By George Smyth, director of R&D at Rocket Software
The banking sector is uniquely placed to offer a high level of customer service that could put it in the same category as retail, a largely acknowledged leader in customer service.
Not only do banks possess detailed customer data, but the majority also have the core technology infrastructure to analyse that information and develop a better understanding of their customers so they can customize programs to meet different lifestyle needs.
At the heart of 96% of the world’s top banks are mainframe computers, which are used to process millions of daily transactions such as cash withdrawals or stock trades. These hugely powerful computers (known as “big iron”) have the capacity to serve customers it other ways, however.
Improving customer service
If you review the top companies for customer service you’ll notice that retailers dominate the list. Retailers have long been known for building bonds and relationships with customers, often through the use of special offers. Special offers incentivised customers to provide their names and additional personal data so retailers could monitor their buying behaviour using loyalty cards. This newfound visibility into customer trends has allowed retailers to personalise their offers accordingly. In recent years, retailers have been taking this a step further by matching historic purchasing data against external sources of data, such as meteorological forecasts. This is allowing them to predict more accurately which offers customers might want to receive, given certain weather conditions, for example.
A Big Data approach to customer service
The banking sector can learn a lot from retail’s Big Data analytics approach to customer service, especially as banks also possess similar buying data – including when and where customers are likely to make purchases. By combining this data with external sources such as social media feeds and geospatial information, banks can begin to build up a holistic picture of their customers. This picture might include knowledge of an impending significant personal event, a customer’s likely location at a point in time, and even personal sentiments towards such things as risk.
All this information, when processed together, can help financial institutions reach a level of customer understanding that you might only expect from a personal relationship. This is helping banks to talk to their customers in a way that suits them, and only offer the services they want to hear about. It needn’t all be about sales, either. Understanding the customer’s habits is already helping banks to identify unusual behavioural patterns that can be an indicator of fraud.
Crunching customer data
Crucial to taking this personalised Big Data approach, however, is the ability to crunch the data. Fortunately, nearly all of the world’s largest banks have the power to do this, in the form of an IBM mainframe. The benefit of the mainframe is that it can reliably and securely manage hugely complex data processing. Mainframes are also capable of handling thousands of request from users and applications simultaneously, without falling over. This is why major institutions trust them with their mission critical applications.
The game-changing technology for banks wanting to process Big Data is not so much the computers: data virtualisation, which allows companies to analyse information held in disparate locations. When the computing power of the mainframe is brought to bear on these numerous data sources, financial institutions can conduct analytics and make decisions instantaneously.
By automating processes, banks can then present offers to customers at the most pertinent times – be that via a smartphone app push notification, SMS, email, or even a phone call from a customer service representative.
Banks already own the data required to offer this high level of personalised customer service; they just need to find the best way of accessing it. There is a need, of course, to give careful consideration to the actual communications that are made on the back of this data. But the point is that organisations are looking for ways to use data to build stronger relationships with their customers. And the technology required to make this happen is ready and available.