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Full profile conjoint- or choice-based trade-off studies have traditionally been limited to six attributes. Full profile studies allow for the estimation of interaction terms and generally present more realistic choices to the respondent than partial profile or self-explicated approaches. However, clients often want to test a long list of potential product features that may or may not be included in the final product, depending on the results from research. Additionally, they may be interested in complex pricing issues that require some interaction effect estimation or wish to test certain attributes such as brand and price in a “full profile’ format. Being limited to six attributes renders traditional full profile trade-off analysis useless in this situation.
The method described here has been developed and successfully applied numerous times and offers several advantages over traditional full profile conjoint and choice methods:
Introduction
Trade-off analysis is a family of methods by which respondents’ utilities for various product features (usually including price) are measured. In some cases, the utilities are measured indirectly. In this case, respondents are asked to consider alternatives and state a likelihood of purchase or preference for each alternative. As the respondent continues to make choices, a pattern begins to emerge which, through complex multiple regression (and other) techniques, can be broken down and analyzed as to the individual features that contribute most to the purchase likelihood or preference. The importance or influence contributed by the component parts, i.e., product features, are measured in relative units called “utils” or “utility weights.”
In other cases, respondents are asked to tell the interviewer directly how important various product features are to them. For example, they might be asked to rate on a scale of 1 to 100 various product features, where 1 means not at all important to their purchase decision and 100 means extremely important to their purchase decision.
Trade-off analyses produce several types of information. First, they tell us what features (and levels of features) are most valued by customers. Second, they allow us to model how likely people will be to purchase various configurations of products, the share of revenue these products will most likely receive and what role price plays in the assessment of acceptability.
There are four main types of trade-off:
Within both conjoint and discrete choice methods, there exists a further subdivision into full profile and partial profile approaches. Full profile methods use one level from each attribute in the study when defining a product configuration for respondents to rate or rank. Partial profile methods allow the researcher to design product configurations based on a subset of all the attributes included in the study. Partial profile methods generally accommodate more attributes than full profile methods.
Before briefly describing each of the four general approaches to trade-off analysis, two additional concepts need to be introduced:
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Avant de fonder MACRO, M. McCullough a été le vice-président de Cheskin Associates, leader mondial en stratégies de recherche marketing. Il a ensuite poursuivi sa carrière en tant que directeur de recherche marketing d’une division de Levi Strauss & Company qui fait un chiffre d'affaires de plus 300 millions de dollars.
M. McCullough a également eu l’occasion de faire des conférences et des présentations sur différents sujets de marketing avec l’American Marketing Association (AMA), la Marketing Research Association (MRA) et diverses universités. Il a également siégé au conseil d'administration de l'Association Américaine de Marketing, branche de San Francisco et a été cité dans le Canadian Packaging Magazine. Durant plusieurs années, il a fait des conférences et animé des colloques sur l’application des analyses statistiques multivariées. Il est également membre de diverses autres associations professionnelles.