Last-Mile Performance

Cancellation rate is the share of placed orders that are canceled before delivery is completed, whether initiated by the retailer, the carrier, or the customer. It reflects demand that was accepted but never fulfilled and is distinct from returns, which occur after a fulfilled delivery.

Calculated as total canceled orders divided by total orders placed within a given period, usually segmented by cause. Retailers separate carrier-driven cancellations (coverage gaps, capacity shortfalls) from merchant-initiated ones (inventory mismatches, pricing errors) since each points to a different root problem and owner.

Cancellation rate exposes whether a business can actually deliver on demand it accepts, not just generate it. Carrier-caused cancellations signal network risk and capacity gaps, especially during peak periods. Merchant-caused cancellations signal broken inventory or fulfillment logic upstream of delivery, both of which erode customer trust immediately.

Rising cancellation rates convert marketing spend into wasted acquisition cost, since the sale never completes. Customers who experience a cancellation are less likely to reorder. Repeated failures during high-demand periods expose capacity planning gaps that compound as volume grows, which often reveal themselves too late to correct.

Customer rating is the score shoppers assign to their delivery experience, typically gathered through post-delivery surveys, app prompts, or star ratings tied to a specific order or driver. It captures subjective satisfaction with the delivery itself, separate from product quality or overall brand sentiment.

Calculated as an average score per order, driver, region, or delivery window, usually on a 1-to-5 scale collected immediately after delivery confirmation. Some retailers weight ratings by order value or customer tier to prioritize feedback from their highest-value, most frequent shoppers.

Customer rating is a leading indicator of repeat purchase behavior that often surfaces dissatisfaction before it shows up in retention or lifetime value metrics. Unlike price or product reviews, it reflects the most recent, most tangible interaction a customer has with a brand after checkout.

Declining ratings foreshadow churn well before it appears in revenue reporting and can offer retailers a narrow window to intervene. Left unaddressed, low ratings compound: dissatisfied customers order less frequently, spend less per order, and are more likely to switch to a competitor after just one poor experience.

OTIF measures whether an order arrives by the promised delivery date, complete and without damage. It combines two conditions, timeliness and completeness, into a single pass-or-fail outcome, since a delivery that is on time but missing items still breaks the customer's expectation.

Calculated as the number of on-time, in-full deliveries divided by total deliveries attempted over a given period. Retailers often segment OTIF by carrier, region, or delivery method to isolate which parts of the network are driving missed promises.

OTIF is the clearest, most direct evidence that a business kept the specific promise made at checkout. Because it's binary, a delivery either meets the promise or it doesn't, it removes ambiguity from performance reporting and ties execution quality straight back to the commitment customers were sold.

Missed OTIF targets drive a predictable chain reaction: rising WISMO (where is my order) contact volume, increased refund and reship costs, and declining customer ratings. Repeated failures push customers toward permanent retailer abandonment, since delivery reliability is one of the hardest broken promises to recover from.

Cart conversion measures how many shoppers complete checkout after reaching the delivery-options step, where cost, speed, and available windows are presented. It isolates the delivery experience's specific impact on purchase completion and is separate from broader site or product-page conversion metrics.

Calculated as completed orders divided by the number of shoppers who reached the delivery-options or shipping-cost stage of checkout. Retailers often break this down further by delivery option selected. They compare conversion across free, standard, and expedited choices to isolate which options actually close sales.

Delivery cost and speed are consistently among the top reasons shoppers abandon a cart, so this stage is one of the last controllable moments before a sale is lost. Cart conversion shows whether the delivery promise itself, not the product or price, is closing or costing the sale.

Poor cart conversion means shoppers reject the delivery promise before an order even ships, which turns marketing and merchandising work into wasted spend. Left unaddressed, this shows up as a persistent revenue leak that's often misattributed to pricing or product issues rather than the actual checkout-stage delivery experience.

Delivery cost is the total expense of getting an order from dispatch to the customer's door, driven primarily by distance traveled and driver time per stop. It captures the direct operational cost to fulfill the delivery promise, separate from upstream fulfillment or warehousing expenses.

Calculated as total delivery expense (fuel, labor, vehicle) divided by number of deliveries or stops in a given period, often expressed as cost per delivery or cost per mile. Retailers and LSPs segment this by route density, region, and delivery type to isolate where costs concentrate.

Delivery cost is the most visible line item tying last-mile execution to margin, and it's the number finance teams scrutinize first. Since it now represents more than half of total shipping cost, small inefficiencies in distance or drive time compound quickly across daily route volume.

Rising delivery costs damage margins on every order, and the pressure often gets passed to the customer as higher shipping fees, which undermine the checkout conversion this framework tries to protect. Left unmanaged, it forces retailers into a bind between raising prices and absorbing losses.

Dispatcher cost is the labor and time expense of planning, assigning, and adjusting routes and driver schedules, including manual intervention required when plans change. It reflects the operational overhead behind getting a delivery plan in place, before a single mile is driven.

Calculated as planning labor hours or cost divided by number of routes or orders dispatched. It’s often tracked alongside time-to-action metrics such as how quickly a dispatcher responds to a delay, cancellations, or reroute requests during an active shift.

Dispatcher cost reveals how much manual effort a network requires to function, which is a direct signal of how automated or fragmented the planning process actually is. High manual intervention slows response time to disruptions and limits how many routes a single dispatcher can effectively manage.

High dispatcher cost caps how much delivery volume a business can scale without proportionally growing headcount. Slow time-to-action on exceptions cascades into missed OTIF targets and rising driver idle time, since delays in replanning routes ripple forward into every subsequent stop on the schedule.

Customer care cost is the expense of handling post-purchase delivery issues, primarily WISMO (where is my order) calls, and the time required to resolve exceptions like delays, damage, or missed deliveries. It captures the cost of a delivery promise that requires intervention to make right.

Calculated as support labor cost divided by delivery volume, and often tracked alongside WISMO call volume as a percentage of total orders and average time-to-resolution per exception. Retailers segment this by cause to see which failure types drive the most support burden.

Customer care cost is a direct, measurable consequence of upstream delivery failures. It’s one of the clearest signals that a visibility or reliability gap exists elsewhere in the network. Every WISMO call represents a customer who didn't trust the delivery promise enough to wait quietly.

Rising customer care cost signals that failures upstream, missed OTIF, poor visibility, are being pushed downstream into support teams rather than prevented. Left unaddressed, it inflates cost-to-serve on every order and signals declining customer confidence long before it shows up in retention numbers.

Driver utilization is the percentage of a driver's paid shift spent on active, productive delivery work as opposed to idle time, waiting, or inefficient routing. It measures how effectively labor capacity is converted into completed stops.

Calculated as active delivery time divided by total paid shift time, often broken down further into idle time, drive time, and stop time. Retailers and LSPs track this per driver, per route, or fleet-wide to identify where capacity goes unused.

Driver utilization is one of the largest levers on delivery cost, since labor is typically the single biggest expense in last-mile operations. Low utilization means a business is paying for capacity it isn't using, regardless of how efficient any individual delivery route looks on paper.

Poor utilization inflates cost per delivery even when routes appear well-planned, since idle time is paid time with no output. It also limits how much volume a fleet can absorb without adding headcount, forcing costlier fixes, like overtime or additional hires, that a better-utilized fleet wouldn't need.

Viewed through risk, cancellation rate measures how often the delivery network itself fails to cover accepted demand, distinct from cancellations caused by pricing or inventory issues upstream. It isolates carrier coverage gaps, capacity shortfalls, and network fragility as the specific cause of the canceled order.

Calculated the same way as the revenue-side metric, canceled orders divided by total orders, but filtered specifically to carrier-driven and capacity-driven causes rather than merchant-initiated ones. Retailers and LSPs track this segment separately and often spike it during peak-season stress tests.

This lens shows whether a network can hold up under real demand, not just whether it can generate demand. Rising carrier-caused cancellations expose capacity and coverage gaps before they become visible anywhere else. 

A network with high risk-driven cancellations is fragile when volume matters most during peak periods, promotions, or unexpected demand spikes. Left unaddressed, it creates costly last-minute capacity fixes or forces retailers to absorb the reputational cost of failing to deliver at scale.