What type of data is necessary for executing Demand Sensing algorithms effectively?

To effectively execute Demand Sensing algorithms, historical lag-based snapshot data is essential because it provides a comprehensive view of past demand patterns and trends. This type of data captures the regular fluctuations in demand over time, allowing the algorithms to detect and adapt to shifts in consumer behavior more promptly. Demand Sensing relies on analyzing historical data to create a baseline, which is then adjusted based on real-time signals and changes in demand.

While other data types might offer some insights, they do not serve the primary purpose of demand sensing as efficiently. For instance, real-time market news feeds may be relevant for understanding broader market trends but do not directly reflect sales patterns, and thus they do not provide the historical context that is crucial for effective Demand Sensing. Similarly, sales output from the last quarter can be useful; however, it is a limited snapshot that does not capture the full scope of demand variability over time. On the other hand, forecast models tailored to specific products may predict demand but do not include the historical variances and actual sales history needed for real-time adjustments. Hence, historical lag-based snapshot data is the most beneficial for executing Demand Sensing algorithms effectively.