In today’s class we learnt about Conjoint analysis, which is also called multi-attribute compositional models or stated preference analysis. It’s basically a statistical technique that originated in mathematical psychology. Today it is used in many of the social sciences and applied sciences including marketing, product management, and operations research. It is not to be confused with the theory of conjoint measurement.
Conjoint analysis requires research participants to make a series of trade-offs. Analysis of these trade-offs will reveal the relative importance of component attributes. To improve the predictive ability of this analysis, research participants should be grouped into similar segments based on objectives, values and/or other factors. The principle behind conjoint analysis is to break a product or service down into its constituent parts then to test combinations of these parts to look at what customers prefer. The exercise can be administered to survey respondents in a number of different ways. Traditionally it is administered as a ranking exercise and sometimes as a rating exercise (where the respondent awards each trade-off scenario a score indicating appeal).
Analysis is traditionally carried out with some form of multiple regressions, but more recently the use of hierarchical Bayesian analysis has become widespread, enabling fairly robust statistical models of individual respondent decision behaviour to be developed.
For example, a television may have attributes of screen size, screen format, brand, price and so on. Each attribute can then be broken down into a number of levels. For instance, levels for screen format may be LED, LCD, or Plasma.Respondents would be shown a set of products, prototypes, mock-ups, or pictures created from a combination of levels from all or some of the constituent attributes and asked to choose from, rank or rate the products they are shown. Each example is similar enough that consumers will see them as close substitutes, but dissimilar enough that respondents can clearly determine a preference. Each example is composed of a unique combination of product features. The data may consist of individual ratings, rank orders, or preferences among alternative combinations.
As the number of combinations of attributes and levels increases the number of potential profiles increases exponentially. Consequently, fractional factorial design is commonly used to reduce the number of profiles that have to be evaluated, while ensuring enough data is available for statistical analysis, resulting in a carefully controlled set of "profiles" for the respondent to consider
There are three phases in the analysis of conjoint data:
- Collection of trade-off data through a questionnaire.
- Statistical analysis of the data, and
- Market simulation.
- estimates psychological tradeoffs that consumers make when evaluating several attributes together measures preferences at the individual level
- uncovers real or hidden drivers which may not be apparent to the respondent themselves
- realistic choice or shopping task
- able to use physical objects
- if appropriately designed, the ability to model interactions between attributes can be used to develop needs based segmentation
- designing conjoint studies can be complex
- with too many options, respondents resort to simplification strategies
- difficult to use for product positioning research because there is no procedure for converting perceptions about actual features to perceptions about a reduced set of underlying features
- respondents are unable to articulate attitudes toward new categories, or may feel forced to think about issues they would otherwise not give much thought to poorly designed studies may over-value emotional/preference variables and undervalue concrete variables
- does not take into account the number items per purchase so it can give a poor reading of market share
Conjoint analysis also forms the basis of much pricing research and powerful needs-based segmentation. From the understanding obtained from the class, conjoint analysis is one of many techniques for dealing with situations in which a decision maker has to choose among options that simultaneously vary among two or more variables. The problem facing the decision maker is how to trade off the possibility that option X is better than option Y on attribute A but worse than option Y on attribute B, and so on.
Group : Marketing 5
Author : Mihir Sikka