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PRODUCT, GROWTH & DATA

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How to not run out of runway - the seed stage growth model

In a seed round, you need to both find PMF *and* grow to a few thousand users. This model helps you plan that growth.

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New! - Limited time - Free Office Hours with me

I'm giving out free half-hour consultations about data, growth and experimentation or product issues. Yes, free.

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Check these 4 things before you share your experiment's results with your team

Experimentation is easy in theory and hard in practice. Many effects can cause us to draw incorrect conclusions. Some...

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What I learned about fundraising during a pandemic

I raised the seed round for Radical in the midst of COVID-19. Here's a few things I learned about making the best of it.

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'Additive' and 'Substractive' measures, or how to goal for stuff that shouldn't happen

Most of the stuff we measure should go up and to the right. But what about stuff where the goal is 'this never happens'?

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Accuracy of small-sample interpolated empirical distribution functions: a simulation

When you have a few data points, and no model of the distribution they are drawn from, you can still use what you have.

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Understand, Identify, Execute

Understand, Identify, Execute is at the heart of Facebook's product management framework and key to its success.

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Why your experiment's impact is probably greater than you think

A mental model of blockers can help understand why we need a regime of experiments rather than just one to fix an issue

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The false dichotomy of Quant-Qual analysis and the real tradeoff of data-driven product development

There's no tradeoff between qualitative and quantitative analysis. Do both. The real tradeoff is something else.

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The measured life of 1944

I randomly discovered a history of interest in data and visualization running through my ancestry, dating back to 1944

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Organizing research into seven output types

There's a range of options for "deliverables" from applied researchers in the industry. Understand their tradeoffs.

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