Case Study
AI-Based Pricing Optimization for Online Marketplaces
Problem Statement
An online multi-vendor marketplace struggled to maintain competitive pricing across thousands of products. Static pricing models led to missed sales opportunities, inventory imbalances, and poor profit margins. The company wanted to implement an AI-based dynamic pricing system that could optimize prices in real time, based on market demand, competition, seasonality, and user behavior.
Challenge
Data Complexity: The platform hosted over 500,000 SKUs with constantly changing supply and demand variables.
Competitor Tracking: Competitor prices varied frequently, requiring constant monitoring.
Price Sensitivity: Products in categories like electronics and fashion showed strong customer price sensitivity.
Scalability: The pricing system had to handle real-time decisions at scale across different regions and sellers.
Profit Margin Protection: The model had to balance competitive pricing with profit goals and discount limitations.
Solution Provided
The marketplace implemented an AI-powered pricing engine using machine learning and real-time data inputs to automate pricing decisions. The system was designed to:
Predict Optimal Prices: Use historical sales, user behavior, and external market data to predict price elasticity and optimize pricing.
Monitor Competitors: Integrate web scraping APIs to monitor competitor pricing and adjust accordingly.
Real-Time Adjustments: Dynamically change product prices based on supply, demand, time of day, and user segments.
Custom Rules Engine: Allow vendors to set pricing thresholds and constraints to maintain brand value and profitability.
Development Steps
Data Collection
Collected transactional history, competitor prices, traffic patterns, and inventory data.
Feature Engineering
Created features such as demand trends, seasonal shifts, customer segments, and time-based patterns.
Model Development
Built price optimization models using XGBoost and Random Forest algorithms to predict the optimal price point for conversion and margin.
A/B Testing
Implemented controlled testing on select product categories to evaluate model performance versus static pricing.
Integration
Deployed the pricing engine through REST APIs, allowing seamless integration with the marketplace backend.
Monitoring & Feedback Loop
Used dashboards and feedback mechanisms to continuously monitor model performance and retrain weekly.
Results
Sales Increased by 24%
Real-time pricing led to higher conversion rates during peak hours and promotional periods.
Profit Margins Improved by 12%
Intelligent discounting preserved margins while remaining competitive.
Reduction in Manual Pricing Effort
Automated over 90% of pricing decisions, saving significant time for marketplace admins and vendors.
Lower Cart Abandonment Rate
Dynamic offers and price reductions at checkout encouraged more completed purchases.
Vendor Satisfaction Improved
Sellers appreciated greater pricing control and increased sales volume without deep discounting.