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I made a free scorecard to diagnose my B2B sales pitch

Hacker News

I made a free scorecard to diagnose my B2B sales pitch

Hi HN. I'm a founder working on a sales tool. I found my own pitch was a major weak point, so I created a 10-point framework to grade it objectively based on best practices (value prop, social proof, etc.). I'm trying to validate if this is a problem other founders face. The scorecard is just a simple page with the checklist. Any feedback on the framework itself would be really appreciated.

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Actual performance

1points
Did not reach leaderboard

Launch Intel predictions

Analyze your own launch →
Indie HackersFits the IH revenue-focused audience · Strong signals: created · Missing: supports, reddit linkedin, podcasting
56%56% 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 HuntUnlikely to reach the leaderboard · Missing: mac, agents, macos
48%48% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Missing: https docs, excited, just released
41%41% 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 · Missing: plus, platform, intuitive
27%27% predicted probability of success on AppSumo, based on ML models trained on real launch data.
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 · 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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