Adoption research determines the drivers of success and market potential for a new product (or a reconfigured product) to support forecasting and launch strategy. In pharmaceutical markets, these studies also inform the design of clinical trials.

In order to inform these hard decisions, it is paramount that model estimation allow for a realistic view of the marketplace. There are many techniques and processes we use to develop and estimate models, as well as analyze results, for this purpose:

Hierarchical Bayes
We use HB to estimate almost all our models. This approach has many advantages, including higher accuracy, ability to provide individual-level utilities, and being designed to deal with smaller samples.

Team Discussions
We believe these discussions are critical for assessing the use of the data and the selection of the dependent variable(s). At this stage, we might find, for example, a need to simultaneously estimate the impact of a new product for patients and prescriptions.

Attribute Importances and Sensitivities
We calculate these measures dynamically to more accurately reflect market changes.

Source of Share Assessment
We provide SoS to help determine the degree to which the market expands due to the new product introduction and/or the impact on existing products in the marketplace.

Calibration
We calibrate to integrate real-world market information for greater precision.

Adoption Curves
We provide ACs as part of our reporting to map preference share more realistically to the marketplace.