TEAM A DAY 10: Avinash Pandey

CONJOINT ANALYSIS

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 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.

First we listed attributes and asked respondents to give score and then some of all was given

the difference between the attributes is calculated and higher difference shows higher importance.

Coming to Generating orthogonal design

here we filled in all attributes when it came to a large number and cannot be done manually on excel, this takes in all attributes and finally gives us best 16 chosen lists which we can analyse on excel and calculate differences. It represents the best combinations.

Generate Orthogonal Design generates a data file containing an orthogonal main-effects design that permits the statistical testing of several factors without testing every combination of factor levels. This design can be displayed with the Display Design procedure, and the data file can be used by other procedures, such as Conjoint.

Example. A low-fare airline startup is interested in determining the relative importance to potential customers of the various factors that comprise its product offering. Price is clearly a primary factor, but how important are other factors, such as seat size, number of layovers, and whether or not a beverage/snack service is included? A survey asking respondents to rank product profiles representing all possible factor combinations is unreasonable given the large number of profiles. The Generate Orthogonal Design procedure creates a reduced set of product profiles that is small enough to include in a survey but large enough to assess the relative importance of each factor.