Metrics are great as an objective measure of feature releases/goals/success, but they don't provide the whole picture in terms of product success or direction.

Metrics are great as an objective measure of feature releases/goals/success, but they don't provide the whole picture in terms of product success or direction. Years ago I interned on a messaging team at a FAANG company where everything was driven by ~3 metrics. We had a screen with the trailing 30d average of our metrics, future work was prioritized based on the estimated impact to the metric, and the success of our team was based on these 3 metrics. My intern project involved some machine learning modeling using the (anonymized/scrubbed) content of the messages sent with our product, and despite the team having existed for >12 months, this was the first time anyone on the team had read a message sent with the product. In this case, the sharp focus on metrics was super useful for prioritizing features and evaluating our success - but was done so to a fault when the team didn't have a concrete idea of what people were trying to do with our product. The usual solution to this is to spend more time talking to users - it'd be interesting if there was a process that can help teams know how much time to spend on metrics vs qualitative feedback