Highlights of the Case Study:
|Client||A major Asia Pacific-based retailer in the gasoline industry wanted to implement and develop a robust pricing strategy to drive regional fuel sales.|
|Business Challenge||The client approached Quantzig for tailor-made pricing analytics solutions capable of optimizing price, performing pricing structure across channels, and monitoring price gaps in real-time.|
|Impact||Quantzig’s AI-driven pricing analytics solutions helped the client improve sales margins and leverage the use of predictive modeling to manage prices.|
Game-Changing Solutions for the Oil and Gas Retailing Business
Retail fuel prices have always been determined by considering the fuel’s wholesale price and the prices offered by close rivals. Other essential factors include defining the targeted profit margin and the price range that consumers would accept. Fuel pricing methods still revolve around these data points, but three things have changed significantly. There are now many more data points to take into account, information is accessible in real or nearly real-time, and very little of this process needs to be done manually.
In the oil & gas industry, a lack of an effective pricing strategy can lead to difficulties and limit commercial outcomes. Through our cutting-edge pricing analytics tools, Quantzig can assist companies in navigating this competitive industry.
The Challenges of the Retail Client
Our client is a major retailer in the gasoline industry located in the Asia Pacific region. It collaborated with Quantzig to implement and develop a robust pricing strategy to drive regional sales. The client approached Quantzig for its tailor-made pricing analytics solutions capable of optimizing pricing structure across channels and monitoring the price gaps in real-time.
The client’s primary concern was assessing the market environment’s regulatory standards. They approached Quantzig to leverage its expertise in offering actionable pricing analytics solutions to improve their pricing strategy and boost their retail sales margins. The customer also recognized the value of understanding the pricing tactics of its competitors because it aided in adopting appropriate pricing analytics methods.
With Quantzig’s pricing analytics solutions in oil industry, the client wanted to boost their sales margins through an effective pricing strategy.
Request a Demo of this Case Study.
AI-Driven Pricing Analytics Solutions for the Oil & Gas Retail Industry
Quantzig offered the client an AI-driven pricing analytics solution that extracted patterns and trends in customer behavior to help forecast oil prices. Quantzig adopted a data-driven approach to forecast demand, flow, and price to optimize profit. In addition, we used artificial intelligence assisted with risk management to help manage volatility and uncertainty. Our analytics system has been designed to analyze and predict the following attributes:
- Evaluate sales and profit contribution by division
- Maximize results with price optimization
- Optimize daily replenishment
- Discover the profitable channels
Impact Analysis of Quantzig’s Pricing Analytics Solutions
Quantzig’s pricing analytics solution helped the client improve sales margins and leverage the use of predictive modeling to manage prices. Quantzig’s solutions successfully determined the optimal selling prices, considering all the production-line expenses. Our solutions had the following impact on our client’s business:
- Optimized prices from 19% to 39% in downstream operations
- Provided real-time insights on price fluctuations
- Increased retail pricing transparency by leveraging pricing analytics technologies
Quantzig adopted a data-driven approach to price optimization, leveraging predictive analytics and AI-based methodologies to develop high-impact regional pricing and markup strategies. Additionally, the Quantzig solution helped to improve the entire process by providing better real-time visibility of the supply chain.
Quantzig was thus able to help the client provide real-time insights that helped the client scientifically evaluate all the variables to arrive at an optimum pricing strategy that would help increase its profits and consolidate its position in the market.
A Broad Perspective on the Role of ML and AI for Predictive Analytics in the Oil and Gas Sector:
Machine learning, artificial intelligence, and excellent digital skills can be used to great advantage by oil and gas retailers to assist them in converting large amounts of data into actionable pricing decisions. This will help to enhance the retailer’s gasoline sales strategy effectively. The convenience retailer or network of retailers can use AI and machine learning to predict pricing eventualities in the future.
The financial benefit of using cutting-edge digital tools like AI and machine learning to price fuel correctly can go far beyond the operator’s actual margins on gasoline or diesel. Since 45% of fuel customers will visit the store after refueling, the correctly advertised price for a gallon of fuel can draw a client who then spends money on higher-margin in-store items. On the other hand, incorrect pricing can drive consumers away and reduce profit margins. Therefore, intelligent gasoline pricing is essential not only for the sale of gasoline but propel other prospective sales opportunities.
Therefore, in the modern-day retail scenario, it makes sense to rely on machine learning and AI to enable a precise fuel pricing system to achieve desired margins and sales volumes.
Key Takeaways- Pricing Analytics Solutions in Oil Industry
Quantzig’s predictive analytics solutions enabled the following benefits for the oil and gas retail client:
- Identified the correct oil and gasoline prices based on several factors
- Accounted for and anticipated product shortages while setting pricing strategies
- Evaluated changes in consumption pattern
- Adjusted to competitors’ prices and discounts
- Optimized pricing and markup strategies
Request a Demo of this Case Study.
- Pricing Analytics in the Food and Beverage Industry Helped Increase Revenue for an F&B Major by Improving their Portfolio Pricing?