Once we launch a feature, we also need to measure and track how our product responds under different scenarios. We defined success metrics to validate the bets we made about the feature idea's impact on key business outcomes. While we check these metrics, we are also evaluating other limitations of our product. Does our product still work well when there is a surge in demand? How does our app respond to peak time demand? What if the peak demand period shifts? What if a new consumer base is adapting our product? Does our product work well in a different geography?

These are ongoing health checks that are needed to ensure that our product continues to deliver and drive value to the consumer and to the business. The data that we gather when a feature is live will be useful in the next phase of product building. If the product performance is stopping us from meeting customer demand, then this is an important input for stakeholders when they decide which key business outcome to invest in. These metrics not only help us respond to change but also to enhance our product's capabilities and identify its limitations. Individual health metrics may not provide ample data that could drive a decision to pivot. However, they may over time provide ample data points to unearth trends, bottlenecks, and strengths. They may also help us to understand the success or failure of an individual feature. Databases, user interaction analytics, volume and performance tracking tools, and so on, can be part of our data strategy to capture and analyze data and trends over time.