Textio
company on 3 shows · 9 statements across 3 episodes · said 35 times in 8 episodes since 2016
the MAD Podcast 19 
the a16z Podcast 10 
20VC 6
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the MAD Podcast 19the a16z Podcast 1020VC 6
2019 9 mentions in 4 episodes 2 per episode
- a16z Podcast | It's Not What You Say, It's How You Say It -- When Language Meets Big Data
- a16z Podcast | The Product Edge in Machine Learning Startups
- 20VC Exclusive: Roy Bahat on Bloomberg Beta's New Fund, The Truth About Valuation That Very Few VCs Will Tell You & Why Founders of Venture Backed Startups Make The Best Angels
- Better Together: Humanity + Machine Learning
- every mention in 2019, scene by scene →
2017 2 mentions in 1 episode
2016 24 mentions in 3 episodes 8 per episode
- Using AI to Predict the Performance of Text // Kieran Snyder, Textio (Data Driven NYC / FirstMark)
- 20VC: Why Machine Intelligence Will Eat The World Of Software with Roy Bahat, Head of Bloomberg Beta
- 20VC: Greenfield Opportunities For Machine Learning, Why Massive Corporates Finally See It's Potential & Why VC's Investment Decision Making Process Needs To Change with James Cham, Partner @ Bloomberg Beta
- every mention in 2016, scene by scene →
every mention on every show, scene by scene, with the transcript →
9 statements about Textio, every show
Jensen Harris: Early ML startups need thousands, not billions, of data points
“We found in our sort of earliest days, like our first six months that we didn't have to, you know, really ingest tens of billions of things then. What we really needed was, you know, tens of thousands or hundreds of thousands of really good pieces of data.”
Jensen Harris: Textio uses off-the-shelf Python ML libraries, focusing on data pipelines
“Our actual, you know, machine learning algorithms and the core NLP stuff we do is the standard sort of Python libraries that You know, you can go download and use, but we have put an enormous amount of time into our data processing pipeline.”
Jensen Harris: Early ML startups should use cloud providers, not custom infrastructure
“Yeah, you don't need to custom build something, and you shouldn't spend any of your time working on that. Like, you should figure out what cloud platform you're using, whether it's AWS, or whether it's Azure, or something else. They all have built-in ML servic…”
Snyder: Textio predicted Kickstarter fundraising success with over 90% accuracy
“We got over 90% predictive on minute zero of a project as to whether it was going to hit its fundraising goal based solely on things like how long is the text and what kind of fonts are you using and how many headings do you have.”
Snyder: Textio identified 25,000 job description phrases affecting recruiting outcomes
“In jobs, it matters hugely. You know, we, we've identified at this point over 25,000 unique phrases that move the needle on how many people will apply for your job, what demographics, how qualified they are.”
Snyder: The word "synergy" torpedoes job listing candidate response rates
“The biggest, you know, the, one of the very common, we call it a gateway term that kind of torpedoes your listing is the word synergy. But it's a gateway term because when people include synergy, they're also significantly more likely to include, you know, val…”
Snyder: Overused language optimization patterns lose effectiveness, forcing marketing innovation
“If everybody tries to glom onto the same patterns, they're no longer effective. Someone is gonna figure out, as with any marketer, someone is gonna figure out how to do it better, and they're gonna introduce the next Pattern for success.”
Textio collected 15 million job listings tagged with performance outcome metrics
“At this point we have about fifteen million job listings across industries and geographies that are tagged with, in many cases, very rich outcomes.”