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Retail — Retail E-Commerce Pricing Optimization Pipeline

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This DAG optimizes pricing and promotional strategies by analyzing sales and inventory data. It leverages predictive models to assess the impact of pricing changes, enhancing revenue and marketing effectiveness.

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Overview

The purpose of this DAG is to optimize pricing and promotional policies for retail e-commerce businesses by analyzing sales and inventory data. The data sources include sales transaction records, inventory levels, and historical promotional effectiveness metrics. The ingestion pipeline collects this data from various retail systems, ensuring data integrity and timeliness. The processing steps involve data cleaning, feature extraction, predictive modeling, and generating actionable pricing recomm

The purpose of this DAG is to optimize pricing and promotional policies for retail e-commerce businesses by analyzing sales and inventory data. The data sources include sales transaction records, inventory levels, and historical promotional effectiveness metrics. The ingestion pipeline collects this data from various retail systems, ensuring data integrity and timeliness. The processing steps involve data cleaning, feature extraction, predictive modeling, and generating actionable pricing recommendations. Quality controls are implemented to validate the accuracy of the predictive models and the relevance of the recommendations. The outputs of this DAG include optimized pricing strategies, promotional recommendations, and a report on expected sales uplift. Monitoring key performance indicators (KPIs) such as sales increase and return on investment (ROI) from promotions allows stakeholders to measure the success of the implemented strategies. Additionally, the DAG is designed to restart automatically in case of failures, ensuring continuous operation and reliability. The business value lies in improved pricing strategies that drive sales growth and enhance profitability, ultimately leading to a competitive advantage in the retail market.

Part of the Fraud & Anomaly Analytics solution for the Retail industry.

Use cases

  • Increased sales through optimized pricing strategies
  • Enhanced ROI on marketing promotions
  • Improved inventory turnover rates
  • Data-driven decision-making for pricing policies
  • Competitive edge through agile pricing adjustments

Technical Specifications

Inputs

  • • Sales transaction records
  • • Current inventory levels
  • • Historical promotional effectiveness data

Outputs

  • • Optimized pricing strategies
  • • Promotional recommendations report
  • • Sales uplift forecast

Processing Steps

  1. 1. Collect sales and inventory data
  2. 2. Clean and preprocess the data
  3. 3. Extract relevant features for modeling
  4. 4. Apply predictive models to assess pricing impact
  5. 5. Generate pricing and promotional recommendations
  6. 6. Store recommendations in promotion management system
  7. 7. Monitor KPIs and trigger alerts on failure

Additional Information

DAG ID

WK-0268

Last Updated

2026-02-16

Downloads

56

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