Which forecasting method applies statistical techniques like time series and trend analysis?

The chosen answer, quantitative revenue forecasting, is the correct response as it relies on statistical techniques to analyze historical data and identify patterns or trends that can be used to predict future revenue. This method encompasses various approaches, including time series analysis, which examines data points collected or recorded at specific time intervals, and trend analysis, which looks at long-term patterns in data to project future values.

Quantitative revenue forecasting is particularly effective in environments where data is abundant and consistent. By utilizing mathematical models, analysts can derive more objective forecasts that are based on measurable past performance rather than subjective opinions or estimates. This rigorous approach provides a foundation for sound financial planning and decision-making.

Other methods, such as qualitative revenue forecasting, rely heavily on expert judgment, market research, and general economic indicators rather than statistical analysis. Naïve revenue forecasting typically employs a simple method, such as using the most recent data point as the forecast for the future without any statistical manipulation. Lastly, outlier detection is a technique used to identify anomalies in data but does not itself focus on forecasting. Therefore, the use of statistical techniques inherent in quantitative forecasting makes it the appropriate choice for the question.