An Interesting Tool called Conjoint Analysis !

An Interesting Analysis tool-CoNjOiNt Analysis.!!!

Learning combined with humour is always interesting. Today we learnt about factor analysis and conjoint analysis. I would like to focus on conjoint analysis. This method was learnt in an interesting manner where we are asked to do some exercise in class. This taught us that we need to trade-off between various parameters so that we can find an optimal result.

In actual terms- Conjoint analysis is a sophisticated technique and there are technical issues that need to be considered. In particular, the design of attributes is a crucial step in a conjoint project as choices between poorly defined levels can render the exercise meaningless. We should also be aware that there are different flavours of conjoint analysis depending on the application.

It is like cluster and factor analysis in the sense that these methods try to identify the interdependencies which exist between numbers of variables. In the example involving a new public transportation system, the variables are the features and characteristics that can be designed into the new system and conjoint analysis tries to measure the relative importance of various combinations of those features and characteristics.

In finance it may seek to explain a certain phenomenon, such as the return on a common stock, in terms of the behaviour of a set of predictive factors. It helps in the analysis of seemingly unrelated phenomena and their disparate and combined effect on an investment. Factor analysis takes a large number of dependent variables and seeks to isolate the independent variables determining them. Isolating the independent variables (called factors in this context) helps reduce the number of variables that the analyst must study in order to make accurate statements and predictions about the direction of an investment.

It is also used in many other fields like marketing where it has a huge application.

Steps in Developing a Conjoint Analysis

Developing a conjoint analysis involves the following steps:

1. Choose product attributes, for example, appearance, size, or price.
2. Choose the values or options for each attribute. For example, for the attribute of size, one may choose the levels of 5", 10", or 20". The higher the number of options used for each attribute, the more burden that is placed on the respondents.
3. Define products as a combination of attribute options. The set of combinations of attributes that will be used will be a subset of the possible universe of products.
4. Choose the form in which the combinations of attributes are to be presented to the respondents. Options include verbal presentation, paragraph description, and pictorial presentation.
5. Decide how responses will be aggregated. There are three choices - use individual responses, pool all responses into a single utility function, or define segments of respondents who have similar preferences.
6. Select the technique to be used to analyze the collected data. The part-worth model is one of the simpler models used to express the utilities of the various attributes. There also are vector (linear) models and ideal-point (quadratic) models.

The data is processed by statistical software written specifically for conjoint analysis.

Thus, we see that conjoint analysis is used widely for various purposes as it is efficient and makes analysis a bit easy.

Group: Finance 2

Author Name: Rishika Agarwal