Sales reach-out platform for representatives for personalized physician targeting

Trigger Program Generation System

The Client

Biopharmaceutical Company

One of the world’s largest biopharmaceutical giants, employing thousands of employees and providing effective medicines to millions was looking for a machine learning solution to build a sales reach-out platform for representatives for personalized physician targeting.

Akaike’s edge-cloud agnostic solutions offer great flexibility.

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Executive Summary

Industry Overview


The healthcare industry is a sector that offers products and services to treat patients with curative, preventative, rehabilitative, or palliative care. The current pandemic, which continues to consume the majority of resources and attention in healthcare systems, presents new problems that the global healthcare sector is overcoming. They keep improving the working conditions for their employees and changing what, how, and where work is done. They are also quickly expanding the availability of virtual health services for patients and forming alliances to create and obtain the necessary drugs, vaccinations, and supplies. In addition, health information technology offers several prospects for enhancing and altering healthcare, including lowering human error rates, enhancing clinical outcomes, facilitating care coordination, increasing practice efficacy, and monitoring data over time. Physical visits to healthcare institutions during the pandemic were decreased, and AI-based technology has assisted sales representatives in operating at peak efficiency.
Business Challenge

Customer Experience

One of the most important tactics for any business is to build a lean sales and marketing process with the optimized usage of time and resources. The Pharma customer wanted us to build a sales reach-out platform for their representatives for personalized physician targeting. The model would not only help in understanding physicians’ beliefs and perspectives but also assist in estimating physicians’ patient potential.

The Akaike Edge

Inbuilt libraries, DL models with transfer learning capabilities

Experienced ML and DL Ops teams

Efficient Deployment 0%
Integration 0%
Ongoing Maintenance 0%


Inbuilt libraries, DL models with transfer learning capabilities
Step 1.

Collect the relevant data and extract the useful information

Physicians’ data such as marketing, treatments, claims, and demographics data, and patients’ medical data such as pharmacy data, insurance data, and treatment history were collected. Also, data from publications, clinical trials, and social media were collected to extract dominant physicians’ behaviors such as receptiveness, experimental nature, opinion changes, and brand loyalty.

Step 2.

Various models were trained to estimate a patient’s potential by a physician

Models such as Disease Staging and Patient Journey models, Patient Eligibility models by brand, and Patient Initiation models were trained for the use case. Firstly, the models were used to find the patient potential by a physician, following that, marketing efforts were focused on the doctors for whom the model scored strongly.

Step 3.

physician selection and identification of patients' potential

Generated timely trigger alerts for sales reps and the driving factors to summarize the trigger messages at the physician level. Also, forecasts were made for new drug prescriptions and new patient starts on the physician level.

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