Ask most field reps how they plan their day and the answer is usually some variation of: they look at their list of visits, they think about where things are, and they figure out a rough order that makes geographic sense. Then they drive.
This process takes between twenty and forty-five minutes for a typical rep planning a full day. And the route they end up with is almost never optimal. Not because reps are bad at planning, but because route optimisation is a genuinely hard combinatorial problem that human intuition handles poorly.
The travelling salesman problem, briefly
Route optimisation is a variant of the travelling salesman problem: given a set of locations, find the shortest route that visits all of them. Even with ten stops, there are over 3.6 million possible orderings. The human brain is not built to evaluate 3.6 million options before 8am.
Algorithms designed for this specific problem can find solutions within a few percent of optimal in milliseconds. The gap between a human-planned route and an algorithm-planned route across a full working week translates, in our data, to between 90 minutes and two hours of avoidable drive time per day per rep.
What smart routing accounts for
Basic route optimisation just minimises distance. A good route planner for field sales needs to account for more than that.
Time windows. Customers have availability constraints. A route that visits everyone in the optimal geographic order is useless if it arrives at the key account at the wrong time. Real optimisation respects time windows for each stop.
Visit priority. Not all stops are equal. A high-priority renewal visit might justify a geographic detour. A brief check-in visit should be slotted opportunistically on the way to somewhere else.
Dwell time. Different visits take different amounts of time. A route plan that assumes every stop is thirty minutes and doesn't account for a two-hour strategic review will fall apart by lunchtime.
Traffic patterns. A route that looks efficient on a static map can be painful in real traffic. Good optimisation uses live or historical traffic data, not just straight-line distance.
The "nearby opportunities" feature most reps don't use
One of the highest-leverage features in SafiTrack's routing is the nearby opportunities view. While a rep is completing a visit, the app surfaces prospect accounts within a defined radius that match their ideal customer profile and haven't been visited recently.
This turns dead time (the drive between appointments, the window when a meeting ends early) into prospecting time. Reps in our highest-performing cohort average 1.4 more customer interactions per day than reps who plan manually, and a significant portion of that difference comes from opportunistic visits that wouldn't have happened without the nearby prompt.
The change management piece
A lot of reps push back on algorithmic routing at first. The feeling of having a computer rearrange your carefully planned day is, understandably, not immediately welcome. The resistance usually fades within about a week when the time savings become tangible. But it's worth acknowledging upfront rather than trying to mandate adoption.
The framing that works best: the algorithm handles the logistics so you can focus on the relationships. You still decide who to visit and when the customer has a genuine time constraint. The routing handles the sequencing puzzle that's not really a high-value use of your brain.
What two hours actually buys
Two hours per rep per day is not a small number. Across a team of ten reps over a working year, that's roughly 4,800 hours. The equivalent of two and a half full-time employees, recovered from windshield time and redirected toward actual selling. Even if only a fraction of that time converts to incremental customer visits, the pipeline impact is material.
The reps who resist it most in the beginning are often the ones who end up using it most enthusiastically once they've seen their numbers at the end of a month.