CASE STUDY: ENERGY

Transforming Fuel Pricing into a Data-Driven Profit Engine

Year 1 Margin
Increase

Estimated $7.7M in incremental gross margin, validated through A/B testing of the ML-driven pricing approach

3 Year
ROI

Projected $23M+ return over three years, based on current performance trends (conservative estimate)

AI-Driven Pricing
at Scale

Shifted from manual pricing to AI-driven, near real-time decisioning, enabling faster response to market dynamics and scalable expansion

CHALLENGE

Fuel pricing was largely manual and difficult to scale across a distributed network, leading to:

Disconnected data and delayed decision-making
Slow response to market changes, reducing competitiveness
Inconsistent pricing execution impacting margins
High operational effort to maintain pricing strategies

This limited the ability to capture margin opportunities in a fast-moving market.

SOLUTION

Bits In Glass built and implemented the Dynamic Fuel Pricing Assistant, an ML-driven platform on Databricks enabling real-time pricing at scale, including:

Unified internal and external data integration
AI-driven pricing recommendations updated daily
Automated monitoring and reliable operations
Continuous model optimization

This established a scalable, cost-efficient pricing foundation.

TECHNOLOGY

About the Client

A leading North American fuel distributor operating a large network of retail fuel stations across the United States. The organization manages highly dynamic, region-specific pricing influenced by market volatility, competitive activity, and diverse data inputs. The client set out to modernize its pricing approach by adopting advanced analytics and machine learning to improve decision speed, margin performance, and scalability.