In a dataflow, what is a best practice for tracking changes to customer behavior over time?

Implementing time-stamped events is a best practice for tracking changes to customer behavior over time because it allows you to capture the exact moment when a specific action occurs. This temporal granularity is crucial for understanding not just what actions customers are taking, but when they are taking them. By associating customer interactions with specific time markers, businesses can analyze trends over different time periods, correlate changes in behavior with marketing campaigns or external events, and understand the evolution of customer preferences and habits.

This approach enriches the dataset with time-series data, making it easier to perform longitudinal studies and identify shifts in customer behavior that occur as a result of strategic changes or market dynamics. Time-stamped events lend themselves well to advanced analytics techniques, enabling the identification of patterns and forecasting future behavior based on historical data.

In contrast, static reporting methods fail to capture the dynamic nature of customer interactions, and conducting periodic analysis might miss real-time insights. Focusing on immediate reports doesn't provide the depth of understanding needed to see how customer behavior evolves, reducing the effectiveness of data-driven strategies over time.