Your HCP segments are already out of date

A physician segment built six months ago may still look precise in a presentation. That does not mean it still reflects how healthcare professionals behave today.

Surveys and focus groups remain useful for understanding motivations. The problem starts when they become the main foundation for HCP segmentation. They capture a moment in time, while prescribing patterns, channel preferences, access, affiliations, and market conditions continue to change.

Why static HCP segmentation creates moving targets

Traditional segmentation is usually an intensive project. Commercial teams commission research, define segments, and map HCPs. Repeating that process across brands and territories takes time, so refreshes may happen annually or around a major launch.

Between refreshes, the market moves. An HCP may engage with different content, change their preferred channel, join another practice, develop a new affiliation, or become more or less accessible. A segment based largely on stated preferences may miss those changes.

Campaigns can then target yesterday’s priorities. Field teams question central recommendations, regional teams create their own lists, and functions work from different versions of the customer.

Fragmented data makes the problem harder

Even a better model will fail if the same HCP appears differently across CRM, master-data, consent-management, and analytics systems. Records may use different identifiers, specialty names, affiliations, or communication preferences. A dashboard can show one profile while a field system shows another.

Veeva’s commercial biopharma research surveyed 116 senior life sciences leaders across the US, Europe, and Asia Pacific. It found that 72% of companies spend up to 100 days matching HCP data across sources, while 84% spend up to 100 days on HCP mapping processes alone. That is time spent preparing information before analysis can create value.

When a recommendation conflicts with what field teams know, they may return to spreadsheets or personal judgment. The cost goes beyond analyst hours: duplicated activity, slower decisions, poorly allocated resources, and technology that people avoid using.

Put a number on the manual work

There is no universal figure for the cost of HCP segmentation. Data availability, brand structure, and internal processes vary too widely. A simple planning calculation is more useful.

ILLUSTRATIVE EXAMPLE  Twelve territories × four quarterly refreshes × 20 preparation hours equals 960 hours per year. If a governed data pipeline removes half of that preparation, it frees 480 hours. At an illustrative blended cost of $100 per hour, that equals $48,000 annually.

This is not a promised saving. It excludes software, data licensing, integration, and governance costs, as well as the value of reaching the right HCP sooner. Its purpose is to create a baseline that Commercial Operations can test.

A concrete US example

Consider a national oncology team using the same four HCP segments across its Northeast, Midwest, and Western territories. The model was created before the current campaign and relies heavily on research responses, specialty, and historic prescription indicators.

Since then, CRM interactions and permitted digital engagement data suggest that behavior has shifted. In one territory, previously accessible HCPs respond less often to field activity. In another, a group outside the priority segment repeatedly engages with scientific content. Regional teams can see the change, but the official segments have not caught up.

An AI-enabled solution links and deduplicates permitted records, establishes one governed HCP view, and groups HCPs using permitted and governed signals such as specialty, location, affiliations, past interactions, channel engagement, and appropriately aggregated market or prescription indicators. The national team gains a consistent foundation while retaining necessary brand and territory rules.

Regional teams receive updated segments with an explanation of what changed. A commercial reviewer can approve them before use. Refreshes can run on a schedule or when a launch, access change, or market event requires one.

Build the data foundation before the model

AI cannot repair unclear ownership or poor-quality data by itself. Teams must define permitted sources, field ownership, and how duplicates, conflicting values, and missing information are handled.

For US programs, define which HCP data may be used and who can access it. Professional contact data is not automatically protected health information. The HIPAA Privacy Rule protects identifiable health information handled by covered entities and their business associates. Patient-related data should not be a default input; check source rights and applicable federal or state rules before use.

The software should show why an HCP belongs to a segment, which sources informed the decision, and when data was refreshed. Missing or conflicting information should go to a person, especially when segments influence high-value activity.

From static segments to refreshable decisions

The value extends beyond faster clustering. Commercial teams spend less time reconciling records and more time planning campaigns. Analytics teams reuse a governed foundation. Field teams receive priorities that reflect recent behavior, while leaders can compare decisions across brands and territories.

The value of a connected foundation is already visible in industry examples. In one global customer-data program at Astellas, the time from adding a new customer and capturing consent to multichannel engagement fell from nearly five days to less than five hours, according to Veeva.

Start with one brand, one territory, and one decision the segmentation must improve. Give commercial teams a current, controlled view they can trust while keeping judgment with people.

HCP segmentation is one of five connected AI workflows for pharma Commercial and Omnichannel teams. Explore all five use cases.

Sources

Veeva Systems, The State of Data, Analytics, and AI in Commercial Biopharma (2025)

Veeva Systems, Navigating Customer Data Fragmentation in Biopharma

US Department of Health and Human Services, The HIPAA Privacy Rule