Field sales route planning from an exported list
Auspector ·
A field team does not work a list of rows; it works a sequence of stops. An exported list already carries what a route needs — an address, a district and coordinates on effectively every record — so the route can be built in a spreadsheet: group by district, cluster coordinates by rounding them to a ~100-metre grid, cut the result by how many visits a day holds, or load the CSV into whichever route planning tool you already use.
From rows to stops
A list of forty new businesses reads as forty rows; a field rep reads it as forty doors, some of them five minutes apart. Route planning is the step that turns the export into an order of visits. Nothing exotic is required: the two columns that do the work — district and coordinates — are on effectively every record, and the rest is sorting and cutting.
Method 1: group by district
The simplest method needs no tooling at all. Sort the export by district, count rows per district, and you have a density ranking of where the new businesses actually are. Assign whole districts to days rather than scattering them: a district with a handful of rows becomes a half-day; a dense one gets a full day plus a lunchtime boundary. District grouping also matches how teams hand over work — “the northern districts are yours this week” is a sentence everyone understands. The narrower choice of area is the second step of building a lead list.
Method 2: cluster by rounded coordinates
Districts are coarse; coordinates are precise, and rounding them makes them clusterable. Round both latitude and longitude to three decimals — a 0.001° step is roughly a hundred metres, about a city block — and records that round to the same pair belong to the same cell. Count rows per cell and the geography of the list appears: dense cells are a morning’s work each, sparse cells are travel between stops. Two practical notes: sort cells by count, not by name, and keep the unrounded original column untouched — the rounded pair is a grouping key, not a location.
Method 3: cut by daily visit capacity
Capacity decides the week. Take the number of visits one rep realistically completes in a day — your number, from your own history — and cut the ranked cells into day-sized batches: fill a day from the densest cells before touching the next, because a stop five minutes away costs a fraction of a stop across town. The list order is irrelevant to geography; never visit in export order. A batch that overflows goes to the next day with its own area rather than spilling across the whole week.
Method 4: hand the CSV to a route planner
If your team already uses a route planning tool, the export slots straight in: the CSV carries one row per business with its coordinates and address, which is exactly what such tools ingest. Load the file, confirm the address columns, set the day capacity, and let the tool order the stops. When the results come back with visits marked, re-import against the stable record identifier so each business keeps its history instead of appearing twice. No specific tool is required — whichever one you already pay for will do.
Keeping the route alive
A route built once is stale within days, because new businesses arrive every day. Put the underlying search on watch: new matches arrive by email each morning, and the fresh rows slot into next week’s districts through the same rounding trick. The discipline that makes field work compound is small: same filters, same watch, same grid, one route rebuilt weekly.