Conjoint Analysis Technical Notes II

Conjoint analysis is a technique for measuring, analyzing, and predicting customers’ responses to new products and to new features of existing products. It enables companies to decompose customers’ preferences for products and services (typically provided as descriptions or visual images) into “part-worth” (or utilities) associated with each level of each attribute of the product. They can then recombine the part-worths to predict customers’ preferences for any possible combination of attribute levels, and the likely market share or revenue that a new product is likely to achieve when introduced into a market in which other competing products may already be available. They can also use conjoint analysis to determine the optimal product concept or to identify market segments that value a particular product concept highly.

There are three stages in a typical conjoint study:

(1) Design of a data collecting instrument,

(2) Collecting data from consumers, and

(3) Analyzing the data and simulating market response.

Using conjoint data for market simulations

Depending on the exact nature of the data collected, there are various options for analyzing the data and creating a part-worth function for each respondent. The simplest approach is to use dummy variable regression with ratings or rank-order data (data collection options 1 and 2 listed in the previous section).

j= a particular product or concept included in the study design;

Rij = the ratings provided by respondent i for product j; (Alternatively, the rankings could be reversed so that higher numbers represent stronger preference, and then used as if they are similar to interval-scaled ratings);

aikm = part-worth associated with the mth level (m=1, 2, 3, ..., Mk) of the kth attribute;

Mk= number of levels of attribute k;

K= number of attributes;

xjkm = dummy variables that take on the value 1 if the mth level of the kthattribute is present in product j and the value 0 otherwise; and

ij= error terms, assumed to be normal distribution with zero mean and variance equal to 2 for all i and j.

To facilitate interpretation, the aikm’s obtained from regression can be rescaled so that the least preferred level of each attribute is set to zero and the maximum preferred product combination is set to 100, producing results that are more easily interpreted. Letting a~ikm’s denote the estimated (rescaled) part-worths, the utility uij of a product j to customer i is equal to:

Note that product j can be any product that can be designed using the attributes and levels in the study, including those that were not included in the estimation of the part-worths.

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