Retail — Automated Product Recommendation Model Retraining

Overview

The purpose of this DAG is to automate the retraining of product recommendation models based on the latest sales data, ensuring that recommendations remain relevant and effective. The primary data sources include ERP transaction logs and CRM sales data, which are essential for understanding customer preferences and purchasing behavior. The ingestion pipeline begins with data extraction from these systems, followed by data preprocessing to clean and format the information for analysis. The proces

The purpose of this DAG is to automate the retraining of product recommendation models based on the latest sales data, ensuring that recommendations remain relevant and effective. The primary data sources include ERP transaction logs and CRM sales data, which are essential for understanding customer preferences and purchasing behavior. The ingestion pipeline begins with data extraction from these systems, followed by data preprocessing to clean and format the information for analysis. The processing steps include training the recommendation models using machine learning algorithms, validating their performance against historical data, and implementing quality control checks to ensure the accuracy of the recommendations. These checks include metrics such as precision and recall, which are critical for assessing model effectiveness. The outputs of this DAG are exposed through a scoring API, allowing seamless integration with retail platforms and enabling real-time access to updated recommendations. Key performance indicators (KPIs) such as recommendation accuracy and user engagement metrics are monitored to evaluate the impact of the retraining process. Ultimately, this automated retraining process delivers significant business value by enhancing the personalization of customer interactions, increasing conversion rates, and optimizing inventory management based on predicted demand.

Part of the SOPs & Playbooks solution for the Retail industry.

Use cases

• Increased customer engagement through personalized recommendations
• Higher conversion rates from improved product suggestions
• Optimized inventory management based on accurate demand predictions
• Reduced manual effort in model retraining processes
• Enhanced decision-making with real-time data insights