32 Reps, 1400 Pharmacies, Everyone Getting Equal Attention -That Was the Problem

32 reps. 1400 pharmacies. Everyone getting equal attention.

The maths was clean: 44 pharmacies per rep, visit each one monthly, move on.

The results were mediocre.

The peanut butter problem

Equal allocation feels fair. Every customer gets the same attention. Every rep has the same workload. The system is simple to manage.

It’s also guaranteed to underperform.

Not all pharmacies have the same potential. A high-volume independent in a medical precinct isn’t the same as a small rural pharmacy. Treating them identically treats reality as simpler than it is.

Peanut butter allocation spreads resources evenly. It doesn’t spread resources intelligently.

What the data revealed

When we mapped customer potential against call frequency, the pattern was stark.

High-potential pharmacies (the top 15%) were being visited monthly -same as everyone else. They could have absorbed twice the attention and converted it to growth.

Low-potential pharmacies (the bottom 30%) were being visited monthly -same as everyone else. Most of those visits produced nothing. The relationships were pleasant. The commercial impact was zero.

The middle 55% varied wildly. Some were growing and needed support. Some were stable and needed maintenance. Some were declining and needed intervention.

Everyone getting the same attention meant nobody getting the right attention.

The range-based segmentation

We rebuilt the call plan around potential, not equality.

A-tier pharmacies (top 15%): Weekly visits. These were the growth engines. More presence meant more SKU placements, more recommendations, more share.

B-tier pharmacies (next 35%): Fortnightly visits. Growing or stable accounts that needed regular but not intensive attention.

C-tier pharmacies (next 30%): Monthly visits. Maintenance accounts where presence mattered but frequency didn’t drive incremental results.

D-tier pharmacies (bottom 20%): Quarterly visits with phone support. Pleasant relationships with limited commercial impact.

Same 32 reps. Same 1400 pharmacies. Completely different allocation.

The results

Within 90 days:

28% increase in call productivity. Measured as commercial outcomes per call, not calls per day. Reps were having better conversations with more receptive customers.

41% increase in new SKU placements. The A-tier pharmacies responded to increased presence by expanding range. The attention they’d been missing was now converting to shelf space.

15% reduction in wasted travel. D-tier pharmacies clustered geographically in ways that didn’t justify monthly visits. Quarterly scheduling reduced drive time without reducing relationship quality.

Zero decrease in D-tier revenue. The pharmacies getting less attention didn’t buy less. They were never buying based on visit frequency in the first place.

The fairness question

Some reps pushed back. “It’s not fair that I have to visit some customers more than others.”

The reframe: fair isn’t equal. Fair is proportional.

A customer with R500K potential deserves more attention than a customer with R50K potential. That’s not favouritism -it’s resource allocation.

The reps who previously complained about “wasting time on dead-end pharmacies” suddenly had more time for growth opportunities. Their commission improved. Their job satisfaction improved.

Equal attention wasn’t serving them any more than it was serving the customers.

The implementation lesson

Segmentation on paper is easy. Segmentation in practice requires discipline.

Every rep will have a C-tier pharmacy they love visiting. Nice owner. Good coffee. Easy conversation. They’ll want to visit more often than the data suggests.

That’s where management matters. The call plan is a constraint, not a suggestion. Visit frequency should be defended, not negotiated.

The 28% productivity gain didn’t come from better data. It came from better discipline. The data told us what to do. The discipline made us do it.

Peanut butter is for sandwiches. Not for customer allocation.