A summary of the 6 chapters of the book A. Lemmens provided on Blackboard. The book is really useful if you like to have background or good examples of what we are thought in class. I included the most interesting examples. (full 6 chapters = 56 pages)
Conjoint Analysis
Chapter 5 – Which conjoint method should I use?
Which Conjoint Method should I use?
1. The Ratings-Based Systems:
- Introduced to the market research community by Paul Green and colleagues in the early
1970s. It involved asking respondents to rate (or rank) a series of concept cards (where each
card displayed a product concept consisting of multiple attributes). In 1985, Sawtooth
Software released its first conjoint analysis software system called ACA (Adaptive Conjoint
Analysis).
- In each section, only one or a few attributes were presented at a time, so as not to
overwhelm the respondent with too much information at once. The software led the
respondent through a systematic investigation over all attributes, resulting in a full set of
preference scores for the levels of interest (part-worth utilities) by the end of the interview.
- Limitations
o ACA needed to be computer-administered
o A main-effects model. This means that part- worth utilities for attributes are
measured in an “all else equal” context, without the inclusion of attribute
interactions (can be limiting for some pricing studies where it is sometimes important
to estimate price sensitivity for each brand in the study.)
2. Choice-Based Conjoint (CBC)
- CBC interviews closely mimic the purchase process for products in competitive contexts.
Instead of rating or ranking product concepts, respondents are shown a set of products on
the screen and asked to indicate which one they would purchase.
- They can choose “none” to defer their purchase.
- There are a number of ways to analyze choice results, including:
o Aggregate Choice Analysis single set of part-worths represents all respondents
o Latent class analysis addresses respondent heterogeneity in choice data. Instead of
developing a single set of part-worths to represent all respondents (aggregate
analysis), Latent Class simultaneously detects homogeneous respondent segments
and calculates segment-level part-worths. If the market is truly segmented, Latent
Class can reveal much about market structure (including group membership for
respondents) and improve the predictability over aggregate choice models. Subtle
interactions also can be modeled in Latent Class, which seems to offer a compromise
position, leveraging the benefits of aggregate estimation while recognizing market
heterogeneity.
o Hierarchical Bayes estimation “borrowing” information from every respondent in
the data set to improve the accuracy and stability of each individual’s part-worths.
Suppose we have individual-level part-worths in a data set, and there are two types of
respondents. One group prefers Brand A and is less price sensitive; the other prefers Brand B
and is more price sensitive. If we perform sensitivity simulations with no interaction terms
included, we will see that the share for Brand B is more sensitive to price changes than Brand A.
Brand B respondents are more likely to switch to Brand A due to price changes than vice-versa.
Even though no interaction terms were included, a brand/price interaction was revealed due to
between-group differences in price sensitivity operating within the context of a market simulator.
1
Conjoint Analysis – Book Eline van de Ven
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