CONTEXT:
In a rapidly evolving oncology landscape characterized by a surge in approved therapies and intensifying competition, pharmaceutical companies face the significant challenge of effectively identifying and engaging niche patient segments. With narrow drug labels and small patient populations inherent to various cancer types, the task of finding the right patients at the right time has become increasingly complex. To address this, leading companies are adopting advanced analytics capabilities to predict key events along the oncology patient journey. These capabilities enable sales and marketing teams to dynamically target oncologists likely to treat eligible patients, enhance patient support programs by predicting therapy drop-offs, and proactively support patient retention.
Our client prioritized the development of predictive and artificial intelligence (AI) capabilities, advanced its utilization of real-world data (RWD), and established a robust framework to optimize decision-making. The ultimate objective for its Insights and Analytics team is to empower the sales force with these advanced tools and capabilities.
CHALLENGE:
When the product was launched, it was recognized as a breakthrough treatment for platinum-resistant ovarian cancer patients who tested positive for the folate receptor biomarker. A critical objective for the team managing product's market strategy was to develop an advanced application that integrated patient attributes with available treatment options for healthcare providers.
The challenge was to strategically enhance market penetration in the earlier-line setting by leveraging data-driven insights to guide the sales and marketing teams on the next best actions to take in their outreach. This included identifying and engaging suitable healthcare providers to maximize its impact in its optimal therapeutic use.
APPROACH:
To expand product's reach in the early-line setting, a data integration strategy consolidated patient claims and HCP profiles, enabling precise engagement and targeted outreach. A predictive alerts model was then developed to identify on-label opportunities by analyzing treatment patterns. Integrated with engagement systems, the model provided actionable insights to sales teams for proactive HCP engagement. Continuous learning and feedback loops ensured ongoing improvements, empowering teams with a dynamic, data-driven approach to strengthen the drug's position in the early-line setting.
IMPACT:
Our data-driven approach to market penetration demonstrated significant impact, particularly in targeting earlier line settings. By leveraging advanced predictive modeling and feature engineering, we enabled sales teams to act proactively, equipping them with bi-weekly alerts that identified high-potential patients and their associated healthcare providers (HCPs).
Key outcomes include:
- Improved Patient Transition Rates:
HCPs receiving these alerts demonstrated a 2 to 3 times higher likelihood of having at least one patient transition to the next treatment line compared to the baseline of the overall HCP target list. - Enhanced Pre-Engagement Planning:
The solution provided deeper insights into HCPs and patient journeys, enabling customer-facing roles to engage with greater confidence and deliver more impactful interactions. - Broader Organizational Interest:
The success of this project has spurred interest across the portfolio, with plans to extend these capabilities to other claims data assets and brands, amplifying the solution’s scalability and value.
This initiative has not only positioned the product effectively within the intended patient population but also set the stage for a more data-informed, agile approach to market penetration strategies across the portfolio.