What type of forecasting would use previous sales data to predict future sales?

Time-series forecasting is a method that specifically focuses on using historical sales data to predict future sales trends. This approach analyzes patterns within the data collected over time, such as seasonal fluctuations, trends, and cyclical behaviors. By identifying these patterns, organizations can make informed predictions about future sales based on the observed past performance.

This type of forecasting is grounded in the belief that past patterns will continue into the future, making it particularly effective when there is a stable and consistent history of data. As businesses collect more sales data over time, time-series forecasting techniques can become increasingly accurate, enabling better inventory management, staffing decisions, and financial planning.

In contrast, qualitative forecasting relies on expert opinions and subjective judgment rather than historical data, causal forecasting looks for cause-and-effect relationships that may not necessarily involve direct time sequences, and random sampling is a statistical method for selecting individuals or items from a larger population but is not a forecasting method itself.