For decades, biopharmaceutical commercial teams have built their go-to-market strategies on a remarkably simple premise: find out who wrote the most prescriptions last quarter, and go see those people. That logic gave us deciling, the practice of sorting physicians into ten volume-based tiers and directing field resources toward the top. It persists because it is fast, familiar, and easy to operationalize. It is also no longer good enough for the market pharma now operates in.

Deciling still has a role in commercial planning, but that it no longer captures enough of the real growth opportunity available in today's market. Commercial teams need a more dynamic way to determine not just who mattered last quarter, but who is most valuable today and into the future.

A model built for a different era

The commercial operating model that most pharma companies are running today was designed for a different era. It was built around blockbusters, stable customer bases, and a relatively straightforward relationship between reach and revenue. You found the high-volume physicians, you sent your reps to see them, and the math mostly worked.

That era is ending. The shift toward specialty drugs and biologics has complicated the picture considerably. Outside of GLP-1s, genuine blockbusters have become rare. Product launch windows are compressing. Gross-to-net is getting squeezed. Health systems and purchasing groups are consolidating, shifting more formulary decisions away from individual physicians and into committee rooms. Channel disruption, including the collapse of major pharmacy chains, is restructuring how patients access medication. Consumer and patient influence on treatment decisions has grown. And the regulatory environment, particularly now, changes faster than any commercial plan can accommodate.

Pharma companies are being asked to perform with greater precision under conditions of greater uncertainty, using commercial infrastructure that was not built for either.

Deciling fit the old model well. It fits the current one considerably less well.

When 60 percent isn't enough

Here is what the data actually shows when you examine how well decile-based targeting performs: you are capturing somewhere around 60 percent of the opportunity. The remaining 40 percent is invisible to you because your model does not look for it: physicians with genuine prescribing propensity, patients who could benefit from your therapy, decisions that could go your way with the right engagement at the right moment.

A blind spot that big is not something you optimize your way around. It has to be addressed at the model level.

It goes deeper than what deciling misses. Static segmentation drives every competitor toward the same visible targets. In most therapeutic categories, competing commercial teams are using similar prescription data and similar segmentation logic, which means they converge on the same high-decile physicians. Those physicians absorb enormous promotional investment and return relatively little. The marginal impact of one more rep visit to an already-saturated, competitor-loyal physician is close to zero. Everyone knows this. The behavior continues anyway.

Meanwhile, the physician who has three patients who would qualify for your therapy, who has shown signals of openness to switching, and who has not been called on in six months sits below the threshold of visibility in a decile-driven model. That is where real opportunity is frequently lost.

It does not matter what you say as a pharma commercial team if you are saying it to the wrong customer. The field activity is real, the spend is real, the rep hours are real. But if the targeting is off, none of it compounds into anything. You are pouring resources into a funnel with a hole in the bottom and calling the water level a strategy.

Looking in the wrong direction

The core limitation of deciling is direction. Not sensitivity, not granularity. The model looks the wrong way.

Deciling is backward-looking. It tells you who prescribed the most, which is a useful measure of historical value. What it cannot do is reliably indicate who is most likely to prescribe next, who is newly responsive to engagement, or where the next point of growth is emerging in real time.

Dynamic scoring models flip that. Rather than asking who prescribed the most last quarter, they ask who is most likely to drive growth next quarter, and they answer that question continuously, incorporating behavioral signals, engagement patterns, patient population data, and promotional responsiveness alongside historical volume.

This changes what field teams are doing on a daily basis. Instead of calling a ranked list, they are operating against a live picture of where the opportunity actually is. A physician who had three qualifying patients admitted to their practice last week looks different than they did two weeks ago. That information exists. Static deciling cannot see it.

There is also the question of what happens downstream. When the segmentation is wrong, everything built on top of it inherits the same distortion, channel allocation, message sequencing, rep call prioritization, and marketing spend. Getting more precise about who to engage is not just a targeting improvement. It is the foundation the rest of the commercial model has to stand on. Fix the who, and the downstream gets more efficient almost automatically.

What better looks like

One top 20 pharma’s commercial transformation over the past two years is a useful reference point. What drove their go-to-market modernization was not a new therapy or a different channel strategy. It was a decision to get more sophisticated about how data and analytics inform execution, to move from static segmentation to something closer to real-time commercial intelligence. The results have shaped their investment priorities for the next three to five years, spanning sales modernization, medical modernization, and digital and data capability-building across the organization.

The companies making this shift are not simply refining targeting. They are redesigning how commercial decisions get made. Their field teams spend more time with physicians who are genuinely positioned to move and less time logging activity against customers whose behavior is unlikely to change. Their commercial leaders gain a clearer picture of what is actually driving impact, rather than trying to infer it from activity metrics that tell you what happened but not why.

The organizations that do not make this shift will keep optimizing a model built for a market that no longer exists in the same form.

Beyond static segmentation

Deciling still serves a purpose. For broad planning and historical visibility, it remains useful.

But today's commercial environment requires more than structure. It requires speed, timing, and the ability to recognize opportunity as it emerges rather than after it has already materialized in prescription data.

Static segmentation can still inform planning. It can no longer be the system that governs execution.

The market has changed. The operating model has to change with it. The teams that make that shift first will not just target more efficiently. They will compete at a fundamentally different level of precision, and the gap between them and the ones still running last quarter's list will keep widening.