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Case Studies in Patient Incentives

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Predicting RM Program Opt-Ins

A Case Study for Predicting Opt-Ins to a Patient Relationship Marketing Program

Situation:

A leading pharma company had just implemented a new patient relationship marketing program and wanted to ensure that their new patient incentive offer provided their patients a good discount while also driving patients to sign up for their RM program.

The brand team wanted to understand the value of getting a patient into their RM program and then find the best offer  to drive the best mix of patients into their RM program.

Our Solution:
  • Alpha 1C produced a unique analysis leveraging both our history in setting up RM programs and our unique “Patient Incentive Optimal Offer Model”.
  • We examined the data and determined values for both the average patient and the patient who also enrolled in an RM program.
  • We then examined over 10 unique offers and simulated the impact on not only trial and adherence but also on opt-in rates for their RM program.
  • We provided customized functionality which allowed the client to adjust each of these variables.

Result:

  • Alpha 1C utilized the client specific information, combined it with key performance data from their co-pay vendor and unique data from the Alpha 1C database to produce a custom simulation model including patient opt-in estimates .
  • The resulting model showed a side by side analysis of  many offer types including different approaches to driving opt-ins into the RM program.  For example, requiring the patient to sign up for the RM program was modeled against other options that gave greater incentives to those patients who signed up for the RM program vs. those who didn’t sign up for the program.
  • In addition, many types of offers were tested, including “graduating discounts”, “pay no more than”, “save”, “free trial”, ‘zero co-pay” and “combination offers” .  We also included their current in-market offers for comparison purposes.
  • We were able to produce a unique simulation model specific to the client’s combined brands and current market situation.
  • As in all of our models, we presented a “mini case study” on each of the scenarios showing all the KPI’s side by side.
  • The model’s flexible simulation capabilities allowed the brand team to run many more patient offer scenarios “on-the-fly” to quickly determine the impact of additional offer variations on brand results .
  • The brand team was able to utilize the analysis to find the optimal offer and program approach that provided an attractive patient discount, drove opt-ins to their RM program and delivered the best financial return.

Timing:

  • The Alpha 1C finished the entire analysis in less than two weeks!