Automated detection of business-relevant outliers in e-commerce conversion rate
This addresses the need for more effective outlier detection in e-commerce analytics, though it appears incremental as it builds on existing outlier detection methods with a domain-specific adaptation.
The paper tackles the problem of detecting business-relevant outliers in e-commerce conversion rate data by proposing a novel unsupervised method called fluid IQR, which automatically adjusts sensitivity based on platform activity and outperforms existing methods by a large margin in business-relevance and robustness.
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.