Development of a Dynamic Pricing and Markdown Optimisation Engine for Indian Retailers

Objective: To design and implement a data-driven engine that dynamically optimises product pricing and markdowns to maximise revenue and inventory turnover for Indian retail and e-commerce businesses.

Indian retailers face intense price competition and high inventory costs, especially during festivals and end-of-season sales. Static pricing often leads to lost sales or excessive markdowns, impacting profitability in the highly dynamic Indian e-commerce landscape.

This project proposes building a dynamic pricing and markdown optimisation engine using historical sales, competitor pricing, and demand data. The team will leverage machine learning models to predict demand elasticity and optimise prices in real-time for key SKUs.

Deliverables include: a data pipeline integrating sources (e.g., POS data, Flipkart/Amazon APIs), demand forecasting models, optimisation algorithms, a user dashboard (in Power BI or Tableau), and actionable insights for pricing managers.

The solution will help retailers and e-commerce firms improve revenue realisation, reduce unsold inventory, and make data-driven pricing decisions tailored to the Indian consumer and competitive context.