Predictive Pricing System for Retail

AI-based pricing system that analyzes competitor data and demand to recommend optimal prices for retail stores in real time.

TRL: 5

Stage: Prototype Development

What Problem Are We Solving
Retail stores struggle to set the right product prices due to changing factors like competitor pricing, demand, and market trends. Most businesses rely on manual methods, which are slow and often inaccurate. Tracking competitor prices across multiple platforms is difficult, leading to wrong pricing decisions. As a result, businesses either lose customers by pricing too high or lose profit by pricing too low.
Our project solves this problem by developing an AI-based smart pricing system that uses real-time competitor data and predictive analytics to suggest the best possible prices. This helps businesses improve profitability, reduce manual effort, and stay competitive in dynamic retail environments.
Who Are the Customers
The primary customers are physical retail store owners and small to medium-sized businesses that need better pricing strategies. These include electronics shops, grocery stores, and general retail outlets. Secondary customers include e-commerce sellers and wholesalers who want data-driven pricing. Retail chains and startups can also use this system to improve pricing decisions, increase profits, and stay competitive.
AI-Based Predictive Pricing System for Retail Stores
The Predictive Pricing System is an AI-based solution that helps retail stores decide the best price for their products. Traditional pricing methods are manual and slow, and they cannot handle fast-changing market conditions. Our system solves this problem by using real-time data and machine learning.
The system first collects data from different sources. It uses historical product data and also fetches real-time competitor prices from platforms like Amazon and Flipkart using APIs such as SerpAPI. This data includes product price, demand, and competitor information.
Next, the data is cleaned and processed using tools like Pandas and NumPy. This step removes errors and prepares the data for analysis. After that, a machine learning model called ElasticNet is used to learn from the data and predict the best price for each product.
When a user enters product details, the system collects the latest market data and applies the trained model. It then generates a recommended price that balances profit and competition.
The system is built using FastAPI for backend processing and provides results through a simple dashboard or interface. Store owners can easily view and use the suggested prices.
In the future, the system can be improved by adding customer behavior analysis, personalized pricing, and full integration with retail systems.