Conjoint analysis is a term given to a broad set of marketing research techniques that are used in new product development. Some of the main types of conjoint analysis include Choice Based Conjoint (CBC), MaxDiff, and Adaptive Conjoint Analysis (ACA). Because each offers special advantages, THE MARKETING ANALYSTS offers all major types to address the specific requirements of your project.
How Can Conjoint Analysis Help Me?
When the correct conjoint method is used, it is extremely effective determining the optimal set of features that a new product should have and the best pricing strategy. Conjoint analysis works by simulating an actual purchase experience and explains how people make choices between products and services. The result is a dynamic market model that determines the best product design and pricing strategy that optimizes profitability, market share and how the behavior of competitors will influence your market position.
How Conjoint Analysis Works
While the conjoint analysis has been used for many years, new varieties continue to be developed that provide more powerful and accurate results. In a typical study, respondents are asked to make a series of product feature relates trade-offs or choices. The simple exercise usually asks respondents to select or rank the most preferred alternatives from a selection of competing alternatives. The analytical analysis is usually carried out using hierarchical Bayesian mathematics. Because of the flexibility of new conjoint methods, research studies can be conducted by web or paper-and-pencil surveys.
Conjoint analysis can be used to determine what really drives customers to buy one product over another and what customers really value, when the following assumptions are met:
- A product (good or service) must be able to be described or represented by a set of attributes that are mutually exclusive.
- Consumers view the product as a combination of attributes that can be exchanged for others. An example is the inclusion of an additional product feature in exchange for a higher price.
- The total utility (value) of the product being analyzed is equivalent to the sum of the individual utilities of each attribute. It is important to realize that several common forms of “conjoint analysis” do not consider nonlinear relationships, particularly interactions among attributes. Ignoring interaction will lead to bad research results.
- Products with a greater overall utility are more attractive than products with lower total utility scores.
When these assumptions are met, conjoint analysis provides quantifiable and actionable data that include:
- Relative importance for each product attribute
- Most desirable level of each product attribute
- Potential market share for the product
- Market segmentation information
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