Which approach is effective for comparing current sales data with historical data in a story?

The most effective approach for comparing current sales data with historical data in a story is time series analysis. This method focuses on analyzing data points collected or recorded at specific intervals over time. By employing time series analysis, organizations can observe trends, patterns, and fluctuations in sales data, allowing for meaningful comparisons with historical data.

This approach facilitates the identification of seasonal variations and cyclical patterns, which are crucial for understanding how sales trends evolve over periods, making it easier to draw insights and make informed decisions.

Benchmarking refers to comparing performance metrics with industry standards or best practices, which, while useful for assessing relative performance, does not directly compare current performance with historical trends in the same way as time series analysis.

Predictive forecasting involves using historical data to make predictions about future outcomes, with a focus on anticipating future sales rather than directly comparing current and past performance.

Variance analysis examines the differences between planned financial outcomes and the actual performance, primarily focusing on identifying deviations rather than on a continuous time-based comparison of data.

Thus, for directly comparing current sales figures with historical data, time series analysis provides the appropriate framework and insights.