Make Better Decisions with Brand Choice Driver Analysis


Uncover the real drivers of prescribing choices with greater confidence
Identify critical brand differentiators with deeper insights
Focus resources and strategy on the brand attributes that influence prescribing behaviour

Brand Choice Driver Analysis

In the ongoing quest to optimise patient outcomes and increase market share through effective pharmaceutical promotion, understanding what really drives healthcare professionals' (HCPs) prescription choices is crucial.

As market researchers, we can ask doctors, on a list of attributes, what attributes are most important when deciding on a product to prescribe (stated importance). They will tell you (in most cases), it’s efficacy, safety, convenience. This is important, as it tells us what matters to prescribers.

However, it does not tell us if or how these attributes differentiate the brands that prescribers can choose from. We also need to consider the hidden relationships that exist between brand attributes and prescribing choices.

Enter derived importance analysis to uncover the real drivers of prescribing choices. Using this approach, the assumed importance of an attribute is statistically estimated ("derived") from its relationship with a second measure (e.g., overall performance, liking, intention to prescribe, brand share).

Brand choice driver analysis quantifies the impact of variables on doctors' prescribing decisions and uncovers the hidden relationships between brand attributes and prescribing choices.

This reveals attributes that may be critical differentiators in the competitive landscape or necessary requirements for continued consideration by customers, enabling brands to tailor their marketing messages for maximum impact.

How we use it

Although often correlated, stated and derived importance may differ in several ways.

For example, attributes high in derived but not stated importance may be key competitive differentiators, whereas attributes high in stated importance (or both) may be critical requirements necessary for customers to continue to consider services or products.

Often in market research, derived importance analysis is conducted using common measures of attribute importance like standard regression or correlation, which are badly flawed (we won’t bore you with details of continuous data, multicollinearity, and clustering effects).

We overcome these limitations by utilising machine learning and categorical data analysis techniques, which provide robust estimates of the relative importance of drivers for sales or market share.

Importantly, in case you’re wondering, these particular techniques work very well when dealing with small sample sizes like those we’re working with in market research with specialist physicians, which increases the confidence and reliability of our insights.

How it can help you

Our approach not only allows us to rank drivers based on their importance as prescribing drivers, but also their brand discriminating power. We put more weight on statistically significant drivers which at the same time help in discriminating between brands (i.e., not just category drivers).

This empowers you to allocate resources effectively and target the right healthcare professionals with brand messaging that is aligned with your customers' key motivations.

Focusing your marketing efforts on brand features and benefits that are brand choice drivers and at the same time are likely to differentiate between brands, makes more sense than focusing only on features that are category drivers.

By leveraging the power of derived importance analysis, we help healthcare companies identify the key factors that will propel their brand forward and gain a competitive edge.

If you are not using derived importance analysis already, contact us to see how it can help your brand.