Case study: ING Direct taps big data to understand customers

The bank has already invested heavily in data analys, and the next step is to implement a big-data strategy to speed up the process of understanding a torrent of customer data.
Written by Spandas Lui, Contributor

ING Direct wanted to get into the heads of customers, so the bank started a data-collection initiative to gain a deeper understanding of how it was interacting with customers.


Greg Nichelsen
(Credit: ING Direct)

Now, years later, ING Direct faces the problem of having too much data, and is trying to make sense of all of the information in a useful and cost-effective way.

Thus far, ING Direct has already spent in the range of AU$4 to AU$5 million on data analytics alone.

"We have collected quite granular and detailed information on exactly how customers interact with the bank," ING Direct head of business intelligence (BI) Greg Nichelsen told ZDNet. "We do a lot of analytics against traditional relational data, and we just want to be able to take it a bit farther than that."

This has prompted ING Direct to dabble in big-data solutions to expedite the process of using all of the collected data to help make business decisions. As part of its five-year vision around data, the bank is planning to invest AU$1 million to execute its big-data strategy, with technology implementation set to be completed by late 2013.

ING Direct's BI team is not contained within the IT department. In 2006, the data-warehouse team was taken out of the IT department, and merged with the analytics team to form the BI division. The latter has since been combined with marketing intelligence, and now sits in ING Direct's customer department.

The BI team has a stake in marketing, the brand, customer experience, and the banking products themselves.

"Basically, we are responsible for data warehousing, for reporting and analytics, for market research, and targeted marketing," Nichelsen said.

At ING Direct, data analytics is fed into everything. The bank is able to gleam customer segmentation, churn behaviours, and customer experience through data analytics.

"We have just been doing a parallel run of our marketing campaigns against predicting which product would be the best for customers, and it has been extremely accurate," Nichelsen said. "We are about to switch to a largely model-driven, rather than bespoke-driven, marketing campaign."

ING Direct has been looking at the data with traditional analytics tools, but has found that this has become an arduous process.

"We have been looking at how technologies around big data can help us to do what we are trying to achieve, but at a much lower cost of ownership, so to make data analytics faster and more agile," Nichelsen said.

ING Direct has been trying to structure the data into databases, but the company doesn't want to tamper with or compress the information to ensure that they can get the most out of it. That means no data aggregation or picking and choosing what data to analyse.

"We actually want to have access to all of the information our bank has, and all the information we can get externally, as well," Nichelsen said. "It's a big thing to bring all that data together and be able to do something with it.

In terms of volume, the BI team is only responsible for 4TB to 5TB of data that covers everything from traditional financial statistics right through to customer-interaction information, including web clicks, calls made to customer service, and feedback.

Currently, ING Direct has only tested big-data products in an Amazon Web Services (AWS) cloud environment, such as EMC's Greenplum range of database and analytics tools, along with some Microsoft offerings. Nichelsen is also keen to try out some of the newer big-data analytics tools on the market.

"I'm interested in big data from a total data-capability perspective," Nichelsen said. "It has enormous benefits across business units, and I think the biggest benefit is that it takes you an enormous leap forward in analytic capabilities."

Editorial standards