Modelling Specialist Audiences Data

We regularly undertake analysis of small samples of specialist audiences, including Physicians, Patients with specific health conditions, Financial Professionals and Opinion Leader Audiences. Our preferred analytical frameworks work well for these types of projects, as they are particular suited to small as well as large samples.

Medical Specialists examples are given below:

  • A Pharmaceutical company wanted to understand how four groups of specialists would prefer to be communicated with and, specifically, the trade-offs between the information communicated and the channel of communication. We designed and implemented a choice experiment to understand these trade-offs and delivered an excel simulator and summary of the trade-off made by different groups.

  • A manufacturer of disease modifying therapies (DMTs) for a neurological condition wanted to understand the trade-offs that existed between different features of the treatment. They also wanted to see if different groups had different hierarchies of need. The therapy bundles presented included features such as relapse rate, method of administration, side effects, monitoring etc. A Discrete Choice Model was developed to facilitate this and four segments of patients were identified with very different decision making processes. We developed a “What-if” Simulator and interactive excel summary.

  • A Segmentation was created from a small-sample survey of specialist nurses to type them according to their working practices, attitudes and behaviours. The challenge was to develop enough granularity in the segmentation whilst at the same time working with a small sample. Our Latent Class framework was ideally suited for this type of project as it could be configured to work robustly with small samples.

Other specialist audiences:

  • Corporate Reputation among Opinion Leaders: MPs, NGOs, Journalists, and Financial Analysts. Several projects were undertaken to determine the key drivers of reputation for a number of major corporations. In several cases, segmentations were also developed to understand how views differed across these key audiences.

  • We built a panel of “Influencers” for an opinion polling company using initially a segmentation to identify a small group of consumers that lead the opinions of others and then an algorithm to identify them in future research.

  • Several studies amongst employees, who had been through employment tribunals, were commissioned to determine what combination of historical and demographic factors in their case influenced whether they received a payment award. CHAID Analysis was used.

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