Improving Customer Experience for a logistics major

Situation

A global market leader in logistics and express delivery was facing challenges with inaccurate delivery time predictions, which were impacting customer experience and increasing call center costs. To address these issues, the company sought to improve its delivery time predictions to enhance customer satisfaction and protect its market share.

Solution

• We were engaged in a strategic initiative to develop a proof of value and solution blueprint for improving delivery time predictions. By leveraging advanced machine learning models, we estimated significant potential savings in contact center operations.
• Together with our AI Experience practice we developed new machine learning models that outperformed existing rule-based forecasting methods. These models were designed to handle high-volume, high-velocity data streams and capable of delivering over 3 billion daily predictions.
• To support these models, we designed a world-class cloud-based analytics data platform with robust security, scalable architecture, and efficient AI/ML development procedures. This platform is capable of handling dynamic workloads and executing complex machine learning models.

This lead into

• Multimillion-dollar savings in contact center cost due to reduced call volume in call center operations
• Increased accuracy of predictions, and ML models
• ML models able to forecast as early as 4% into the lifecycle vs previous 52%