A data analyst is preparing a dataset of monthly customer transaction amounts for a machine learning model. Profiling reveals a small number of legitimate but extremely large B2B purchases that inflate the mean and skew the distribution. The business confirms these transactions are valid and must remain represented in the analysis, but their extreme magnitude distorts the model's sensitivity to typical retail purchases. Which technique best addresses the outliers while satisfying the business requirement?