ABOUT THE CLIENT
The client is a drayage marketplace that links freelance carriers, including owner-operators and small fleets, with various customers. They manage the equilibrium between customer demand and the availability of carriers in specific geographic regions, ensuring that prices remain reasonable and controlled.
THE CHALLENGE
- Erratic Carrier Retention: The platform experienced unpredictable fluctuations in carrier retention, necessitating an investigation into the causes and the development of strategies to retain top-performing carriers.
- Operational Inefficiencies in Distribution: The competitive nature of the marketplace led to rapid job allocation, resulting
- in high cancellation rates and suboptimal outcomes. It was crucial to ensure that the most efficient carriers received appropriate jobs to maximize their capacity.
- Carrier-Load Matching Inefficiencies: While distribution efforts improved workload for top carriers, there remained a
- need for enhanced system efficiency. The challenge was to refine the carrier-load matching process, balancing efficiency with the strengths of the existing distribution system.
THE SOLUTION
- Conducted a detailed analysis of carrier/driver activity over 1.5 years to understand work allocation challenges.
- Launched a targeted campaign to increase awareness among carriers about available work, addressing high churn rates.
- Developed a scoring system based on performance characteristics to prioritize job allocation for top-performing carriers.
- Designed and implemented a machine learning model to efficiently match carriers with suitable loads, doubling the work share for top carriers and significantly improving job acceptance rates.
- How We Leveraged Looker:
- Looker analyzed extensive carrier data, identifying competitive allocation insights.
- Utilized Looker’s reporting with over 250 complex tables for campaign management.
- Looker optimized scoring for assignment allocation, maximizing top carrier work.
- Integrated Looker for real-time monitoring in ML model effectiveness.
- Conducted A/B testing with Looker to refine assignment matching and efficiency.
KEY RESULTS
- Achieved a 30% reduction in month-over-month churn among high-performing carriers.
- Resulted in a 35% drop in cancellation rates for assigned jobs.
- Doubled the work share for the best-performing carriers.
- Improved job acceptance rate by 200% following the implementation of the ML model for carrier-load matching.