Client Organization:

One of the top-10 large banks of Asia.

Project Owner:

CIO & Head of Transformations.

The Problem:

Large banks have many customers, and it would be difficult to understand each portfolio individually and then make decision. Usually banks call their customers randomly and present their offers in a move to cross-sell other financial products. Sometimes the customer responds positively to the random call from the bank and at times even purchases some product but most of the times these calls annoy customers.At times, the customer calls in looking for a specific product of the bank but the sales representative is not able to assist the customer with the same because the representative is from a different department. All this may result in customer dissatisfaction.

The Solution:

We merged the customer data from all departments of the bank and created a 360-degree view of the customer, to understand the customer and analyze the data in a better and focused manner for cross selling. Then we applied science & technology driven solutions on the data and ensured that the right person engages with the right customer. We developed the models that predicted what product we should offer to a particular customer. These models take the data from the client's Hadoop cluster and make predictions, which appear on the screen of the sales representatives along with the customer's profile information.

Tools & Technologies:

R, Spark-ML lib and Hive