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Analytics
Experimentation & A/B Testing
Good decisions start with good evidence. This page brings together articles on experimentation, from designing A/B tests and controlled experiments to analyzing results and turning them into confident decisions. Whether running the first test or refining a mature experimentation program, you will find these articles practical guides, statistical concepts, common pitfalls, and real-world examples here.
Synthetic Control
A series on measuring the impact of a campaign or launch when you cannot run a classic A/B test building a synthetic version of the test city from similar cities.
Part 1Synthetic Control: The Core Idea with a Easy to understand ExamplePart 1 of 3: what Synthetic Control is, why it beats before-vs-after and single control-city tests, with a hand-checkable free-delivery example.Part 2Synthetic Control: Result Validation with RMSPE and PlacebosPart 2 of 3: validate a Synthetic Control result with RMSPE, the RMSPE ratio, placebo tests, plus the Python code that fits the weights.Part 3Synthetic Control: Pitfalls, Limitations, & Metrics to Know Before the TestPart 3 of 3: how to choose the test city, donor pool, time grain and metric for Synthetic Control, with industry examples, limitations and a leadership summary.