How Capital S Consulting transformed a pharmaceutical company's commercial operations by connecting internal CRM data with third-party patient signals, turning static territory lists into a dynamic, continuously improving lead generation engine.
The Challenge
A specialty pharmaceutical manufacturer had years of patient and provider data stored in their pharmaceutical CRM. The information they tracked internally gave them a foundation to work from, but it did not tell the full story. Commercial teams operated from static territory lists built on healthcare provider specialty and practice size, with no visibility into which providers were actively treating patients with the target condition.
The data needed to solve this problem existed, but not internally. Lab results, claims data, and diagnostic codes held meaningful patient signals across third-party sources that were never connected to commercial outreach. Without that external picture, territory managers had no systematic way to identify the right patients at the right time.
Key pain points:
- Static territory lists built only on provider specialty and practice size, with no behavioral signals
- Valuable third-party lab and claims data never linked to commercial workflows
- No systematic method to surface the right patients at the right moment
The Solution
Capital S Consulting designed and built a predictive analytics scoring model that connected the manufacturer's internal CRM data with multiple third-party sources, including lab results and claims data, using tokenization technology to track patient journeys across platforms while protecting patient identity.
The four-step process:
- Connect - Link internal CRM with third-party lab and claims data through secure data integration methods
- Automate - Replace manual review of thousands of diagnostic codes with real-time scoring
- Deliver - Generate qualified leads tied to prescribing healthcare providers in near real-time
- Refine - Continuously improve accuracy as territory managers report outcomes
The system automated a qualification process that previously required manual review of thousands of diagnostic codes and test results. When external data indicated relevant diagnostic patterns, the system automatically generated leads tied to appropriate prescribing healthcare providers, delivered in near real-time rather than through weekly or monthly data reviews.
The model was built to improve over time. As territory managers worked leads and reported outcomes, the system refined its understanding of which combinations of internal and external signals most accurately predicted patient need, surfacing early-stage opportunities that internal data alone would never have identified.
The Results
Over three years in operation, the system fundamentally changed how the commercial team operated. Performance has continued to improve as the model learns. Territory managers spend less time on broad outreach and more time on targeted, patient-specific conversations. The manufacturer moved from a static, internally-limited view of their market to a dynamic, continuously improving system that gets more accurate the more it is used.
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