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Case Studies in Patient Incentives

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New Product Launch

A Case Study for Securing the Optimal Offer for a New Product Launch

Situation:

A leading pharma company was preparing for a major product launch and wanted to implement a productive patient incentive program as part of their launch plan.  They had a lot of disparate data from multiple sources including market research studies, market share and managed care estimates.

The company was missing several key pieces of data which were needed to do an effective analysis to determine the optimal patient incentive offer. The brand team had several major objectives:
  • Increase trial beyond initial expectations
  • Understand both their average co-pay and the potential incremental volume that the various offer options might deliver
Our Solution:
  • Alpha 1C produced a unique “Patient Incentive Optimal Offer Model” for the client  - specific to their product launch
  • Surrogate data from Alpha 1C’s proprietary database was reviewed with the client and incorporated in place of any missing data.
  • We provided the client unique functionality and flexibility which tailored the model specifically to a launch brand. This flexibility was needed because the inputs frequently changed as new data became available.
  • We examined over 12 unique offers, focusing on the ones that produced the most trial for the most cost effective spend.

Result:

  • Alpha 1C utilized the client specific information combined with key performance data from their co-pay vendor and unique data from the Alpha 1C database to produce a custom simulation model.
  • The new model allowed the brand team to enter and change many of the needed inputs.  This changed the estimates on the fly, ensuring the brand had constant feedback utilizing the most recent data.
  • The resulting model showed that some of the brand group’s initial assumptions were not correct.  For example, the model highlighted that the brand’s assumed average patient co-pay estimate was underestimated by $20. This was a critical finding because the brand had been overestimating their market share and underestimating the cost of their co-pay program as a result.
  • Two main types of offers were tested: “free trial” and “zero co-pay” offers. The brand group was able to see the impact of these seemingly similar offers and was surprised by how different the results actually were.
  • The brand team utilized the model to find their “optimal offer” while also refining  their market share, incentive program budget and overall P&L estimates.
Timing:
  • The Alpha 1C finished the entire analysis in less than two weeks!