Anomaly Detection for an E-commerce Pricing System
This addresses the business-critical need for real-time anomaly detection in e-commerce pricing to prevent revenue loss and maintain customer trust, though it appears incremental in applying existing detection methods to a specific domain.
The paper tackles the problem of detecting pricing anomalies in Walmart's large-scale e-commerce system, developing and deploying both unsupervised and supervised approaches that successfully identified critical anomalies with high precision in production.
Online retailers execute a very large number of price updates when compared to brick-and-mortar stores. Even a few mis-priced items can have a significant business impact and result in a loss of customer trust. Early detection of anomalies in an automated real-time fashion is an important part of such a pricing system. In this paper, we describe unsupervised and supervised anomaly detection approaches we developed and deployed for a large-scale online pricing system at Walmart. Our system detects anomalies both in batch and real-time streaming settings, and the items flagged are reviewed and actioned based on priority and business impact. We found that having the right architecture design was critical to facilitate model performance at scale, and business impact and speed were important factors influencing model selection, parameter choice, and prioritization in a production environment for a large-scale system. We conducted analyses on the performance of various approaches on a test set using real-world retail data and fully deployed our approach into production. We found that our approach was able to detect the most important anomalies with high precision.