Monday, 5 September 2011

Factor analysis and conjoint analysis

Factor analysis is a statistical method used to describe variability among observed variables in terms of a potentially lower number of unobserved variables called factors. In other words, it is possible, for example, that variations in three or four observed variables mainly reflect the variations in a single unobserved variable, or in a reduced number of unobserved variables

It helps marketers determine how changing one thing affects sales and more.

In essence, marketing factor analysis is changing one marketing variable to see what affect, if any, the change has on the outcome. The change in sales also affects the bottom line of the company, so factor analysis in marketing helps companies determine which marketing efforts it should pursue, which efforts need work and which marketing efforts may meet the cutting room floor.

Factor analysis in marketing requires an evaluation of how changing one marketing point, such as price, changes the sales of the product. In order to measure how the factor changes the sales, it requires that only one marketing variable is changed at a time in order to measure the relationship between the variables and the outcome. In marketing, changing one variable can be significant because it may cause an increase or decrease the sales of the product.

Factor analysis in marketing is important because it reflects the perception of the buyer of the product. By testing variables, it is possible for marketing professionals to determine what is important to the customers of the product.

Conjoint analysis

Conjoint analysis is a popular marketing research technique that marketers use to determine what features a new product should have and how it should be priced.

Conjoint Analysis is a procedure for measuring, analyzing, and predicting customers’ responses to new products and to new features of existing products. It enables companies to decompose customers’ preferences for products and services (provided as descriptions, visual images, or product samples) into “part-worth” utilities associated with each option of each attribute or feature of the product category.

Companies can then recombine the part-worth’s to predict customers’ preferences for any combination of attribute options, to determine the optimal product concept or to identify market segments that value a particular product concept highly

A key benefit of conjoint analysis is the ability to produce dynamic market models that enable companies to test out what steps they would need to take to improve their market share, or how competitors’ behaviour will affect their customers.

By: Vivek Dhar

Group: Marketing6

Roll no: 13053

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