This episode carries John Foreman's own address, with nobody on the show putting
questions to them. It still counts as said, and it is kept out of every score on their page.
John Foreman, Chief Data Scientist at Mailchimp, explains the operational inefficiencies of Mailchimp's manual compliance system before implementing anti-spam machine learning.
“A sixth of our compliance tickets had nothing to do with being bad. We just wanted to vet you.”
quote is from the automated transcript, cleaned for reading:
filler sounds and stutters are removed, nothing is rephrased. names can be misheard
(the analysis reads context, assessments check outside sources). how →
More from John Foreman
Insight
Data science deserves no special treatment over simple UI design changes
“Data science products should receive no special treatment. If a designer Can do a better job solving a problem by changing the color of a form than I can do with some AI model or something, then that design product should win.”
John ForemanDec 5, 2013▶ 22:11John Foreman, Mailchimp // Data Driven NYC 19 // October 2013
Opinion
Most tech data features are built to impress investors, not help users
“When you see examples from other companies out in the wild, generally they're to improve, they're sort of to impress investors or the media.”
John ForemanDec 5, 2013▶ 3:55John Foreman, Mailchimp // Data Driven NYC 19 // October 2013
AssertionNot checkable as stated
Gmail's tabbed inbox launch dropped Mailchimp open rates by 10 percent
“This is three weeks before and after tabs were introduced, and we can see about a raw one percent difference, so maybe about a 10% decrease in engagement. We've got three weeks. Blue is weekday. Yellow is, ah, weekend, and these are open rates. So you can see …”
John ForemanDec 5, 2013▶ 9:53John Foreman, Mailchimp // Data Driven NYC 19 // October 2013
Disclosure
Mailchimp's data science team spends 80 percent of time building tools
“And right now we spend about 20% of our time doing insight, which is just one-off reporting or one-off sort of consulting engagements, and we spend about 80% of our time building tools or capabilities”
John ForemanDec 5, 2013▶ 5:20John Foreman, Mailchimp // Data Driven NYC 19 // October 2013
Insight
Data scientists should consider changing the business itself when models fail
“If a model's not working out, you should always ask yourself, can we just change the business? You know, as opposed to somehow optimally solving this problem, can we just stop doing this thing I'm trying to solve?”
John ForemanDec 5, 2013▶ 8:03John Foreman, Mailchimp // Data Driven NYC 19 // October 2013
Disclosure
Mailchimp infers recipient age and gender via cross-network subscription graphs
“We can actually determine a lot of demographic data through this, ah, age, gender, things like that.”
John ForemanDec 5, 2013▶ 18:11John Foreman, Mailchimp // Data Driven NYC 19 // October 2013
Made with StarZero
Turn any episode into a week of clips.
This entire site, over 400 conversations transcribed, diarized, checked and made playable,
runs on the StarZero media pipeline. Drop in your own episode and the podcast clipper finds the
moments worth sharing, cuts them, captions them, and reframes them for every feed.
We use essential cookies to make the site work. With your permission we
also use analytics cookies (Google Analytics and Mixpanel) to understand
usage and improve StarZero. See our Cookie Policy.