MD Analytics offers advanced capabilities in designing discrete choice experiments using Conjoint Analysis and MaxDiff in healthcare and pharmaceutical marketing research.
Among our favorite methodologies is Adaptive Choice-Based Conjoint, see below for our reasons why. To learn more about our experience with MaxDiff, read this article on Improved Capabilities with MaxDiff Exercises that we recently wrote about.
Choice-Based Conjoint (CBC) Analysis
While conducting derived driver analyses (such as regression) can be helpful to determine the impact of drug-related attributes, one of the most accurate ways to assess attribute importance is to use a choice-based conjoint analysis (CBC). CBC is a trade-off analysis used to measure the perceived values/preferences of specific product attributes and to assess the demand for a particular product in light of its various attributes. It is often used to forecast what the likely acceptance of a product would be and resulting impact on market share preference. It is one of the most reliable driver analyses available, for the following reasons:
- It forces respondents to make trade-offs in their consideration set, which better reflects how decisions are made in a real-life setting, and helps determine what physicians truly value; and
- It removes the bias inherently present in Likert rating scales.
Adaptive-CBC is another version of choice-based conjoint analysis that allows the user to obtain reliable results even with the smallest sample sizes, thanks to the adaptive questionnaire design and a statistical model called Hierarchical Bayes. In an adaptive-CBC study, the respondents goes through an additional step of developing their preferred drug profile, by choosing their optimal configuration of attributes and levels. They are then provided with an initial set of potential product concepts to screen for “must have” and “unacceptable” attributes. The software then uses this information to build a series of relevant product profiles for use in the conjoint exercise.
The unique “choose your own adventure” style of approach, helps to reduce the overall number of infinite possibilities while providing more data points than otherwise available from CBC alone, thereby contributing to its ability to provide more powerful results from small sample sizes.
Presenting the Results
- The analysis yields a hierarchy of attributes in terms of how important they are in driving treatment selection. It also shows the relative importance of each level within each attribute.
- Conjoint data can also be utilized for perceptual maps or for the creation of preference-based segments, which would determine if a section of the market is currently untapped.
- Conjoint also comes with a Market Simulator, an interactive online tool that predicts changes in physicians’ share of preference based on the attribute characteristics of each product. This tool can be extremely useful for helping to determine the potential impact of a new product, or a switch to a different messaging strategy.