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jlaurito

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en.wikipedia.org 2y ago

Ruritania

jlaurito
2pts0
en.wikipedia.org 2y ago

Alan Smithee

jlaurito
2pts0
en.wikipedia.org 3y ago

The Waffle House Index

jlaurito
52pts13
engineering.squarespace.com 3y ago

Fluid Engine

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www.state.gov 3y ago

The U.S. Order of Precedence [pdf]

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engineering.squarespace.com 3y ago

A Better Way to Upload Images

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2pts0
en.wikipedia.org 4y ago

Sexy Son Hypothesis

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3pts0
engineering.squarespace.com 4y ago

WebGL Usage Patterns at Squarespace

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en.wikipedia.org 4y ago

Maunder Minimum

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blog.joshlaurito.com 4y ago

My 10 Favorite Posts on Data (and Management) of 2021

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engineering.squarespace.com 4y ago

A Blueprint for an Engineering Manager Forum

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blog.joshlaurito.com 4y ago

Internal Transfers: The Best Way to Build Your Team and Alienate People

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engineering.squarespace.com 4y ago

Engineering Director on management, engineering culture, promotion and burnout

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engineering.squarespace.com 4y ago

How do you make websites look good when you can’t know how they will look?

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engineering.squarespace.com 4y ago

Squarespace's Block Editor

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blog.joshlaurito.com 4y ago

Gresham’s Law of Time Management

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10pts1
engineering.squarespace.com 5y ago

Building a testing culture in mobile app development

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blog.joshlaurito.com 5y ago

5 Years of Newslettering

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medium.com 9y ago

A Technical Primer on Causality

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subwayanalyst.com 10y ago

Hudson Yards NYC – the first week of data

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www.nytimes.com 11y ago

LED Inventor and Employer Settle for $8.1M (2005)

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34pts15
bl.ocks.org 11y ago

Mouse Speedometer

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www.smartasset.com 12y ago

The Impact of the World Cup on Economic Productivity

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jlaurito.github.io 12y ago

CUNY Data Visualization Final Projects

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en.wikipedia.org 12y ago

Parkinson's law of triviality

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blog.joshlaurito.com 12y ago

By 2030 no TV show will get a 10 share (2013)

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jlaurito.github.io 12y ago

Mapping the US Banking System with D3.js

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medium.com 12y ago

Your App is Cheaper than a Cup of Coffee?

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mcmontero.com 12y ago

A Different Paradigm for CTO's

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blog.joshlaurito.com 12y ago

A Startup’s Minimum Revenue Per Employee

jlaurito
41pts21

ghein- I think a comparison of margin/profitability metrics would be interesting as well. If you find any data on that, please post.

This analysis is geared less at understanding profitability/valuation metrics and more at operational and modeling decisions around growth, like headcount needed to support revenues in high growth companies.

Yeah, absolutely- it's even more likely that outsourcing happens more in one industry than another, so the average numbers are skewed as a result. I would love to see data on that- will post more if I find any.

Hey bcbrown- I took the averages from the raw data: you can find them already scraped at https://github.com/jlaurito/inc5000 (inc5000data_cleaned.csv has only these industries).

You are right- the range is wider than I mentioned, and the true minimum is lower (the numbers in the post are industry-by-industry averages).

I used log-log graphs because they reduce the visual impact of outliers. You can play with the graphs yourself at http://blog.joshlaurito.com/inc5000.html if you want to see alternatives.

There are definitely biases in the sample: these are only fast-growing, 3yr+ old companies that want publicity badly enough to open their books to Inc.

If you are working in a company in a company with a similar profile or compete with any of the companies here, I think the data is useful for deciding how quickly to hire and benchmarking against any competitors that might be in the sample. Also, if you're writing a business/evaluating a business plan this might be useful data. For the rest of us, it's just fun to play with!

Well, I agree in theory, but I only chose industries where the major expense is people. So if revenue/employee is lower, either pay/employee is lower or margins are.

I suppose a third possibility is that software compensation may have a larger equity component, which would allow software employees to take similar overall comp at lower revenue/employee levels.

cool post- my experience with conversion rates in startups is that tracking the changing nature of your audience is really important: early adopters are really fundamentally different from people who want others to test the water. you can kind of see a separation in the vintage chart.