Banking on a Future in Big Data - How DBS Bank is Driving Customer Engagements with Decision Analytics

Banking on a Future in Big Data - How DBS Bank is Driving Customer Engagements with Decision Analytics

Posted on July 26, 2012 0 Comments

Earlier this week, I participated in a webinar with FierceFinance on Big Data and next-era business intelligence. In preparation for the event, I took a look at the nature of banking and Big Data which blends the virtual world with the real world. I was struck by just how much opportunity there is for retail banks to use big data and decision analytics to create more valuable customer engagement.

To understand how big data affects banking, we must first understand how the banking customer behavior is evolving:

  1. Increasingly mobile customer with shorter attention spans are resulting in demand for instant gratification through a new level of customer experience, targeted at in the location and context the customer is in right now
  2. Increasing use of social media “in the moment” means that good or bad experience are rewarded or punished instantly

How do changing consumer preferences impact big data?

Banks are unique in their ability to access customer income and spending behavior (all part of big data!). This comes from such diverse sources as call center transactions, online banking, credit cards, ATM transactions, social media and even the changing locations of mobile users. With so much structured and unstructured data available, banks need real-time business intelligence in the form of decision analytics that span these multiple information channels to make big data work for them.

For example, DBS Bank in Singapore, one of the largest banks in Asia, wants customers to use their cards, as do most banks.  Part of their strategy to achieve this is to offer a “right place, right time” and highly relevant promotion. The concept is one of having a “personal concierge” who knows where you are, what you’re doing and what you’re interested in – and can highlight interesting offers that can enrich your life.

Say a customer buys a flat screen TV at a retailer that is a partner of the bank. The customer uses their DBS credit card but has low current account balance. The bank knows the customer is an impulse purchaser and has a propensity to take up compelling credit offers so they might push a payment plan offer straight to the customer’s mobile device. In another scenario, a customer that the bank knows is interested in designer shoes could be pushed an offer when walking past a retail partner’s store: come in for 10% off for the next 20 minutes if you use your DBS credit card.

This is real-time one-to-one promotion. You are relating big data histories and propensity models to big data in motion to discover opportunities to post offers. Only relevant and qualified customers get offers. Irrelevant customers may see it as spam. Relevant customers become stickier as they feel the bank is providing a useful service. And the bank makes more money per customers.

For more on this topic, you can also listen to the webinar in full here.

John Bates

View all posts from John Bates on the Progress blog. Connect with us about all things application development and deployment, data integration and digital business.


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