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Conjoint demonstration - technical notes

X25_1_scissors789895_19201.jpg Our conjoint demonstration is a simplified example of a conjoint exercise to illustrate how choices can be turned into estimates of value. The calculations it uses to estimate the "utility" values, or part-worths associated with the choices being made (as shown on the bars) are simplified.

This article outlines some of the technical notes and issues around the demonstration. There are actually several "flavours" of conjoint analysis depending on the task at hand and we have a simple Excel-based spreadsheet to show the general principles of calculating utilities.


The conjoint demonstration on this site is a very basic version of conjoint analysis simply to show the main principles of conjoint analysis.

The form of conjoint analysis used relies on a pairwise comparison (ie Pizza A v Pizza B) rather than selecting from more than two products, using just two attributes at a time. This is known as a limited profile set, and is similar to an Adaptive Conjoint Analysis (ACA) based on software from Sawtooth Software.

Other forms of conjoint analysis include full-profile and choice-based conjoint analysis which use different way of presenting the description of the products and allow for more products to be seen at one time allowing display formats like virtual shelfs or e-commerce mock ups. Our Cxoice Survey Technologies allows us to build custom conjoint designs to best meet the needs of the research.

In making the estimates, the demonstration is heavily reliant on consistent choices being made, as utility values (the values shown in the graphs) are simplistically calculated based on the last choice made, rather than a probabilistic average of all answers. Consequently inconsistent choices can produce strong variations in value scores. Full conjoint analysis uses more robust statistical techniques to estimate utilities across a sample as a whole.

Thirdly, because the demonstration only has 3 attributes (topping, size and base type) no special schema is necessary to choose which attributes and levels are used to make up each choice task. A core challenge in using conjoint analysis is in minimizing the number of choices per respondent, yet still getting good estimates of utilities. This is most commonly achieved using what are known as orthogonal designs to ensure balance as to which levels are shown and in what combinations.

In a conjoint analysis with 5 attributes each of 4 levels for instance there would be 4x4x4x4x4 possible combinations of products to show to a respondent. By using an appropriate orthogonal design a minimal set of profiles is needed (typically 12-24) by choosing the profile designs so that levels are shown in combination in such a way that the effect of each level can be estimated without needing to show all combinations. This is a key part of any large scale experimental design seeking to examine underlying factors.

Adaptive forms of conjoint analysis, ask pre-questions, such as ranking, to help in selecting pairs to ensure the maximum information is obtained from the smallest number of choices. However, with so few levels here, there is no need to pre-order the levels for each attribute as there is in say ACA.

For further information about how the demonstration works, or the technical issues involved in carry out conjoint analysis, contact us at

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