## Wednesday, 31 August 2011

### Discriminant Analysis Techniques and Segmentation

Discriminant Analysis techniques and Segmentation :
Market segmentation is one of the most essential factors for any product and brand. Market research is done prior before the launch. Segmentation variables have to be pre-selected and the data is collected, it is necessary to choose the statistical process by which the segments will be identified. The segmentation technique to be used depends largely on the type of data available (metric  or non metric variables), and the kinds of dependence observed - that is, dependence or  interdependence (Cooper D. & Emory, W., 1995, p. 521). Among the most common segmentation techniques used are factor analysis, cluster analysis, discriminant analysis, and multiple regression. Newer used techniques include chi-squared automatic detection (CHAID), LOGIT, and Log Linear Modeling  (Magidson, J., 1990).
The descriptive process is intended to yield a full bodied description of the market segments, which will be useful in the evaluation process but most importantly in the marketing mix creation stage. Multiple discriminant analysis is often used for this purpose (Gunter, B., & Furnham, A., 1992).
In marketing, discriminant analysis is often used to determine the factors which distinguish different types of customers and/or products on the basis of surveys or other forms of collected data. The use of discriminant analysis in marketing can be described by the following steps:
1.    Formulate the problem and gather data — Identify the salient attributes consumers use to evaluate products in this category — Use quantitative marketing research techniques (such as surveys) to collect data from a sample of potential customers concerning their ratings of all the product attributes.
• The data collection stage is usually done by marketing research professionals. Survey questions ask the respondent to rate a product from one to five (or 1 to 7, or 1 to 10) on a range of attributes chosen by the researcher.
• The attributes chosen will vary depending on the product being studied.
• The data for multiple products is codified and input into a statistical program such as R, SPSS or SAS.
2.    Estimate the Discriminant Function Coefficients and determine the statistical significance and validity — Choose the appropriate discriminant analysis method.
•  The direct method involves estimating the discriminant function so that all the predictors are assessed simultaneously.
• The stepwise method enters the predictors sequentially.
• The two-group method should be used when the dependent variable has two categories or states.
• The multiple discriminant method is used when the dependent variable has three or more categorical states.
• Use Wilks’s Lambda to test for significance in SPSS or F stat in SAS - The most common method used to test validity is to split the sample into an estimation or analysis sample, and a validation or holdout sample. The estimation sample is used in constructing the discriminant function. The validation sample is used to construct a classification matrix which contains the number of correctly classified and incorrectly classified cases. The percentage of correctly classified cases is called the hit ratio.
3.    Plot the results on a two dimensional map, define the dimensions, and interpret the results. The statistical program (or a related module) will map the results. The map will plot each product (usually in two dimensional space). The distance of products to each other indicate either how different they are. The dimensions must be labelled by the researcher. This requires subjective judgement and is often very challenging.

NEELIMA MAKANI
Marketing 2