Testing Beyond the Lab: Leveraging the Clinical Trial Approach to Enhance Decision-MakingAugust 16th, 2017 | Posted by in Life Sciences and Healthcare
The concept of clinical trials, a central component of the research and development process, is very familiar to most life sciences leaders – but what may be less familiar is how they can take this approach beyond the lab to drive value across their organizations. As the pioneers of clinical trial methodologies, pharmaceutical companies already know that test vs. control analysis is the most accurate way to measure the impact of an initiative. Today, more and more organizations are capitalizing on this existing expertise to reduce the risk of innovation and enhance their decision-making.
Just as clinical trials begin with a hypothesis about the efficacy of a treatment, commercial testing begins with an idea for a business program. By testing a given program with a subset of patients, physicians, hospitals, or markets and comparing their performance to that of a very similar group that serves as a baseline, organizations can more accurately measure and enhance their programs. This “test and learn” approach enables them to answer three key questions:
- How will a given program impact key metrics, such as the number of prescriptions written?
- How can we target the program to the patients, physicians or markets predicted to respond best?
- How can we improve the program for maximum impact?
In the pharmaceutical industry, this approach can enhance data-driven decision-making for non-personal promotion (NPP) campaigns, speaker programs, field force initiatives, direct-to-consumer (DTC) marketing and more. Using NPP campaigns as an example, companies can determine the direct impact of digital or other non-personal outreach campaigns on prescriptions written by comparing the prescribing behavior of physicians who received the campaign to that of other, similar physicians who did not. Similar to a test vs. control trial for a new drug, this approach is the only way to confidently determine which physicians prescribed because of the campaign, and which would have prescribed anyway.
This type of test vs. control analysis can reveal unique and sometimes counterintuitive findings. For example, despite a strong correlation between speaker programs and higher scripts, more rigorous analysis might reveal that attendees of speaker programs were significantly more predisposed to prescribing in the first place. As another example, one brand team for a blockbuster drug invested in NPP campaigns aimed at driving prescriptions for physicians who had low market share of their brand. While that assumption was logical, a more rigorous analysis showed that NPP actually drove the most incremental sales for physicians who had a larger market share.
Experiments like these can also help organizations isolate the impact of one channel from the impact of other investments. For instance, it is critical to isolate how NPP campaigns impact scripts when the same physicians receiving NPP are also receiving details and samples.
To confidently make data-driven decisions, all hypotheses — whether clinical or commercial — should be tested. The examples above illustrate just a few scenarios where test vs. control analysis can unlock valuable insights. As organizations across industries increasingly adopt this approach, life sciences organizations have a significant opportunity to enhance their returns by embracing experimentation in a commercial setting.
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