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There is broad buy-in across the company, including from the C-Suite, that, whenever possible, results from A/B tests or other causal inference approaches are near-requirements for decision making. our first investments in tooling to support A/B tests came way back in 2001. Early experimentation tooling at Netflix, from 2001.
I was mostly coding in C, tuning FORTRAN, and when I needed to do a lot of data analysis of benchmark results used the S-PLUS statistics language, that is the predecessor to R. The SE toolkit was fast, had no memory leaks (monitoring scripts could run for years) and let me implement lots of cool performance monitoring ideas.
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