AI-driven pricing optimization boosts revenue by 8% and profit margins by 14% for a leading retailer
A leading global FMCG retailer struggled to compete with retail giants like Walmart. With pressure to maintain profitability while staying competitive, the retailer faced a tough balancing act. Their pricing strategies were static, and their assortment was extensive for their size, making it difficult to respond to market changes or competitor moves.
They needed a smarter, data-driven approach to:
- Model price-demand relationships to optimize prices dynamically and maximize revenue and profit
- Execute weekly pricing adjustments while adhering to enterprise, category, and location-level rules
- Leverage competitor pricing and market trends to stay competitive without sacrificing margins
The challenge was clear: How could they compete with retail giants while maintaining profitability across hundreds of locations?
The retailer partnered with Fuld & Company to optimize its pricing strategy through advanced analytics and AI. By implementing a state-of-the-art pricing solution that integrated competitor data, machine learning algorithms, and demand elasticity forecasting, we empowered the retailer to make informed dynamic pricing decisions across zones, departments, and product categories.
The results were transformative. Within the first three months of deployment, the retailer saw:
- 8% Increase in Revenue: Strategic, category-wide price optimization drove higher revenue without sacrificing sales volume
- 14% Improvement in Profit: AI-driven demand forecasting enabled margin expansion while maintaining competitive pricing
- 80% of Stores Met or Exceeded Revenue & Profit Targets: The majority of stores achieved their financial goals, thanks to dynamic pricing adjustments
- Automated Weekly Pricing Optimization: Prices were updated dynamically at the store, category, and SKU levels, ensuring competitiveness and profitability
The retailer also gained a stronger competitive edge by aligning prices with real-time market insights and consumer behavior.
How We Did It
We developed an AI-powered pricing optimization strategy that combined competitor intelligence, demand elasticity forecasting, and machine learning to deliver real-time, data-driven pricing decisions. Here’s how we did it:
- Competitor Data & Market Intelligence: Integrated competitor pricing and historical sales data into a centralized platform (Databricks) for real-time insights
- Machine Learning-Driven Price Optimization: Built advanced ML models to predict demand-price response and optimize SKU-level pricing for maximum sales and profitability. Used OLS and SARIMAX regression models to forecast future demand and adjust prices proactively
- Dynamic Pricing Execution: Automated weekly pricing updates across zones, stores, departments, categories, and SKUs, ensuring compliance with predefined rules and profitability guardrails. Enabled flexibility to respond to competitor moves and market trends in real-time
- Demand Elasticity & Smart Adjustments: Applied demand elasticity forecasting to predict how price changes would impact consumer behavior. Allowed zonal, department, and category leads to review and approve recommendations before export and implementation
- Performance and Impact Monitoring: Created interactive Power BI dashboards to provide visibility into pricing trends, revenue, and profitability
- Enabled the retailer to compare the impact of previous pricing adjustments on outcomes, ensuring continuous improvement
- Provided a centralized hub for decision-making, empowering teams to identify successes, spot areas for improvement, and communicate impact to stakeholders
With our AI-driven pricing optimization solution, the retailer achieved a winning combination of competitive pricing, increased profitability, and operational efficiency. By harnessing the power of data and cutting-edge technology, we helped them build a strong foundation to not just survive but thrive in a market dominated by retail giants and discounters. In this modern-day tale of David vs. Goliath, they proved that with the right tools and strategies, even smaller players can outmaneuver the biggest competitors—and win.