30% fuel savings and 40% more stops per driver

A regional courier operator runs a mixed fleet of vans and cars serving B2B and B2C customers across a large metro area. With fuel prices rising and drivers often building routes on the fly, mileage per delivery had crept up and margins were under pressure. The operations team had no single view of planned vs actual routes or driver efficiency.

Geofleet was deployed to optimize routes using real-time traffic, vehicle capacity, and time windows. Drivers received optimized sequences and turn-by-turn navigation in the Geofleet app, reducing wrong turns and backtracking. Managers used the control tower to monitor live progress and intervene only when needed.

Within six months, average miles per delivery dropped by 30%, directly reducing fuel spend. Stops per driver per day increased by 40% without adding vehicles or overtime. On-time delivery reached 95%, and driver feedback was positive due to clearer instructions and less stress.

Crucially, the operation now had a single number for fuel cost as a percentage of revenue — and watched it fall from 22% to 15%. That swing turned a margin under steady pressure into headroom the business could reinvest in growth.

For a courier operator, fuel and mileage are among the few large costs that are truly controllable without cutting service. With prices rising, every wasted mile was eroding a margin that was already thin. Reclaiming 30% of mileage was the difference between absorbing fuel inflation and passing it on to customers.

Adding 40% more stops on the existing fleet also deferred capital spend: the operator could take on more volume before it ever needed to buy or lease another vehicle, improving return on assets across the board.

The team credited two things for the durable result. First, baselining mileage and stops before go-live, then running an A/B comparison against legacy routing, made the savings undeniable to both finance and drivers. Second, treating optimization as continuous rather than one-off — the AI keeps learning from completed deliveries — meant the gains compounded instead of decaying as the network changed.

They also found that driver buy-in mattered as much as the algorithm. Because the optimized routes genuinely made drivers’ days easier, adoption was fast and the team didn’t fight the system.