"We used another full-stack experimentation service in the past, sending them our raw data to analyze experiments in their backend. But the results were questionable. We saw major discrepancies between their results and our calculations. Our leadership team started questioning our work and we quickly realized this fallibility would not convince the leadership team of the benefits of AB testing."
"The results could not be trusted, not even by the data team, let alone the broader business. Starting a new experiment was like taking on a full-blown engineering project, which seriously slowed us down from running more experiments. We couldn’t even implement baseline features like setting up default guardrail metrics across the organization. As a result, different teams with their own unique goals were blind to each other’s metrics, and that led to all sorts of clashes."







