Purolator

Using AI to Predict Variable Operating Margins and Reduce Financial Risk

The Challenge

With over 175 operational facilities, 1,300+ shipping agents, and a diverse fleet of more than 5,000 vehicles, Purolator processes a massive volume of daily shipments—ranging from small packages to hazardous goods. Managing profitability at scale while optimizing delivery logistics and pricing strategy across such a complex network presented a significant challenge.

Purolator’s CFO and CIO sought to explore whether AI could:

  • Predict Variable Operating Margins (VOM) with enough lead time to influence decisions
  • Detect operational inefficiencies or loss-making shipments earlier in the process
  • Provide a clearer financial outlook to guide resource allocation and improve customer experience
  • Establish a trustworthy, explainable AI model rather than a black-box approach

Purolator is a leading international courier, freight, and logistics company, handling over 100 million shipments annually and generating more than $1 billion in revenue.

With 13,000+ employees and a vast operational network across North America, Purolator is known for its forward-thinking use of AI and advanced analytics to drive continuous improvement, particularly during the high-pressure period of the COVID-19 pandemic.

SalesChoice helped us solve a complex financial problem—predicting VOM two months ahead of when we’d traditionally see it. Their team brings scientific rigor, transparency, and a true spirit of collaboration. They’ve been instrumental in helping us strengthen both our customer experience and financial planning.

Paul MoranSenior Director of Finance, Technology Execution & Deployment

The Solution

SalesChoice partnered with cross-functional teams across Finance, IT, and Revenue Operations to design and deliver an AI-powered solution to predict variable operating margins and enhance financial foresight.

Key solution components included:

  • Assessment of existing data lineage and accountability structures
  • Research and application of AI data governance best practices
  • Data quality validation to ensure usable, complete, and trustworthy sources
  • Development of a custom AI model trained on two years of shipment and costing data
  • Creation of Power BI dashboards to visualize and communicate model insights
  • Engineering support to simulate model outputs and prepare for production deployment

This initiative marked a significant step in Purolator’s AI journey—demonstrating that predictive financial insights could be delivered months in advance, giving business leaders time to course-correct.

The Results

With SalesChoice’s support, Purolator was able to:

  • Predict Variable Operating Margins two months in advance, improving financial control
  • Identify patterns in loss-making shipments and take earlier corrective action
  • Build a sustainable AI governance framework to scale future models
  • Improve executive visibility with clear, auditable Power BI dashboards
  • Maintain detailed documentation and AI model history for transparency and future enhancements