How often should you visit each customer on a van sales route?

A snack and bakery distributor running six vans out of one depot built its routes the way most do: every store on a route got visited once a week, same day, same order. It was easy to plan, easy to explain to new reps, and easy to print on a stop sheet. For three years nobody questioned it, because the routes hit their numbers and the trucks came back empty most days.

What the weekly cycle actually did was average two very different problems into invisibility. A handful of convenience stores near a college campus sold through their entire shelf allocation by Wednesday and spent Thursday and Friday selling nothing, because the shelf was bare and the rep wasn't due back until Monday. At the other end, a cluster of low-traffic corner stores took the same weekly visit, restocked two cases, and sat mostly full the whole week — the rep walked in, wrote a small order out of habit, and walked out, burning fifteen minutes that could have gone to a store that actually needed it.

The cost hides inside a route total that looks fine

Neither problem showed up as an obvious number. The campus stores' lost sales never appeared anywhere, because a stockout doesn't generate a return or a complaint — the shopper just buys a competitor's chips instead, and the store owner doesn't call to report a phantom sale that never happened. The overserved corner stores didn't cost anything extra in inventory; they cost time, and time on a route is the one resource that never shows up on an invoice. The route's weekly revenue looked stable for months, because the losses on one end and the waste on the other cancelled each other out in the aggregate number the manager actually watched.

It surfaced sideways. A new rep taking over the campus stretch for a week during vacation coverage mentioned, almost in passing, that two of his stops were "always out of the good stuff by the time I get there." The regular rep had stopped mentioning it because nothing ever changed when he did. Around the same time, a route audit — done for an unrelated reason, checking van stock reconciliation — turned up that three corner stores had reordered the identical two SKUs, two cases each, for eleven straight weeks. Same product, same quantity, every single visit. Nobody had looked at a single store's order pattern in isolation before; the route was always managed as one number.

Why a fixed cycle is the wrong tool from the start

A weekly cycle is a scheduling default, not a decision — it's what you get when a route is built around what's easy to plan rather than what each account actually needs. High-velocity stores need visits matched to how fast they sell through, which for some accounts is twice a week and for a seasonal peak might be three times. Low-velocity stores need the opposite: a longer cycle, or a phone check-in instead of a truck visit, freeing the time for accounts where a visit produces an order worth writing. Treating every stop as interchangeable is the same mistake as pricing every customer the same regardless of volume — it feels fair and uniform, and it quietly overserves your worst accounts at the expense of your best ones.

The reason most distributors don't fix this earlier isn't that the idea is hard — segmenting customers by volume is not a new concept — it's that doing it by hand means someone has to pull order history per store, rank it, and manually reassign route days, and that work never survives contact with a Monday morning. It gets planned once during a slow week and drifts back to the old cycle within a quarter, because nothing forces it to stay current as accounts grow or shrink.

The turning point: letting order velocity set the cycle, not the calendar

The distributor's fix started with pulling twelve weeks of order history by store, not by route — sales per store per week, sorted, not averaged into a route total. The campus stores stood out immediately: consistent sellouts mid-week, every week, for months. The quiet corner stores stood out just as clearly: flat, small, identical orders that hadn't moved regardless of season or promotion.

They re-tiered the route on that data instead of on habit. The campus stores moved to twice-weekly visits, timed around the actual sellout pattern instead of the old Monday default. The flattest corner stores moved to a biweekly visit, with a phone call in the off week to catch anything unusual — a spoiled batch, a sudden order, a complaint — without spending drive time on a store that hadn't changed its order in eleven weeks. The freed time went to two mid-tier stores that had been growing quietly and could support a second weekly visit once someone actually looked at their numbers instead of their spot on the printed stop sheet.

The follow-through mattered as much as the initial split: they set a recheck every quarter, because a store's tier isn't permanent — the campus stores' sellout pattern would shift over summer break, and a corner store on a biweekly cycle could start growing and earn a weekly visit back. Without that recheck, the new cycle just becomes tomorrow's stale default.

Practical takeaway: setting visit frequency by account, not by calendar

• Pull order history per store, not per route, over at least eight to twelve weeks — route-level totals hide both stockouts and overservice.
• Flag sellout patterns, not just stockout complaints. A store that sells through before the next visit rarely calls to say so; look for orders that consistently max out available stock.
• Identify flat, identical repeat orders. A store reordering the same quantity of the same SKUs visit after visit is a candidate for a longer cycle or a phone check-in.
• Reassign the freed time, don't just cut it. Time saved from an overserved account should go to a growing one, not disappear from the route.
• Recheck tiers quarterly. Seasonal shifts and account growth make yesterday's frequency wrong within a few months if nobody revisits it.
• Keep a lightweight touch on downgraded accounts — a call or a shorter visit — so a longer cycle doesn't read to the customer as being dropped.

iotoms tracks order history per store automatically from every van visit, so spotting a sellout pattern or a flat repeat order doesn't require a special audit — it's a report against data the field app is already logging.