A rare disease biologics launch across five European markets. A field force of 48 reps. A target HCP population of approximately 2,200 — but no systematic way to prioritize, sequence, or personalize engagement. AI-driven next-best-action changed that, and the launch performed above forecast in Q1.
A specialty biologics company was preparing for the commercial launch of a rare disease therapy in five European markets. The target HCP population was small — approximately 2,200 relevant specialists across hematology, immunology, and rheumatology — but highly heterogeneous in terms of prescribing behavior, institutional affiliation, and engagement preferences.
The field force of 48 medical sales representatives had no systematic way of prioritizing which HCPs to engage, in what sequence, with what message, or through which channel. Targeting was based on historical call lists, personal relationships, and territory intuition. There was no predictive layer and no feedback mechanism connecting field activity to downstream prescribing behavior.
The company had three years of medical congress attendance data, digital engagement logs from a HCP portal, and CRM records — but no integrated view of how these signals related to prescribing intent or launch readiness. The data existed. The intelligence did not.
In rare disease launches, the cost of misallocated field effort is disproportionately high. With only 2,200 relevant HCPs across five markets, every misspent call is a percentage point of potential market penetration. The absence of a prioritization model meant the field force was distributing effort roughly equally across a population of highly unequal commercial potential.
Initial analysis of the CRM data revealed that a cluster of high-influence immunologists in the Benelux region — with strong publication records and significant peer influence — had received fewer than three field contacts in the 18 months prior to launch. Territory design had systematically underserved this group. Without a data integration layer, this gap would never have been identified.
"The challenge in rare disease is not finding HCPs — it is understanding which ones are scientifically ready, institutionally influential, and channel-responsive. Those three dimensions rarely align, and intuition cannot reliably identify where they do."
Travalcon designed and deployed an AI-driven sales intelligence system built on three integrated components, each addressing a distinct layer of the targeting and engagement problem.
A data integration layer consolidated CRM records, digital engagement data (portal visits, webinar attendance, email open and click patterns), congress attendance history, and publicly available publication and affiliation data into a single behavioral profile for each of the 2,200 target specialists.
This created, for the first time, a single behavioral view of each HCP — replacing four disconnected data sources with one dynamic intelligence record updated in near real-time.
A propensity model was trained on pre-launch engagement data to score each HCP across three dimensions:
Scientific receptivity — likelihood to engage with clinical evidence based on publication history, congress attendance, and portal behavior.
Institutional influence — role in driving formulary or treatment protocol decisions within their center or network.
Channel preference — predicted responsiveness to face-to-face, digital, or peer-to-peer engagement based on historical behavior patterns.
A Next-Best-Action layer was built directly into the CRM interface used by field reps. Rather than a static call list, each rep saw a weekly prioritized view of their top HCPs with a recommended action grounded in behavioral signals and BCB Framework™ communication modules.
Recommended actions included: initial visit with specific clinical data, follow-up with patient profile evidence, invitation to peer webinar, or a digital touchpoint via the portal — each matched to the HCP's predicted channel preference and engagement stage.
All available data sources — CRM, portal analytics, congress records, publication databases — were integrated into a unified HCP intelligence layer. Data quality issues were identified and resolved in 34% of CRM records, including duplicate entries, misclassified specialties, and missing affiliation data.
The propensity model was trained on 18 months of pre-launch engagement data and validated against prescribing behavior from analogous launches in the same therapy area. The model achieved a cross-validation AUC of 0.81 against the prescribing outcome variable, indicating strong predictive power before any live launch data was available.
The Next-Best-Action layer was activated in the CRM two weeks before commercial launch. A structured enablement program trained all 48 field reps and 6 regional managers on how to interpret and act on AI recommendations — including guidance on when to override based on local knowledge, with override reasons captured for model refinement.
Regional managers used the propensity scores in weekly coaching conversations — for the first time having a shared, data-grounded language for discussing territory priorities and rep performance. The Benelux immunologist cluster was surfaced in week one and immediately prioritized for engagement.
As live prescribing data became available, the propensity model was retrained weekly — incorporating actual prescribing behavior, updated engagement signals, and rep override decisions. The model's predictive accuracy improved from an AUC of 0.81 at launch to 0.87 at week 16, reflecting the value of live outcome data in rare disease environments where early prescribers are a strong signal of future adoption patterns.
The same HCP behavioral data was shared with the Medical Affairs team to optimize speaker selection for advisory boards and peer-to-peer programs — extending the intelligence layer beyond the field force into the broader commercial model.
Results were measured at Day 90 and Week 24 post-launch, against pre-defined baselines established during the diagnostic phase and industry benchmarks for comparable rare disease launches in Europe.
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