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Monday, October 24, 2016

Big Data and customers’ retention

As any other business, banking relies heavily on its customers. Is it possible to predict if any customer is about to leave? Well, starting from the fact that keeping customers under your umbrella costs less than acquiring new ones we are in a good path. Acquiring new customers cost from 5 to 15 times more than retaining current ones.

Let´s suppose we have Loui who all Mondays goes to the ATM for his customary $100, all of a sudden Loui transactions begin to be scarce or inexistent. Loui is about to leave but we do not know why. And the worst is that we barely notice about Loui among all our customers. How important is to predict customers who move off from our bank? Which kind of data do we need to analyze?

Our mission is to predict customer’s departure before it actually happens to retain him. Data that comes to our help include: number of contracted products, savings evolution or account movement, occupation, claims, demographic data, etc.

Other source is the one that the bank has collected from lost customers, who had left the bank. Behavioral patterns are there.

Moreover, there are different kind of “break ups” to design and build maps of departure, profile of customers who leave, most common channels that originate such response from our customers, geographic zones, products, and related data.  

What do we obtain from such analysis?

1.       To   know which customers’ segment should be care and kept.

2.       Plans and strategies to respond before customer relinquishment. We can know with anticipation when, who and why.

3.       Increase customer’s satisfaction and fidelity by knowing his needs in a better way.  consumption

How to analyze
·         External Sources that have information about demographics, employment status and current personal status.
·         Social Networks
·         Fusion and analysis of structured and unstructured data
·         Interactive data visualization
·         Customer value that it is used as index of acquiring customers who potentially can leave  
·         Statistical external sources with expenditures/payments information.

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