Highlights
Industry:
eCommerce and Retail
Project scope:
- Development of an AI driven pricing system that monitors market signals and automatically optimizes product prices for different countries, currencies, and customer segments
The Client
A multinational online retail company serving customers in over 20 countries through several high-traffic eCommerce platforms. The company manages a large, frequently changing product catalog with seasonal fluctuations, promotions, and flash sales across regions.
The Project
The client operated across diverse markets with different levels of competition, purchasing power, and price sensitivity. Pricing decisions were managed manually, often based on historical rules or short-term promotions, without real-time visibility into market changes. This made it difficult to adjust prices quickly and consistently across multiple regions, especially during high-demand periods and sales campaigns.
As a result, products were frequently either underpriced, reducing potential revenue, or overpriced, lowering conversions. To solve this, the client needed an AI powered pricing engine that would continuously analyze demand, competitor activity, customer behavior, and regional factors to recommend optimal prices in real time, while still allowing human oversight for strategic or high-impact pricing actions.
Project duration:
6 months in active phase
Project team:
Solution Architect, Data Scientist, AI/ML Engineers, Business Analyst, QA Engineers
Project labor costs:
18 man-months in active phase
Technology stack:
Python, FastAPI, PostgreSQL, TensorFlow, Pandas, Airflow, Redis, REST API
The Solution
XIM built an AI driven Dynamic Pricing Engine integrated with the client’s eCommerce platform, product catalog, and analytics systems.
The system collects and analyzes data on product demand, competitor prices, stock levels, promotions, and purchasing behavior. Machine learning models predict price elasticity and calculate optimal price points per product, region, and customer segment. The engine provides actionable pricing recommendations with projected impact on revenue, conversion, and margin.
Approved pricing updates are synchronized automatically with the online store, while high-value or high-risk items follow a manager approval workflow. The pricing model is continuously retrained to adapt to seasonal trends, customer behavior patterns, and evolving competition.
The Outcome
The Dynamic Pricing System significantly improved pricing precision and responsiveness to market changes. The client increased revenue and margin performance without harming conversion rates, and reduced manual pricing work across regions. Teams gained full transparency into pricing decisions through data-driven recommendations and clear rationale for each change.