Three hours waiting. Two minutes talking.

That’s the reality of detailing high-volume physicians. The ratio feels absurd, yet reps do it repeatedly because “that’s where the volume is.” I struggle with this - not because the maths is wrong (it’s often right), but because the maths is incomplete.

The volume trap

Traditional call planning prioritises high-prescribing physicians. More scripts, more potential, more calls. The logic is clean. The reality is messier.

High-volume physicians are busy. Their waiting rooms are packed. Access is compressed. Every pharma company in the market is competing for those two minutes, which means the three-hour investment produces two minutes of attention shared with ten other companies trying to say the same thing.

Volume-based targeting reliably produces volume-based crowding.

What the research on detailing elasticity shows

Academic research on pharmaceutical detailing - most notably work by Puneet Manchanda - has consistently found that the relationship between detailing and prescribing is non-linear.

Some physicians are highly responsive. A few calls produce meaningful prescribing shifts. Others are barely responsive: many calls, minimal change. The difference isn’t about how much they currently prescribe. It’s about elasticity - how much prescribing behaviour shifts in response to sales activity.

A medium-volume physician with high elasticity can be worth more than a high-volume physician with low elasticity. The medium-volume physician will actually change behaviour. The high-volume physician won’t, regardless of how long you wait.

Responsiveness-based segmentation

Instead of asking “who prescribes the most?”, ask “who changes prescribing in response to our activity?”

That single reframe changes the call plan.

High responders (regardless of current volume): increase frequency. They respond to presence - give them more.

Low responders (regardless of current volume): decrease frequency or change approach. More of the same won’t move the needle. Reallocate the time or find a genuinely different angle.

Moderate responders: hold current frequency. Responsive enough to justify attention, not responsive enough to justify intensity.

The waiting room calculus shifts. Three hours for two minutes with a high responder might be worth every minute of it. Three hours for two minutes with a low responder almost never is.

How to identify who responds

Responsiveness can be measured looking backwards and predicted looking forwards.

Retrospective: Look at historical detailing data alongside prescribing trends. Did increased call frequency correlate with increased scripts - and for which physicians specifically?

Prospective: Test call frequency variations on a defined sample. Increase calls to one group, hold steady on another. Measure. The physicians who shift behaviour are your high responders.

Proxy indicators: Certain physician characteristics correlate with responsiveness - newer physicians often respond more than deeply established ones; those who attend educational events tend to be more open to new information. These are directional indicators, not guarantees, but they’re a reasonable starting point when historical data is thin.

The uncomfortable truth

Responsiveness-based segmentation means calling less on some high-volume physicians.

This feels wrong. Your biggest prescribers are getting less attention - how does that make sense?

It makes sense because attention isn’t the same as impact. A high-volume, low-elasticity physician has already decided what to prescribe. Your waiting room time won’t change that. Meanwhile, a medium-volume, high-elasticity physician is actively making decisions you can influence. That influence is worth more than presence that produces nothing.

What I actually struggle with

I don’t struggle with the logic. I struggle with the implementation.

Telling a rep to reduce calls on their biggest customer contradicts decades of sales training. It requires genuine trust in data that most organisations haven’t had time to build - and the data is sometimes wrong. A “low responder” can be a relationship that hasn’t been properly established yet. Sometimes persistence does pay off. These are real counterarguments, not just resistance.

The waiting room calculation isn’t simple. But it’s worth questioning - honestly, with the numbers in front of you.

Every three hours in a waiting room is three hours not spent somewhere else. The opportunity cost is real. The question is whether we’re measuring it. If we’re not measuring responsiveness, we’re not managing territories. We’re scheduling visits and hoping.

Hope isn’t a strategy. Even when it comes with three hours of waiting time.