Scoop – automated carpooling

Hacker News

Our team (all ex-commuters ourselves) at Scoop is really excited to announce Scoop for commuters who head into San Francisco from across the Bay Area every day. Scoop’s mission is to end traffic congestion nationwide by turning single-occupant vehicles into efficient carpools. 80% of Americans drive alone to work everyday, which means 100M people are clogging up highways across long stretches of the country - and for most, public transportation or other alternatives aren't viable. Our automated carpool solution enables commuters to schedule and share their commutes, saving time, money, and the environment. We specialize in longer distance (10+ miles) commutes, and we're finding a sweet spot with Bay Area employees (by partnering with their employers) who commute on some of the country’s worst routes.

Visit product View on Hacker NewsNov 4, 2015

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Actual performance

6points

3comments

Did not reach leaderboard

Launch Intel predictions

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

82%82% 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.

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

77%77% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRFits verified-revenue profile · Strong signals: way · Missing: mobile apps, ios, personal

51%51% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Strong signals: single · Missing: mac, agents, macos

50%50% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

31%31% 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

17%17% 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

3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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