The Successful Launch and Diffusion of New Therapies - Innovation and Marketing in the Pharmaceutical Industry - page 200

Fig. 7.2 Setting up a
conjoint study

Define characteris cs or a ributes of the new
product(for example for a treatment: price level,
dosage level, form, effec veness, side effects, …)

Define realis c levels for all chosen a ributes

Construct scenarios or alterna ves (combina ons of
a ribute levels) based on an experimental design

Choose the response type (choice between scenarios,
ranking or ra ng of scenarios)

1982 ). Recently, web-based methods, together with efficient algorithms and more
powerful computational capabilities, have yielded new interactive conjoint methods
that generate more accurate knowledge with far fewer questions compared with
traditional methods (Dahan and Hauser 2002 ; Hauser and Toubia 2005 ; Toubia
et al. 2003 , 2004 ).
By executing a conjoint analysis, companies observe the importance of the dif-
ferent attributes to physicians or patients as well as the preference for specific levels
of these attributes. The complex payment structure of the pharmaceutical industry
complicates the ability to assess market sensitivity to the price of a new pharmaceu-
tical drug. In many cases, one of the attributes in a conjoint study is price (or, the
co-pay of the patient), as incorporating this attribute allows companies to make
statements about patients' or payers' willingness to pay or physicians' willingness
to prescribe. In a conjoint study, every product—which is a combination of attribute
levels—gets assigned a value based on assessments of attribute-level preferences.
By letting consumers evaluate different products, conjoint studies enable inferences
to be made with regard to the expected market share of different products.
Furthermore, conjoint analysis also allows companies to discern different segments
in the markets. Segments are groups of respondents that attach similar importance
to attributes and share a preference for specific attribute levels. This type of infor-
mation has proven to be very valuable when developing a new product and forecast-
ing the demand for that product (Dolan 1990 ). To forecast the demand for a new
product, the results of the conjoint analysis are incorporated into a model based on
mathematical representations of each consumer's preferences alongside the specific
attribute composition of the product. An aggregate-level diffusion or sales model
can then be used to aggregate these individual forecasts into an overall prediction of
the new product's sales (Gupta et al. 1999 ; Lee et al. 2006 ; Roberts et al. 2005 ;
Urban et al. 1990 ).
The complex structure of demand in the case of pharmaceutical drugs forces
pharmaceutical firms to identify patients' needs, either through direct means or
through the mediation of physicians. Accordingly, prelaunch sales forecasts for new