A delivery route runs on time through its first three stops. But the fourth stop has a fourth-floor walkup and a security gate requiring a buzz-in. Both were unaccounted for. The route optimization engine called for 35 minutes and the job took 80.
Every stop that follows bends. By the end of the shift, dispatchers rescheduled three deliveries, the driver ran two hours past their window, and a customer who took the day off work got a phone call instead of a sofa.
Sound familiar?
The routing engine produced a feasible day. The variable that broke the route sat one level upstream from the algorithm itself, in the assumption the system made about how long a single stop would take. This kind of breakdown plays out across last-mile operations every day and rarely traces back to its root cause: the input feeding the routing engine. The retailers who attack this challenge at the input level can streamline their operations and secure even more customer lifetime value.
The inefficiency snowball no algorithm can fix
AI has helped many businesses improve their routing optimization, ETA accuracy, and visibility. Survey data from 150 enterprise executives in The 2026 Last-Mile Performance Outlook report found that AI adoption stands at 74% in reporting and visibility and 72% in routing, which were the highest adoption figures.
However, the intelligence layer feeding all three AI layers has not improved. As a primary example, service time is still configured as a flat average per service type in most enterprise operations: A furniture delivery is 45 minutes and an appliance install is 90. The routing engine’s ETAs are optimized against static numbers for each stop in most operations, regardless of what makes each stop different.
When the inputs are wrong by 45 minutes per stop, no amount of routing intelligence will produce a plan that holds. Operations teams know this. They account for delays or they absorb the variance upstream by padding service times. Padding can reduce failed routes but at the cost of locking fleet capacity and suppressing available delivery slots at checkout. It builds inefficiency into every part of the last-mile (from slots at checkout to vehicle capacity to ETAs) that finance can’t trace and operations can’t defend.
Failed attempts compound the cost. General industry consensus places the average cost of a failed standard delivery at $17.20-$17.78 per attempt. Big-and-bulky operations run materially higher given two-person crews, specialized vehicles, and rescheduling overhead that resets the entire stop economics.
Labor makes the math harder. Industry consensus places labor at 50-60% of total last-mile delivery cost. Every overtime hour built into a padded schedule, every reschedule triggered by a cascading delay, every additional driver hired to absorb variance the system could have predicted, runs against margin that is already structurally compressed.
This snowball effect of inaccurate inputs reflect in how often executives said they fall short of key performance targets:
- 64% miss on cost-per-delivery
- 52% miss on customer satisfaction
- 51% miss on ETA accuracy
Routing engines today are good at their jobs when variables are consistent and businesses are quick to invest in them. They just aren’t built to absorb so much variance based on static inputs and businesses are already feeling the pain.
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High-value customers feel failures first
Bad service time inputs often damage the goodwill and retention of the highest-value customers.
Power shoppers, defined as consumers who place 11 or more orders per month, drive a disproportionate share of customer lifetime value. They buy more often than any other segment, hold delivery expectations, and walk away faster when the experience falls short.
"Last-mile delivery is now a customer-lifetime-value problem, not just a cost problem,“ Bringg's Chief Product and Technology Officer Yishay Schwerd wrote on the cascade of operational decisions that shape last-mile outcomes. “And that starts with the front door experience. Consumers decide where to shop based on delivery, pay more for reliability, and reward retailers who get it right with repeat purchases.”
The power shopper cohort is where that argument concentrates because losing one of these high-value shoppers is a significant revenue hit.
The 2026 report of 1,000+ consumer insights, Power Shoppers Are the Biggest Loyalty Risk, shows the gap clearly. Across every dimension of delivery that depends on the planning layer, power shoppers index significantly above regular shoppers:
- 75% of power shoppers rate on-time arrival as important, compared to 64% of regular shoppers.
- 44% rate flexible scheduling as extremely important, compared to 31% of regular shoppers.
- 30% prioritize same-day or next-day delivery, nearly double the regular-shopper rate of 17%.
Each of these factors traces back to a planning layer that depends on accurate service time inputs to function. When the inputs are wrong, the layer that delivers reliability and flexibility to consumers cannot do its job.
The commercial impact compounds when service time inputs miss. 29% of power shoppers name the absence of delivery window choice as a reason for cart abandonment, and roughly a quarter (27%) cite missed delivery windows as a top concern. When operations pad schedules to absorb service time variance, power shoppers miss out on slots that disappear. When routes break from incorrect time estimates, they are the least forgiving.
The broader consumer pattern sits in the same direction. 51% of general consumers will abandon a purchase if a retailer cannot provide on-time delivery estimates at checkout, and 55% will abandon a brand entirely after a negative delivery experience. The planning inputs that determine route reliability also determine whether the customer comes back.
The planning inputs that determine route reliability also determine whether the customer comes back.
What big-and-bulky and services merchants can do now
For big-and-bulky retailers, service businesses, and installation-heavy operators, the planning gap hits hardest. Closing the gap doesn't mean redeploying the planning stack. It starts with treating service time as a variable to be managed rather than a configuration to be set and forgotten. Five measurable moves stand out, and most operations can make them without procuring new technology.
Measure the variance in installs and complex deliveries
- Compare 90 days of configured service times against actual recorded durations
- Break the data down by install type and service complexity
- Prioritize the service types with the widest gaps
Audit the data behind every prediction
- Capture stop-level timestamps and completion data: install status, haul-away, callbacks, customer sign-off
- Track crew-level data, not just driver-level Install teams introduce variables single-driver models miss
- Enforce the check-in and completion protocols already built into the crew's mobile app
Track service time as its own metric
- Add service time variance to the KPI stack alongside on-time rates and first-time completion
- Set a baseline per service type, not as a single operational average
- Surface the metric in dispatcher dashboards and weekly reviews
Disaggregate the averages by stop variables
- Segment data by install type, building access, crew configuration, and SKU complexity
- Identify the segments with the largest deviation: multi-floor walkups, complex installs, haul-away stops, etc.
- Treat those segments as the priority list for predictive work
Connect planning inputs to commercial KPIs
- Combine service time variance with slot availability, first-time completion, and labor cost in one view
- Bring planning, eCommerce, CX, and finance into the same conversation about the same input
- Treat service time as a cross-functional metric the entire commercial team owns
The next last-mile frontier
The next wave of effective last-mile planning is rooted in better inputs. From there, service time is treated as a prediction rather than an assumption. Per-merchant models are trained on each operation's own historical data and weighted to the specific variables that actually drive variance: SKU complexity, building access, installation type, crew configuration. Models that retrain as patterns shift and adjust for seasonal changes or operational shifts without human-defined rules. Predictions feed the routing engine as accurate inputs, not fixed configurations.
The downstream effect is less tension across last-mile performance. Routes can be built on predictions that reflect the reality of stop variation. Padding will shrink. The slots that used to hide in safety buffers will surface at checkout, where the customers who drive the most revenue actually see them. Overtime will fall and reschedule rates will drop. The snowball of broken routes won’t build momentum.
Cost goes down and customer value goes up, all from the same operation.
Conversations about AI and greater efficiency in the last-mile of late have largely been focused on algorithms. The next phase of the discussion belongs to the inputs that feed them. The companies that close that gap will be those that point intelligence at the variables that determine whether a route holds, and whether the customer returns.
Gauge the impact of time on site estimations with this calculator
