Business Challenge

In recent years, the oil and gas market has become increasingly volatile, with prices fluctuating more frequently due to global factors and emerging competition. The entrance of new local and international competitors has also increased pressure on margins, making it difficult to maintain profitability without sacrificing volume or customer satisfaction.

Key challenges faced by the client included:

• Navigating rapid fluctuations in oil and gas prices.

• Rising competition leading to lower margins.

• The need to maximize profit while staying competitive and maintaining or increasing sales volumes.

• Identifying which competitors truly impacted their market and how to strategically position their prices to stay ahead.

Scope of Project

To tackle these challenges, the client sought a comprehensive solution that would optimize their pricing strategy across the city’s network of stations. The main objectives of the project were:

• Citywide Optimization: Maximize performance at the city level by adjusting prices at each station to optimize overall profitability.

• Profit Maximization: Increase profit margins while maintaining or even increasing sales volumes where possible.

• Competitiveness: Stay competitive by identifying key competitors and understanding which ones required more attention in pricing strategies.

Our Solution

We introduced our advanced Real-Time Dynamic Pricing System powered by industry-leading AI and Machine Learning models. This solution was designed to respond instantly to market changes, competitor pricing, and customer demand while being granular enough to adjust prices at a station product level.

Key components of our solution included:

• Real-Time Dynamic Pricing: Our system allowed the client to adjust prices in real time based on current market conditions, competitor activity, and customer demand, ensuring that they stayed competitive in a fluctuating market.

• Advanced AI & Machine Learning Models: Our models are among the most advanced in the industry, designed to continuously learn from new data and market behaviors. This enabled the client to stay ahead of competitors by leveraging the latest technological advancements for pricing optimization.

• Global Optimization Pricing: Instead of focusing on maximizing profit at individual stations, our models considered the entire city’s network of stations. Some stations might sacrifice a little volume or profit, but the overall profit for the network was maximized. This approach ensured a more balanced, strategic approach to pricing that considered the city as a whole.

• Granular Station-Level Adjustments: Our system allowed for detailed pricing adjustments at the station-product level, enabling precise control over pricing strategies based on location-specific factors.

• Business rules automation: Given the complexity of the operations and the market, there were a set of rules that the model had to meet, but sometimes it was mathematically impossible to meet all the rules, therefore a decision making process was created to set the optimal price while maintaining all or at least the most important business rules in place.

Results

The implementation of our dynamic pricing solution resulted in significant improvements in profitability and competitiveness. Within the first few months, the client experienced the following:

• Increased Citywide Profits: By leveraging our global optimization solution, the client saw a significant boost in the total city’s average daily profits by +30%. Our model strategically maximized profits at certain stations while focusing on volume growth at others, ensuring that the entire network benefited from a balanced, holistic approach.

• Increased Citywide Volume: Through tailored pricing strategies, the system identified the most opportune stations to drive volume increases. This approach resulted in an increase in total city’s average daily volume by +5% and total city’s average daily profits by +30%, optimizing performance at every level.

• Actionable Insights and Station Segmentation: Our advanced AI identified clusters of stations with similar behaviors based on factors such as seasonality, day of the week, and other key variables. By leveraging these insights, we were able to fine-tune the model’s performance, further optimizing profitability and competitiveness for the client.

• Enhanced pricing precision, allowing them to respond to market changes more effectively and stay ahead of local competitors.