Which analysis technique assists in identifying variances in sales data over time?

The analysis technique that assists in identifying variances in sales data over time is time series analysis. This method involves examining data points collected or recorded at specific time intervals. By analyzing trends, seasonal patterns, and cyclic behaviors, time series analysis enables users to understand how sales figures change over time, making it possible to identify variances and deviations from expected performance.

Time series analysis offers insights that can help detect patterns in sales data, highlighting periods of significant change or fluctuation, which is essential for businesses aiming to improve sales strategies and forecast future performance based on historical data trends.

While variance analysis is a method used to derive the difference between expected and actual performance, it is not solely focused on time-related data. Instead, it typically compares actual results to specific benchmarks or standards, which may not inherently involve analyzing data across different time frames. Thus, while variance analysis is important for performance measurement, time series analysis is more precisely tailored for understanding temporal changes in sales data.