What a beat actually is
A beat (also called a PJP, Permanent Journey Plan) is a fixed, repeating route an RSO follows -- a set list of retailers, visited on a set schedule. The idea is simple: instead of a salesperson deciding each day who to see, the route is planned in advance so every retailer gets a predictable, regular visit.
Setting visit frequency
Not every retailer needs the same frequency. A high-volume outlet might genuinely need a weekly visit to avoid running out of fast-moving stock; a smaller one might only need a fortnightly or monthly call. Frequency is usually set per beat -- Weekly, Fortnightly, Every 2 Months -- with an optional week pattern (e.g. 1st and 3rd week of the month) for anything less than weekly, so the plan stays predictable even at a lower cadence.
Getting frequency wrong in either direction has a real cost: too frequent wastes RSO time on outlets that don't need it; too infrequent loses sales to a competitor who shows up more often.
What "beat productivity" measures
Beat productivity isn't just "did the RSO show up." It's usually a combination of: how many of the planned retailers were actually visited, how many of those visits resulted in an order, and what that order was worth relative to the retailer's average. A beat with 100% visit coverage but a low order-conversion rate is a different problem than one with poor coverage in the first place -- the fix is completely different (coaching on the call vs. fixing the route itself).
The averaging trap
A genuinely common mistake: when looking at productivity over a custom date range (say, a quarter), averaging each month's percentage together. This quietly distorts the real number -- a month with very few planned visits but a high percentage gets the same weight as a month with thousands of visits and a slightly lower percentage. The correct approach is to sum the underlying counts across the whole range first (total visits planned, total completed) and calculate the percentage once, from those sums. The difference sounds small but can swing a reported number by several percentage points on an uneven month.
Why territory-level view matters
A single company-wide productivity number hides which zones or states are actually underperforming. Filtering by territory -- zone, state, down to an individual RSO -- is what turns "productivity is at 78% this quarter" into something actionable: which specific territory is dragging that number down, and whether it's a coverage problem or a conversion problem.
What it takes to track this properly
You need the plan (which retailers, which beat, what frequency) and the actuals (which visits actually happened, and what came of them) in the same system, recalculated from current data rather than a once-a-month manual rollup that's already stale by the time anyone reads it.
WorklikeGenie's beat plan and productivity reports are recalculated on demand, filterable by territory, and -- for a custom date range -- sum the underlying counts and re-derive the percentage from those sums, specifically to avoid the averaging trap above.