Predictive Analytics
Using machine learning (ML) algorithms to identify non-prescribing physicians likely to adopt a drug.
The adoption of drugs by physicians is driven by multiple factors, including patient benefits. The ability to communicate these health benefits of drugs is crucial to encourage prescriptions. Additionally, physician and patient demographics, macroeconomic factors, and regulatory influences play an essential role in determining drug adoption. Being able to predict the likelihood of non-prescribers adopting target drugs on being exposed to a set of promotional activities can help pharma companies channel their sales efforts in the right direction. Sophisticated Machine Learning (ML) algorithms can help identify high-probability adopters based on past trends, increasing sales conversions, prescription volume and benefitting patient populations with timely and appropriate treatment.
The identification of 200+ physicians, including 16 high-probability adopters
A $4 Mn potential addition to the drug’s long term prescription revenue.
Prioritizing physicians for higher sales force conversion rate.
An opportunity to expand market share in the highly competitive lung oncology market.
Read this illustration to learn about Axtria’s ML algorithm to identify high-probability drug adopters.
Download the free case study here.
Contact us at insights@axtria.com with any questions.
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