Textio

7 statements across 2 episodes · 4 bullish · 1 bearish · 2 people on the record · first statement Jan 2, 2019 by Kieran Snyder · said 10 times in 4 episodes since 2017 · across every show →

Mentions by year

brought up most by Sonal Chokshi (6), Frank Chen (3), Kieran Snyder (1)

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001.51.533201720182019episodesmentions per episode
2019 8 mentions in 3 episodes 3 per episode
2017 2 mentions in 1 episode

every mention, scene by scene, with the transcript →

Everything said about Textio, oldest first

Jan 2, 2019
Insight
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.”
Kieran Snyder Jan 2, 2019 ▶ 31:27 a16z Podcast | It's Not What You Say, It's How You Say It -- When Language Meets Big Data
Jan 2, 2019 negative
Assertion Not checkable as stated
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…”
Kieran Snyder Jan 2, 2019 ▶ 11:04 a16z Podcast | It's Not What You Say, It's How You Say It -- When Language Meets Big Data
Jan 2, 2019 positive
Assertion Not checkable as stated
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.”
Kieran Snyder Jan 2, 2019 ▶ 2:59 a16z Podcast | It's Not What You Say, It's How You Say It -- When Language Meets Big Data
Jan 2, 2019 bullish
Assertion Not checkable as stated
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.”
Kieran Snyder Jan 2, 2019 ▶ 7:43 a16z Podcast | It's Not What You Say, It's How You Say It -- When Language Meets Big Data
Jan 2, 2019 positive
Insight
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…”
Jensen Harris Jan 2, 2019 ▶ 16:34 a16z Podcast | The Product Edge in Machine Learning Startups
Jan 2, 2019 neutral
Disclosure
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 Jan 2, 2019 ▶ 14:04 a16z Podcast | The Product Edge in Machine Learning Startups
Jan 2, 2019 positive
Insight
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 Jan 2, 2019 ▶ 10:01 a16z Podcast | The Product Edge in Machine Learning Startups
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