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Data Rescued Us!

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Lecture

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Nowadays, because of stiff market competition, organizations’ tendency to customer orientation and customer relationship management has turned into certain complexities. According to previous studies, it has been estimated that the cost of attracting new customers will be five times more than the costs of the maintenance of existing customers. However, some managers believe that any enterprise should optimally spend their limited resources in order to maintain key customers. Recently, banks and financial and credit institutions have tried to calculate the profitability of their customers to offer tailor-made services and establish systematic plans for the allocation of credits to different clusters of customers based on transaction and demographic behaviors. Besides, one size does not fit all and banks found that they cannot establish similar marketing plans, campaigns or services for all at once and prior that they need to know their customers' value. So, based on their net worth, separated offers should be made. In like manner, financial data scientists try to identify new entrant customers’ behavioral model, so they predict their behavior and determine their desires in the future for a better marketing strategy and resource allocation. Thus, the scope of the project can be defined based on the below phases:

Phase 1: Evaluating the customers' value based on their transactional behavior

Who are high net worth individuals?

How many segments does the company have based on different financial behavior?

Phase 2: mining repetitive patterns happened among the demographic characteristics of each segment

What are the dominant characteristics of each segment?

Phase 3: predicting new entrant customers value

how are likely new entrant customers fall into which segments?