn, Twitter, etc. Here’s how it works: 1.Connect your CRM or import your leads directly to LeadCraft. It then enriches and provides tailored snippets (e.g., press mentions, technology interests, thought leadership contributions, etc.…) about all your leads in one place. 2.Enter the problem you are solving, your detailed solution, and Ideal Customer Profile (ICP) into LeadCraft. 3.Zoom in on individual prospects and get lead summaries for discovery calls, assistance in crafting unique emails, and LinkedIn messages. Sign up for early access and get discounted pricing. We’re eager to learn how LeadCraft fits into your sales outreach process!

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Actual performance

3points
Did not reach leaderboard

Launch Intel predictions

Analyze your own launch →
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: new, email · Missing: mac, agents, macos
65%65% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Strong signals: calls · Missing: plus, platform, intuitive
54%54% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
41%41% 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
31%31% 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
15%15% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: introduce · Missing: web3, chat, crypto
2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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