By Sagar Shankaran, Founder of CallSphere
Peak is 33 operating days of staffing risk. How 2026 demand models handle lumpy courier history, price stops by density, and where they still get it wrong.
Key takeaways
From the Monday before Thanksgiving to the first week of January is roughly thirty-three operating days, and for most independent last-mile operations those days carry a quarter of the annual stop count and nearly all of the year's staffing risk. Get them right and you fund the first quarter. Get them wrong in either direction — too many flex vans idling, or too few and a wave of rescue routes — and you spend February explaining a completion-rate dip to an account manager.
Nobody in this trade forecasts peak with a model. They forecast it with a whiteboard, last year's numbers, and a phone call to the shipper's transportation planner who tells you volume will be "up about fifteen." Then Cyber Monday's injection lands on the Tuesday and Wednesday instead of spreading, and the whiteboard is fiction by 8 a.m.
Demand forecasting for a courier means predicting stop counts by day and by zone far enough ahead to commit drivers and vans, using history that is messy, seasonal and full of one-off weeks — which is precisely the shape classical spreadsheets handled worst. Roughly 48% of manufacturers now run demand forecasting on modern models; on the pricing side about 72% of retail and e-commerce companies do. Couriers, sitting between those two, mostly still guess.
Every operator knows these and none of them survive a monthly average. The Monday spike: weekend orders plus Friday's carryover, reliably the heaviest day of the week, and the day your part-time bench is thinnest. The post-holiday snap-back: the first operating day after any Monday holiday runs 30 to 40 percent over a normal day and gets staffed like a normal day. Weather: an ice event does not reduce your volume, it moves it to the next two days, compounded. And the January cliff, where volume falls off a table while your flex drivers are still on the schedule and the returns wave gives back only part of it.
Layer on the account-specific patterns nobody writes down. Lab and pharmacy routes surge Monday morning because of weekend collection backlogs and again through flu season. Floral accounts have two days a year that matter. B2B document work dies over the holidays and comes back hard in tax season. A per-day average across all of that is a number that describes no actual day.
The reason forecasting sat unused in this trade was not stubbornness. It was that the tools available needed clean, regular history — and courier history is anything but. Three years of stop counts full of holidays, an account you won in March and lost in September, a two-week period where a shipper's own system was down, a zone you stopped covering. Feed that to a classical forecast and it produces something confidently smooth and useless.
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flowchart TD
A["Three years of stop counts by day and zone"] --> E["Weekly peak plan"]
B["Shipper injection forecasts"] --> E
C["Weather and holiday calendar"] --> E
D["Driver availability and time-off requests"] --> E
E --> F{"Do we need flex vans that day?"}
F -->|"Yes"| G["Book flex drivers five days out"]
F -->|"No"| H["Run the base fleet, keep two drivers on call"]
The 2026 tools handle the mess. You hand over the export from your routing software, the holiday calendar, the shipper's injection forecasts and your own notes on which weeks were abnormal and why, and you get a daily number by zone with a range around it. The working assistants — Claude Cowork since January, ChatGPT Work since 9 July — will take a goal like "build me a day-by-day driver plan for the six weeks from 16 November, flag the days where we need flex vans" and come back with the spreadsheet, having read the exports themselves. Your operations manager reviews it. Nobody writes a formula.
The other half of this is what you charge. Most independent couriers still quote a new B2B account off a spreadsheet built years ago: a per-stop rate, a minimum, a fuel surcharge tied to the published diesel index. What that spreadsheet almost never contains is density — stops per mile — which is the single variable that decides whether the account makes money. A sixty-stop-a-day account clustered in four ZIP codes and a sixty-stop-a-day account spread across a county are not the same business at the same rate, and everyone knows it and quotes them the same anyway.
The pricing models now common in retail do the same job here: take your actual completed stops, cost them by drive time and dwell rather than by straight-line distance, and tell you what a prospective account's stop list would really cost to serve before you sign. That turns a renewal conversation from "we need a rate increase" into "here are your stops per mile, here is the dwell time at your consignees' docks, here is what the residential mix did to attempts per delivery." Shippers argue with percentages. They argue much less with their own stop data.
Illustration for a 22-van operation across the 33-day peak window. Costs are placeholders; put your own settlement and rental numbers in.
| Cost of one flex van-day (driver settlement, fuel, rental, insurance) | $310 |
| Cost of one rescue route when you are short | $420 |
| Today: 4 extra vans carried across all 33 days | 132 van-days = $40,920 |
| Today: rescue routes still needed on the spike days | 18 = $7,560 |
| Today, total | $48,480 |
| Forecast-led: flex vans only on the days that need them | 49 van-days = $15,190 |
| Forecast-led: rescue routes on days the forecast ran short | 7 = $2,940 |
| Forecast-led, total | $18,130 |
| Difference across one peak | about $30,350 |
Two caveats that keep this honest. The forecast still ran short seven times — that is what the rescue line is for, and any plan that shows zero misses is lying to you. And the saving only exists if you can actually flex: a bench of qualified part-time drivers you can call five days out, plus vans available on short-term rental. Without the bench, a better forecast just tells you more precisely how short you are going to be.
It will miss anything that has never happened before in your data. A shipper who moves a distribution center forty miles, a new account onboarding mid-peak, a competitor failing in your market and dumping volume on you in week two. Those are conversations, not calculations, and they come from your account managers — which is why the forecast should be reviewed weekly with the people who talk to shippers, not filed once in October.
It will also be wrong about people. The model does not know that two of your best drivers have already asked for the week of Christmas, or that the depot cannot physically stage more than nineteen vans at 6 a.m. without the loading order falling apart. Capacity constraints on the ground are the operations manager's call and always will be.
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And do not let a pricing model set a rate on its own for an account you want strategically. Sometimes you take a thin route because it anchors a zone you are trying to build density in. The model cannot see that; you can.
Two full peaks is enough to be useful, three is better. If you only have one, start anyway but treat the output as a discussion document and lean harder on the shipper's injection forecast. Missing months are fine; the current tools handle gaps without you cleaning them first.
You need one export per year of completed stops with date, zone or ZIP, and stop type. Onfleet, DispatchTrack, Route4Me and OptimoRoute all produce that. The assistant reads the raw export — you do not need to reformat it into anything.
It will tell you what serving them would cost, given their stop list and your drive times, which is the number you have never actually had. What you charge on top is still a judgment about the market and the relationship.
Then that is a variable worth tracking. Record what they told you and what arrived, for two or three seasons, and the model will learn how far off they run and in which direction. That single record is often more valuable than the forecast itself.
Export the last three years of completed stops by day and by ZIP from your routing software. Add a two-column list of the days that were abnormal and why — ice storm, shipper outage, account won or lost. Hand both to a working assistant and ask for stop counts by day for the next six weeks with a range around each. Compare its numbers for last November against what actually happened. That comparison, not a sales deck, tells you whether to trust it for this peak.
One thing a forecast will not do is answer the phones the extra volume brings. Peak weeks turn every delivery question into a call, and that is where CallSphere fits: voice and chat agents that pick up the status, reschedule and delivery-window calls around the clock so your dispatcher can keep releasing routes at 6 a.m.
Written by
Sagar Shankaran· Founder, CallSphere
LinkedInSagar Shankaran is the founder of CallSphere, where he builds production AI voice and chat agents deployed across healthcare, hospitality, real estate, and home services. He writes about agentic AI, LLM engineering, and shipping voice agents that handle real calls in production.
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