From Sales to Signals: The New Approach to HCP Targeting

TL;DR: For insured prescription products, targeting works best when sales data is paired with claims that show real clinical opportunity and intent to treat. Supplemental signals like Open Payments add context on access and competitor effort, giving you a clearer, more confident target list.

Every year, commercial teams circle back to the same question. Who should we target?

On a slide, it feels simple. Identify the right clinicians. Prioritize them. Deploy the field force.

In reality, targeting becomes one of the most sensitive decisions in the brand plan. Leadership must buy into the method. Field teams need to trust that the list makes sense. And when a product has been on the market for years, any change can spark resistance.

At its core, targeting is about getting the right information to the right clinicians at the right moment in the patient journey. You are not chasing volume. You are trying to reach the HCPs who see the patients your therapy is built for. No company has the field capacity to cover everyone, so choosing the correct HCPs becomes critical.

To make those choices, companies use different sources. Some stick to sales data. Others blend in patient claims, clinical profiles, engagement history, competitive intel from research, or Open Payments activity. Today, we are spoiled for data. That was not always the case.

The Era When Sales Data Ruled Everything

Before large-scale claims datasets existed, sales data was the entire playbook. TRx, NRx, market share, growth rates.

This worked in the blockbuster era. Huge primary care populations. Predictable treatment habits. A world where historically high-revenue clinicians carried the business.

The problem was what sales data could not show. It could not reveal which patients were being treated. It could not show whether a clinician managed early or late-line disease. It could not show switches, add-ons, or anything about the patient journey.

In specialty medicine, where every patient is different, this was a major limitation.

Leadership still loved sales data. It was familiar, clean, and easy to defend. Big numbers meant lean in. Smaller numbers meant intervene. Sales answered one question. Where is the revenue?

It could not answer the question that matters more today. Where is the opportunity?

Then Claims Data Changed the Game

When anonymized, longitudinal claims became widely available, everything shifted.

Claims revealed not just what clinicians prescribed but why, when, and for whom. Suddenly you could see:

• Diagnoses

• New starts

• Discontinuations

• Switches

• Add-ons

• Comorbidities

• Payer friction

• Competitor movement

Targeting moved from a static sales decile list to a clearer picture of clinical reality. You could identify who actually treated the types of patients aligned with your indication. You could spot early adopters and understand where patients were moving in or out of your brand.

Claims did not replace sales. It reframed sales.

Sales shows what happened. Claims shows what could happen next.

Why the Debate Still Continues

If claims unlocks deeper insight, why do some companies still anchor targeting in sales? Three reasons. Inertia. Cost. Capability.

Claims requires engineering, analytics, and interpretation. And without those, teams can misread the data and chase noise instead of opportunity.

Meanwhile, sales data feels clean and comforting. It covers your own brand well. KPIs are straightforward. Market share is easy to track. But the biggest gap remains. Competitor visibility is incomplete.

Claims also has limitations. It reflects payer visibility. It reflects historically treated patients. And like any backward-looking data, it can pull teams toward old patterns instead of emerging ones.

This is why the strongest commercial models do not rely on a single source. They blend them.

The Blended Targeting Model

Modern targeting works best when multiple sources are used together.

• Sales confirms reality.

• Claims reveals opportunity.

• Open Payments shows competitive intent and influence.

• Engagement history shows preferences and responsiveness, used carefully to avoid anchoring on comfortable habits.

This approach shifts targeting away from chasing high-volume writers and toward clinicians who treat the right patients at the right time.

Where Claims Fall Short: OTC and Cash-Pay Products

For insured prescription products, claims are the foundation of modern targeting.

But categories like ED medications, weight-loss compounds, hair-loss treatments, and certain compounded therapies do not behave this way. Many are cash-pay. Claims data is thin or nonexistent. OTC products follow the same pattern.

These markets operate more like consumer goods. Targeting relies more on retail analytics, search behavior, digital signals, and consumer activation. A different playbook worth its own discussion.

The Bottom Line

Sales data built the old model. Claims data built the new one.

The best commercial teams use both. One shows the present. The other shows the future.

As healthcare becomes more specialized and patient journeys more complex, targeting will continue to move toward integrated, patient-centered insight.

Done well, targeting becomes less about generating lists and more about clarity. Seeing the clinicians who truly shape outcomes and giving them the information they need to make the best decisions.