Conjoint analysis (in marketing)

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Conjoint analysis is a statistical technique used in market research to determine how people value different features that make up an individual product or service.

The objective of conjoint analysis is to determine what combination of a limited number of attributes is most influential on respondent choice or decision making. A controlled set of potential products or services is shown to respondents and by analyzing how they make preferences between these products, the implicit valuation of the individual elements making up the product or service can be determined. These implicit valuations (utilities or part-worths) can be used to create market models that estimate market share, revenue and even profitability of new designs.

Conjoint originated in mathematical psychology and was developed by marketing professor Paul Green at the Wharton School of the University of Pennsylvania. Other prominent conjoint analysis pioneers include Richard Johnson (founder of Sawtooth Software) who developed the Adaptive Conjoint Analysis technique in the 1980s and Jordan Louviere who invented and developed Choice-based approaches to conjoint analysis and related techniques such as MaxDiff.

Today it is used in many of the social sciences and applied sciences including marketing, product management, and operations research. It is used frequently in testing customer acceptance of new product designs, in assessing the appeal of advertisements and in service design. It has been used in product positioning, but there are some who raise problems with this application of conjoint analysis (see disadvantages).

Conjoint analysis techniques may also be referred to as multiattribute compositional modelling, discrete choice modelling, stated preference research and form part of a broader set trade-off analysis tools used for systematic analysis of decisions. These tools include Brand-Price Trade Off, Simalto and new mathematical approaches such as evolutionary algorithms or Rule Developing Experimentation.

[edit] Conjoint Design

A product or service area is described in terms of a number of attributes. 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. Levels for screen format may be CRT, LCD or Plasma for instance.

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, whilst ensuring enough data is available for statistical analysis, resulting in a carefully controlled set of profiles for the respondent to consider.

[edit] Types of conjoint analysis

The earliest forms of conjoint analysis were what are known as Full Profile studies. Here a small set of attributes (typically 4-5) are used to create profiles that are shown to respondents, often on individual cards. Respondents then rank or rate these profiles. Using relatively simple dummy variable regression analysis the implicit utilities for the levels can be calculated.

Two drawbacks were seen in these early designs. Firstly, the number of attributes in use was heavily restricted. With large numbers of attributes the consideration task for respondents becomes too large and even with fractional factorial designs the number of profiles for evaluation can increase rapidly.

In order to use more attributes (up to 30), hybrid conjoint techniques were developed. The main alternative was to do some form of self-explication before the conjoint tasks and some form of adaptive computer aided choice over the profiles to be shown.

The second drawback was that the task itself was unrealistic and did not link directly to behavioural theory. In real life situations the task would be some form of actual choice between alternatives rather than the more artificial ranking and rating originally used. Jordan Louviere pioneered an approach that just used a choice task which became the basic of choice-based conjoint and discrete choice analysis. This stated preference research is linked to econometric modeling and can be linked revealed preference where choice models are calibrated on the basis of real rather than survey data. Originally choice-based conjoint analysis was unable to provide individual level utilities as it aggregated choices across a market. This made it unsuitable for market segmentation studies. With newer hierarchical bayes analysis techniques, individual level utilities can be imputed back to provide individual level data.

[edit] Information collection

Data for conjoint analysis is most commonly gathered through a market research survey, although conjoint analysis can also be applied to a carefully designed configurator or data from an appropriately design test market experiment. Market research rules of thumb apply with regard to statistical sample size and accuracy when designing conjoint analysis interviews.

The length of the research questionnaire depends on the number of attributes to be assessed and the method of conjoint analysis in use. A typical Adaptive Conjoint questionnaire with 20-25 attributes may take more than 30 minutes to complete. Choice based conjoint, by using a smaller profile set distributed across the sample as a whole may be completed in less than 15 minutes. Choice exercises may be displayed as a store front type layout or in some other simulated shopping environment.

[edit] Analysis

Any number of algorithms may be used to estimate utility functions. These utility functions indicate the perceived value of the feature and how sensitive consumer perceptions and preferences are to changes in product features. The actual mode of analysis will depend on the design of the task and profiles for respondents. For full profile tasks, linear regression may be appropriate, for choice based tasks, maximum likelihood estimation, usually with logistic regression are typically used. The original methods were monotonic analysis of variance or linear programming techniques, but these are largely obsolete in contemporary marketing research practice.

In addition, Hierarchical Bayesian procedures that operate on choice data may be used to estimate individual level utilities from more limited choice-based designs.

[edit] Advantages

  • 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

[edit] Disadvantages

  • 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
  • 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

[edit] See also

[edit] Finding related topics

[edit] External sources