/Michael Wilson

Accelerating Our A/B Experiments With Machine Learning tl;dr: "Dropbox runs experiments that compare two product versions — A and B — against each other to understand what works best for our users. When a company generates revenue from selling advertisements, analyzing these A/B experiments can be done promptly; did a user click on an ad or not? However, at Dropbox we sell subscriptions, which makes analysis more complex. What is the best way to analyze A/B experiments when a user’s experience over several months can affect their decision to subscribe?"  

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