Onboard new hires like a boss

Hacker News

Inspired by HN posts like this https://news.ycombinator.com/item?id=10890032 we set out to make an onboarding platform that helps teams design and implement effective onboarding workflows. I'm reaching out to the HN community for feedback on our private beta. I'd really like to connect with those that have struggled with onboarding in the past to see if our tool can help. If you're interested in trying the product, tweet at me on twitter @raykanani :) Product link http://www.qrtrmstr.com/

Visit product View on Hacker NewsJun 16, 2016

Share card

Actual performance

5points

Did not reach leaderboard

Launch Intel predictions

Analyze your own launch →

Hacker NewsStrong engagement from HN community · Missing: https docs, excited, just released

73%73% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Product HuntOn track for Day 1 leaderboard · Strong signals: new · Missing: mac, agents, macos

68%68% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

48%48% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

40%40% 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

39%39% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

16%16% 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

Similar products

New 6kb design system called M- is like HTML648%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

New 6kb design system called M- is like HTML6

Hacker News12

New Earbits design is more app-like43%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

New Earbits design is more app-like

Hacker News7

ScriptCat – Like Pingdom for ports56%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

ScriptCat – Like Pingdom for ports

Hacker News2

Jvm, like rvm, but for Java46%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Jvm, like rvm, but for Java

Hacker News2

Kinda like posterous?43%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Kinda like posterous?

Hacker News53

"I'm Like..." - Texting With More Feeling47%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

"I'm Like..." - Texting With More Feeling

Hacker News1

Succinctly manipulate dom-like trees from java50%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Succinctly manipulate dom-like trees from java

Hacker News2

Spritz like bookmarklet37%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Spritz like bookmarklet

Hacker News8

Endorfyn: Everything You Like59%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Endorfyn: Everything You Like

Hacker News14

More Like This Bookmarklet33%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.