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Conjoint Analysis

A Method for Handling a Large Number of Attributes in Full Profile Trade-Off Studies

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…

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A User's Guide to Conjoint Analysis

Conjoint Analysis is the most powerful and important family of analytic techniques in all of marketing research. How else can you answer, all at once, questions of this strategic and tactical magnitude: What price will maximize my profits? What features should my product have? How many of these will I sell? Who will buy them? Why will they buy them?…

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Abbreviated Task Sets

The author examines four commercial data sets to determine how few choice tasks and/or how few respondents are required for generating reasonably accurate disaggregate utilities when Hierarchical Bayes utility estimation is employed.  The author demonstrates that, in carefully  designed and analyzed studies, as few  as one to four choice tasks  per respondent can yield accurate disaggregate choice models. To continue…

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An Examination of the Components of the NOL Effect in Full-Profile Conjoint Models

The existence of the number of levels effect (NOL) in conjoint models has been widely reported since 1981 (Currim et al.). Currim et al. demonstrated that the effect is, for rank-order data, at least partially mathematical or algorithmic. Green and Srinivasan (1990) have argued that another source of this bias may be behavioral. Although NOL can significantly distort study findings,…

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Comment on Kilroy and Williams

This paper, to my mind, epitomizes the Sawtooth Software Conference:  it has real-world applicability, it is thorough and rigorous in its analysis and it is presented in such a straightforward and clear manner that it is accessible to almost any reader.  It is a useful and interesting paper that raises many important issues. The paper discusses two main topics: Sources…

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Comparing Hierarchical Bayes and Latent Class Choice

Choice-based Conjoint is the most often used method of conjoint analysis among today’s practitioners.  In commercial studies, practitioners frequently find it necessary to design complex choice experiments in order to accurately reflect the marketplace in which the client’s product resides.  The purpose of this paper is to compare the performance of several HB models and LCC models in the practical…

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Getting the Most Bang From The Fewest Questions

Conjoint Studies are renowned for yielding a great deal of strategic insight. In a single conjoint study, one can address price optimized relative to profit, revenues, unit sales or market share, optimal product feature set, cannibalization patterns, market response to competitor actions or new product introductions, dollar value of brand equity and many other issues. However, conjoint studies capable of…

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Putting the Why Into Conjoint

Introduction Conjoint models typically provide the “what” but very little of the “why” in customer behavior modeling. Conjoint models may tell the analyst to what degree various product features, a range of prices and/or a variety of brands each affect ultimate product demand but they do not comment on why customers may have been motivated by those features, prices and…

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