Retail — Retail E-commerce Anomaly Detection Workflow

Overview

The purpose of this DAG is to detect anomalies in user purchasing behavior across various sales channels, which is crucial for optimizing marketing strategies and improving supply and demand forecasting in the retail sector. The workflow begins by ingesting user behavior data from multiple sources, including e-commerce transaction logs, customer interaction data, and social media engagement metrics. This data is then processed through a series of statistical models designed to identify atypical

The purpose of this DAG is to detect anomalies in user purchasing behavior across various sales channels, which is crucial for optimizing marketing strategies and improving supply and demand forecasting in the retail sector. The workflow begins by ingesting user behavior data from multiple sources, including e-commerce transaction logs, customer interaction data, and social media engagement metrics. This data is then processed through a series of statistical models designed to identify atypical patterns that may indicate potential anomalies in purchasing behavior. Quality controls are implemented to ensure data integrity and confidentiality, utilizing data masking techniques to protect sensitive information. The processed data generates detailed anomaly reports, highlighting critical deviations that require immediate attention. Monitoring key performance indicators (KPIs) such as anomaly detection rate and false positive rate allows for continuous improvement of the model's accuracy. The outputs of this DAG include actionable insights for marketing teams, enabling them to adjust strategies based on real-time behavioral data. Ultimately, this workflow adds significant business value by enhancing customer engagement, optimizing inventory management, and increasing overall sales effectiveness.

Part of the Supply/Demand Forecast solution for the Retail industry.

Use cases

• Improved marketing strategies based on real-time insights
• Enhanced customer engagement through targeted campaigns
• Optimized inventory management with accurate demand forecasting
• Reduced risk of financial loss from unnoticed anomalies
• Increased sales effectiveness through data-driven decisions