Case study: How statistical analysis revealed the most effective segmentation solution for an established product in multiple indications

The challenge
Our client has an established product for multiple indications. They wanted to create a HCP segmentation typing tool which could be implemented in-field to help classify physicians across two different therapeutic areas. There was rationale to suggest that because the two therapeutic areas are pathologically different, then two separate typing tools would be required. However, it was also argued that a one segment solution would be most suitable as physicians fundamentally have the same attitudes, irrespective of the disease they are treating.

statistical analysis revealed the most effective segmentation -case study Aug 2017
statistical analysis revealed the most effective segmentation -case study Aug 2017

The challenge
Our client has an established product for multiple indications. They wanted to create a HCP segmentation typing tool which could be implemented in-field to help classify physicians across two different therapeutic areas. There was rationale to suggest that because the two therapeutic areas are pathologically different, then two separate typing tools would be required. However, it was also argued that a one segment solution would be most suitable as physicians fundamentally have the same attitudes, irrespective of the disease they are treating.

Additionally, our client would be able to implement a single segmentation tool into its core business much more efficiently than two separate tools. The challenge was to therefore decide whether a one tool or two tool solution would be most suitable.

The solution
We interviewed an extremely robust global, quantitative sample and then worked with our statistician to apply and test a multi-level segmentation model. The first level of the model segmented our sample using all input variables, based on physician attitudes, behaviour and characteristics, whether indication specific or not. This is the standard segmentation approach, but we then needed to ensure that the physicians we classified in the same segment, thought and behaved similarly to one another towards the different indications. It is important to note that we did not expect individual physician perceptions and behaviour towards the different indications to be the same, but we did expect all physicians from a particular segment to have similar approaches to treating the two different indications. If there was significant differentiation within individual segments based on how they thought about and treated different indications, then we would know that a single segmentation tool would not be feasible.

The outputs
Our model showed that the relative differences in response across the different indications is similar for ALL segments and independent of segment membership; there was little or no evidence of differences with regards to indication by segment. This lack of statistical significance identified that a combined indication solution was the most appropriate approach – within each individual segment the relative difference in the way physicians think and behave towards indications is similar. The client has rolled out the global segmentation within its business, including adoption from the sales force to help understand individual physician needs and improve these sales interactions. 

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