Today’s class covered revision of almost all the topics and techniques covered in Business Analytics course so far. It was a good learning to understand the application of different techniques appropriately to analyze the given data and to come up with strategies.
As the freedom is to us to choose any topic for blog today, I prefer to write about Factor Analysis as it is my favorite topic. Factor analysis is a good technique to reduce data/variables to a smaller number of factor or components. Let us take for example we want to examine banking preferences of customers : There will be around 30-40 variables like which would define customer preferences. However it gets time consuming to analyze so many variables. Thus, factor analysis helps to reduce these variables into dimensions in such a way that variables which have high degree of correlation come under one dimension. Thus, in this example 30-40 variables can be reduced to say around 7 or 8 variables under the label: quality, reliability, accessibility, user friendliness, etc.
With the help of SPSS, factor analysis can help us find out the correlation between different variables and also define the extent to which one variable can be extracted to form a single component of different variables. Another advantage of this technique is that correlation between groups can be found.
Once the variables are reduced to components, various other statistical tools and techniques can be applied to groups to come up with findings and solutions. In our earlier example, we can find the preferences for customers for various dimensions and thus come up with strategies.
Let us take an example where an insurance company wants to understand the responsiveness of customers with respect to product communication. It can prepare a questionnaire and collect data from a set of customers. For example: Will you buy insurance product if a celebrity endorses? , Product communication is better through brochures, product communication is better through audios, etc.
Thus, these independent variables can be grouped under various heads having high correlations. The main labels can be: Communication through flyer, communication through videos, communication through website, etc. Thus, after grouping data under various heads, we can find customer preferences of different set of customers for product communication in insurance industry.
The study can help us find out if people in rural areas prefer communication through agents? People in metro prefer communication through website, etc. These conclusions can help the company decide their product communication and marketing strategy.
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