  We evaluate how modern outlier detection methods perform in identifying
outliers in e-commerce conversion rate data. Based on the limitations
identified, we then present a novel method to detect outliers in e-commerce
conversion rate. This unsupervised method is made more business relevant by
letting it automatically adjust the sensitivity based on the activity observed
on the e-commerce platform. We call this outlier detection method the fluid
IQR. Using real e-commerce conversion data acquired from a known store, we
compare the performance of the existing and the new outlier detection methods.
Fluid IQR method outperforms the existing outlier detection methods by a large
margin when it comes to business-relevance. Furthermore, the fluids IQR method
is the most robust outlier detection method in the presence of clusters of
extreme outliers or level shifts. Future research will evaluate how the fluid
IQR method perform in diverse e-business settings.
