Of Credit Cards and their Usage

Business Intelligence is a method of making correct decisions regarding patterns and forecasting several KPIs. It may be the amount of spending, forecasting spending, pattern of spending and many more.
Let us try to explain how a BI tool as SAS 9.2 or Cognos 8.0 can help a bank create products in the local geographical unit.
The credit card usage history tells all. Let us suppose that there are three types of card limits- namely silver, in which the credit cap is 50,000 INR, gold, which is 1,00,000 INR, and platinum which is 2,00,000 INR. Also there is a given fact that, each of this card types are actually having at least 3 times the average monthly salary gross. That is, if the credit cap is 50,000 then the salary must be at least within 16,000 INR range, with +/- 2,000 INR.
Let us now get into the statistics. With the credit card usage types for several bills of payment like
·         Utility bills Cash
·         Air tickets
·         Insurance premium
·         Restaurants
·         Retail shop/Malls
·         Movie Tickets
·         Books Purchase
·         Railway Reservation/ cancellation
One can actually conglomerate with the mobile companies to produce the right m-advertisement, and the actual focused means to get the VAS more refined.
For the above 8 things, 8C2 or 28 combinations or smaller groups are possible which can be targeted as a pool of consumers for the exact required and mapped m-ads. 
After that the results should be analyzed leading to a proper forecasting, based on patterns. 
Let us say, for e.g. the utility and restaurants are paid through the credit card for a group; then the m-ads will target the group with only with utility alert and restaurant ads nearby the vicinity, which is found through the GPRS.
Similarly for book purchase and movie tickets conglomerate, the focus of the mobile ads should be on the latest courses and latest movie show, ringtones, show on mobile, and so on. Once the test data is successful, it actually be launched on a bigger scale.
The other factor is the age group. The younger ones in 15-22 range actually wonder more on movies, pubs and restaurants, and hence can be targeted so. A middle aged executive can have m-ads on bonds, and stock options. 
Of course there is a call, that whether there is any disclosure of secret data, but mobile companies can actually buy those data from the credit card analytics team. This can help us to believe, that mobile will be an indispensible part in the coming years.

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