Big Data at Bank of America
Given Bank of America’s large size in assets (over $2.2 trillion in 2012) and customer base (50 million consumers and small businesses), it was arguably in the big data business many years ago. Today the bank is focusing on big data, but with an emphasis on an integrated approach to customers and an integrated organizational structure. It thinks of big data in three different “buckets”—big transactional data, data about customers, and unstructured data. The primary emphasis is on the first two categories.
With a very large amount of customer data across multiple channels and relationships, the bank historically was unable to analyze all of its customers at once, and relied on systematic samples. With big data technology, it can increasingly process and analyze data from its full customer set. Other than some experiments with analysis of unstructured data, the primary focus of the bank’s big data efforts is on understanding the customer across all channels and interactions, and presenting consistent, appealing offers to well-defined customer segments. For example, the Bank utilizes transaction and propensity models to determine which of its primary relationship customers may have a credit card, or a mortgage loan that could benefit from refinancing at a competitor. When the customer comes online, calls a call center, or visits a branch, that information is available to the online app, or the sales associate to present the offer. The various sales channels can also communicate with each other, so a customer who starts an application online but doesn’t complete it, could get a follow-up offer in the mail, or an email to set up an appointment at a physical branch location.
A new program of “BankAmeriDeals,” which provides cash-back offers to holders of the bank’s credit and debit cards based on analyses of where they have made payments in the past. There is also an effort to understand the nature of and satisfaction from customer journeys across a variety of distribution channels, including online, call center, and retail branch interactions.
The bank has historically employed a number of quantitative analysts, but for the big data era they have been consolidated and restructured, with matrixed reporting lines to both the a central analytics group and to business functions and units. The consumer banking analytics group, for example, made up of the quantitative analysts and data scientists, reports to Aditya Bhasin, who also heads Consumer Marketing and Digital Banking. It is working more closely with business line executives than ever before.
Source: Big Data in Big Companies, Thomas H. Davenport and Jill Dyché, May 2013 (Go to Suggested Readings to view full article)