Most beat plans are built to minimise travel. That is the wrong thing to optimise, and it is why the plan stops being followed by month three.
A beat plan assigns each outlet to a salesman and a day, on a repeating cycle. Stated that way it sounds like a scheduling problem, which is why it usually gets handed to whoever is good with spreadsheets and solved as one.
It is not really a scheduling problem. It is a promise, and it works in both directions.
The salesman knows where to be on Tuesday. More importantly, the retailer knows someone will come on Tuesday — which means they can wait until Tuesday to order instead of calling around, and they can hold less stock because replenishment is predictable. A beat that runs to schedule lets every retailer on it carry less cover, which is a genuine benefit to them and a reason to keep buying from you.
Break the schedule and you break that. A retailer who cannot predict your visit orders defensively: more stock, less often, or from whoever showed up.
Open most beat plans and you find outlets grouped by geography, arranged to minimise travel. It is the obvious optimisation and it is the wrong objective function.
Consider two outlets on the same street. One sells through a normal order in six days. The other takes a month. Grouping them by distance puts them on the same beat at the same frequency. Whichever frequency you pick is wrong for one of them: weekly wastes calls on the slow outlet, monthly stocks out the fast one every three weeks.
Distance is a constraint to optimise within a day, after you have decided who needs visiting that day. Lead with distance and you have optimised fuel at the cost of orders, which is a bad trade in every category where the margin per carton exceeds the cost of driving an extra kilometre. That is most of them.
The right visit interval for an outlet is a function of how fast it clears a normal order, not how big it is.
This is the same logic as a reorder point, applied to a visit schedule instead of a stock level. If an outlet sells 5 cartons a day and a comfortable order is 30 cartons, it has six days of cover, and a visit interval longer than six days means guaranteed stockouts between calls. The full mechanics of cover and buffers are in reorder point and safety stock in practice, and the Reorder Point Calculator does the arithmetic.
Sorting outlets by days of cover typically produces three bands:
That last band matters more than it looks. A physical visit to an outlet that orders once a month is an expensive way to collect one order. Handling those on a message, and spending the recovered time on the fast band, is usually the single biggest improvement available to an existing beat — and it is a natural fit for the kind of ordering flow described in WhatsApp automation workflows that retailers reply to.
Numbers to re-run with your own, not a benchmark.
Say a territory has 180 outlets and one salesman working a six-day week.
Step one — band them by cover. Suppose 40 outlets need weekly visits, 80 need fortnightly, and 60 need monthly.
Step two — count the calls. Over a six-day cycle: 40 weekly outlets is 40 calls a week. 80 fortnightly is 40 a week. 60 monthly is about 15 a week. That is 95 calls a week, or roughly 16 a day across six days.
Step three — check that against reality. Sixteen productive calls a day is demanding but not unusual in a dense urban beat. In a spread-out semi-urban territory it is not achievable, and the plan has to change: move the monthly band to remote ordering, or the territory needs splitting. Deciding this now is the entire point of the exercise. A plan that requires 24 calls a day will be abandoned in week two, and then you have no plan.
Step four — only now, optimise travel. Within each day, sequence the assigned outlets to minimise distance. This is where geography belongs — as the last step, not the first.
Step five — publish the day to the retailer. Tell each outlet which day their visit falls on. This is the step that gets skipped, and it is the one that converts a schedule into a promise the retailer can plan around.
A beat plan is built against a snapshot: these outlets, these volumes, this territory. All three change continuously.
Outlets open and close. A shop changes hands and the new owner buys differently. A fast outlet slows when a competitor opens opposite. A slow outlet doubles when the neighbourhood builds up. None of this shows up in the plan, because the plan was a one-time exercise.
After two or three quarters the plan describes a market that no longer exists. The salesman, who does know the market, starts deviating for sensible reasons. Once deviation is normal, adherence stops being measurable, and the plan is decoration.
The fix is a scheduled revision, not a better initial plan. Re-band the outlets quarterly against the last quarter of order data — which is straightforward if you are already tracking cover per outlet, and impossible if you are only tracking dispatch totals.
Two numbers, and the second one matters more.
Adherence — what share of planned calls actually happened on the planned day. Below about four-fifths, the plan is aspirational rather than real, and the usual cause is that it was overloaded at step three.
Strike rate — what share of calls produced an order. This is the one that tells you whether the frequency bands are right. A high adherence with a falling strike rate means you are visiting on schedule but too often: the outlets have not sold through yet, and the calls are being spent on outlets that had nothing to order. Move those outlets to a longer interval and redeploy the calls.
A beat plan that hits both is doing the only job it has: putting a salesman in front of a retailer at roughly the moment that retailer needs to reorder. Everything else in the plan is bookkeeping. The wider commercial argument for where that recovered time should go is in growth tactics for distributors.
The questions that come up when a distributor rebuilds a beat plan.
A beat plan assigns every retail outlet to a specific salesman on a specific day of a repeating cycle, usually weekly or fortnightly. Its purpose is to make coverage predictable in both directions: the salesman knows where to be, and the retailer knows when someone will come, which is what lets the retailer plan their own ordering around the visit.
Visit frequency should follow how fast the outlet sells through a normal order, not how large the outlet is. An outlet that clears its stock in six days needs a weekly visit or it will stock out between calls; one that takes a month to clear the same order gains nothing from weekly visits beyond a relationship. Size and frequency correlate, but it is the sell-through rate that determines the right interval.
Usually because the plan was built once against a snapshot of outlets and never revised, while the territory kept changing — outlets opened, closed, changed hands, changed volume. Within a couple of quarters the plan describes a market that no longer exists, so the salesman starts deviating sensibly, and once deviation is normal the plan is no longer a plan.
Distance is a constraint, not the objective. A route that saves twenty minutes of travel but visits a fast-moving outlet a week late costs an order, which is worth far more than the fuel saved. Build the plan around the frequency each outlet needs, then optimise travel within each day once those frequencies are fixed.
The FlowKartAI team builds WhatsApp-native ordering for Indian B2B distributors and the kirana stores they serve. We write about distribution economics, GST compliance, and the practical side of putting AI in front of retailers who have never opened an app.
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