Helped the customer in identifying the right set of physicians to target for their upcoming drug in the market

Built a predictive framework on potential patient cohorts to identify physicians likely of treating in the market with analog products and gain robust insights into the market to achieve boosted market launch

Upwards of brand share impact estimated

Teams consuming the results

“D Cube Analytics’ strong domain knowledge and pro-activeness in bringing new ideas to the table helped this project to evolve from a simple target list creation exercise to a predictive model framework development.

They followed a completely transparent white box approach in terms of the process and solution development by keeping us informed/involved very closely which made consumption easy for us.”


The client was on track to receive a regimen approval for multiple myeloma that was in the third phase of trials. Finding the right potential prescribers for an upcoming drug in the market is crucial for success as the whole market dynamics can be influenced by these key decision treatment makers.

For the preparation of the new approval and to understand the future behaviour of the upcoming drug, there was a need to develop the potential prescribers list by referring to the competitor analogs and gain robust insights into the market.


Explored and identified the correct market analog for the upcoming drug and tracked the treating behaviour of the prescribers.

Integrated different data sources, to find out the key treating decision-makers for the analog drug. Furthermore, a predictive framework was built to identify the potential patient groups who are treated with the analog drug but are wrongly classified due to gaps in data capture.

Various features such as – patient attributes, treatment metrics, prescriber attributes were used in building the framework.


  1. Applied extensive market knowledge in creating business rules which were used as a base rule for defining the lines of therapy and regimen combinations, and identified relevant patients for the analysis
  2. Expanded the scope to include a predictive analytics model that could classify potential patients from the larger pool of treated patients identified due to data capture issues
  3. Helped in obtaining a comprehensive target list by expanding the treating physician’s list to treatment-triggering physicians list as well, to include more expansive targeting


  1. Point towards the right pool of prescribers who would be high priority targets for the upcoming regimen
  2. Help in channelling marketing and promotional activities towards these high priority prescribers
  3. Predictive analytics outputs to overcome the gaps in data capture and arrive at the exhaustive list of prescribers
  4. Understand the treating behaviour of prescribers and key drivers in drug decision

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