You are asked to evaluate whether a product or newly launched feature is successful. Describe how you would define success from a data science and product analytics perspective. Your answer should cover: - The product objective and how success depends on the product's stage (launch, growth, maturity). - A primary success metric or north-star metric. - Supporting metrics across the user funnel, such as acquisition, activation, engagement, retention, monetization, and user satisfaction. - Guardrail metrics that ensure the product is not improving one outcome while harming others. - How you would distinguish correlation from causal impact, for example through A/B testing or quasi-experimental methods. - How you would handle confounding factors, seasonality, novelty effects, selection bias, and heterogeneous effects across user segments. - What decision framework you would use to conclude whether the product is successful.