Liz Crawford

CPTO, Flare · 1 appearance on the record.

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executiveengineerfounderoperator@liscrawford ↗LinkedIn ↗flarehr.com ↗

Liz Crawford is the Chief Product and Technology Officer at Flare. She previously served as the Chief Technology Officer at Birchbox, where she scaled engineering, data science, and personalization systems.

10statements → 4claims → 0claims resolved → 4.3/5average certainty → 1.8/5average debate potential →

4 not checkable as stated how the 4 claims stand · each chip opens the sources

4 assertions · 3 insights · 3 disclosures · every statement was checked. The predictions and assertions are the 4 claims: statements the public record can support or contradict. 0 are resolved, and 4 name no date, number or outcome precise enough to check. Everything else (opinions, insights, what ifs, disclosures) can never be settled by the record, so it carries no assessment.

The record, in short

What the tape says about how Liz argues and how the claims held up. Everything they said, and everything said about them, is in the tabs below.

How they sound: speaking style how? →

221 words/min while actually speaking · 18.5 um and uh per 1k words

No argument clarity score for Liz Crawford: no usable question→answer exchanges on raw tape (a fair score needs 8+). We do not score a sample that small. Roundtable and news formats yield far fewer direct exchanges than interviews.

Measured by listening to the audio itself: 3,829 words across 1 episode of raw-level tape, transcribed verbatim with every um and uh kept, each one attributed only where the alignment onto our timed stream is unambiguous. These are measurements of speaking style. We do not rank them: across this corpus, fluency and argument quality are nearly uncorrelated (ρ≈0.2), and smooth talking does not signal clear thinking. How it's measured →

What they said10 Episodes1

Everything Liz Crawford said on the MAD Podcast that made the record, most notable first. Filter by type, assessment or year in the ledger →

Insight

Centralized functional data teams prevent duplicate work and foster peer learning

“To organize functionally, but what we've found is that people can learn from each other really well that the data scientists and the statistical analysts can learn from each other and that that's great, and that it really helps when people are working together…”

Liz Crawford Oct 21, 2015 ▶ 1:45 Liz Crawford, Birchbox // Data Science & Analytics at Birchbox (Hosted by FirstMark Capital)

Disclosure

Birchbox defines data scientists as PhD-level coders on product teams

“So we see a data scientist as someone who contributes to product development, essentially. Someone with very specialized skills, Who helps us deliver our products into market. And so by specialized skills, I essentially mean a PhD or something very much like i…”

Liz Crawford Oct 21, 2015 ▶ 2:26 Liz Crawford, Birchbox // Data Science & Analytics at Birchbox (Hosted by FirstMark Capital)

Insight

Non-scrappy data scientists do not belong in early-stage startups

“I wouldn't hire someone that couldn't go out and be scrappy and get their hands dirty. That person doesn't belong in an early stage startup anyway.”

Liz Crawford Oct 21, 2015 ▶ 15:42 Liz Crawford, Birchbox // Data Science & Analytics at Birchbox (Hosted by FirstMark Capital)

Insight

Data science teams must be empowered to gather new data

“Don't be limited by the data that you have today, and if you run a data science team, don't let them be limited by it. Empower people to go out and get the data that they need to do their jobs.”

Liz Crawford Oct 21, 2015 ▶ 14:01 Liz Crawford, Birchbox // Data Science & Analytics at Birchbox (Hosted by FirstMark Capital)

Assertion Not checkable as stated

Birchbox's catalog recommendation algorithm boosted full-size product conversions by 20%

“We were using one of our recommendation algorithms just recently in a catalog sort which we newly put in, and it showed a 20% lift in conversion on, like, full size purchase.”

Liz Crawford Oct 21, 2015 ▶ 20:22 Liz Crawford, Birchbox // Data Science & Analytics at Birchbox (Hosted by FirstMark Capital)

Assertion Not checkable as stated

Birchbox trained nearly its entire company workforce to write SQL

“Trained almost the entire company to use SQL.”

Liz Crawford Oct 21, 2015 ▶ 24:22 Liz Crawford, Birchbox // Data Science & Analytics at Birchbox (Hosted by FirstMark Capital)

Assertion Not checkable as stated

Sample selection is Birchbox's highest monthly customer engagement moment

“So every month our customers are able to choose one of their samples. This is our highest engagement moment every month.”

Liz Crawford Oct 21, 2015 ▶ 7:47 Liz Crawford, Birchbox // Data Science & Analytics at Birchbox (Hosted by FirstMark Capital)

Assertion Not checkable as stated

Birchbox saw huge conversion gains by shifting to triggered messaging

“And moving more and more and more and more of the messages we send our customers over push, over email to triggered, we've seen huge increases in the conversion of those messages.”

Liz Crawford Oct 21, 2015 ▶ 11:52 Liz Crawford, Birchbox // Data Science & Analytics at Birchbox (Hosted by FirstMark Capital)

Disclosure

Birchbox never sends subscribers the same sample product twice

“We never send you the same thing twice, for example.”

Liz Crawford Oct 21, 2015 ▶ 6:07 Liz Crawford, Birchbox // Data Science & Analytics at Birchbox (Hosted by FirstMark Capital)

Disclosure

Birchbox uses the Gurobi solver for monthly subscription box personalization

“And we do this with the help of a very nice solver called Garobi.”

Liz Crawford Oct 21, 2015 ▶ 7:04 Liz Crawford, Birchbox // Data Science & Analytics at Birchbox (Hosted by FirstMark Capital)

Appearances (1)

Episode	Date	Speaking time
Liz Crawford, Birchbox // Data Science & Analytics at Birchbox (Hosted by FirstMark Capita	Oct 21, 2015	19m

Liz Crawford

The record, in short

How they sound: speaking style how? →

Appearances (1)

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