Conjoint Analysis
Introduction
Conjoint analysis is market research methodology for modeling the market. A quantitative, grass-roots approach, conjoint analysis is used to predict consumer preferences for multiattribute alternatives. It is based on economic and psychological research on consumer behavior, especially at the individual level, which is considered key to making accurate predictions of the total market. The subject of a conjoint study can be either a physical product or a service, and the market can include both new and existing products/services.

Think of the decision process that consumers go through when choosing between complex alternatives. Products vary in terms of their features, performance, and quality and thus are offered at various prices. Conjoint analysis considers a product in terms of a bundle of attributes, or characteristics. Through an interview, data are collected from respondents to capture the tradeoffs they make between attributes. These data are processed to estimate a utility function that expresses each respondentís value for product attributes. These utility values are then used in a market model or simulator to make predictions about how consumers would choose among new, modified, and existing products. Conjoint analysis allows us to analyze future market scenarios based on primary market research. Other techniques, such as historical analysis, would be insufficient to forecast the market for new products, whereas conjoint analysis can model consumersí reaction to hypothetical products that may not yet exist.

Conjoint analysis is a decompositional model in that values are derived from consumersí responses to interview questions, as compared to asking consumers to directly estimate model parameters. In direct assessment, respondents are asked how likely they are to buy a certain product or how much they would be willing to pay for a product with an attribute improvement. This technique is limited in that products are not shown in a competitive context and these questions do not generally represent realistic purchase decisions. Alternatively, conjoint analysis uses inference, which provides a more accurate picture of consumersí buying behavior. In the analysis of responses to questions about hypothetical product concepts, we can infer the value to each respondent of having each attribute level. Rather than expecting respondents to provide direct assessments, they are asked to make a number of decisions that are more realistic and natural. In a typical pairwise comparison, two product concepts are considered jointly. For instance:

Implication
The scope of product planning issues addressed with conjoint analysis ranges from the tactical level to the strategic level. The following is a list of some of the product planning decisions for which conjoint analysis is currently used worldwide:

  • Pricing
  • New product design
  • Product positioning
  • Competitive strategy
  • Marketing strategies
  • Market segmentation
  • Investment decisions
  • Sales forecasting
  • Capacity planning
  • Distribution planning
  • Conjoint analysis is a widespread, time-proven strategic tool. To ensure success, practitioners must carefully set client expectations regarding what conjoint can and cannot do. Conjoint simulators are directional indicators that can provide significant insight into the relative importance of product features and preferences for product configurations. These market simulators predict preference share, that is market share potential. Many internal and external influences such as awareness, marketing, sales force effectiveness, and distribution drive market share in the real world. Unless these effects are explicitly modeled in, care should be taken to regard the model results as preference shares that assume perfect market penetration.

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