Big Data in Banking: Driving Value in Next Best Action

Global Banking Industry Marketing, Big Data, IBM

It’s difficult to read a banking technology article or go to a conference without hearing about big data. Most of us now believe that big data is more than just hype, that it can offer business benefits to those that can leverage big data into new business capabilities. But a common question I hear is “How does it relate to my day-to-day business? What does a “big data” business use case look like?”

To best answer that question, let’s define in simple terms what we mean by big data. In banking, big data usually refers to the tremendous volumes and varieties of data, often arriving at extreme velocities from a wide variety of sources, such as customers, partners, regulators and systems. From billions of transactions and real-time market feeds, to detailed customer service records and correspondence, to Web click streams, location data, social media posts and tweets. All the while, big data increases and accelerates, often beyond a bank’s ability to manage and derive value from it.

But where there are challenges, there are opportunities. Banks that are harnessing big data find they can derive more insight about their business than ever before.

Customer focus is an area of particular interest for many financial organizations. In their effort to satisfy today’s increasingly demanding, Internet-era customers, banks are working to learn as much as possible about their needs, preferences and actions. The better a bank knows its customers, the better it can anticipate the products, services and actions that will create a positive and profitable customer relationship.

While banks currently analyze customer data to obtain insight to help them market, sell and service their customers, the growth of data is outpacing many organizations’ ability to mine today’s mountain of internal and external big data for insight. Banks need new technologies to handle the unprecedented volume, variety and velocity of information. And they need new capabilities to manage the numerous types of data, as well as advanced real-time analytics designed to transform data into fast, actionable insights.

Improving Customer Focus - and Service

In the area of customer focus, let’s look at an example use case where big data can add value – Next Best Action (NBA), which is top of mind for banking executives. NBA tries to balance customer needs with company priorities to come up with the next best action to sell or service the customer – whether it’s an offer for a new product or service, or a response to a service issue – while furthering company objectives, such as increased revenue, higher profits or improved customer retention.

Next Best Action is not a new concept – what is new is the implementation of Next Best Action with the most innovative real-time decision-making technology, which takes into consideration a customer’s profile, preferences, behavior, interaction history, events and location. That insight is then used to suggest the action or best set of actions to recommend to a customer, such as a targeted promotion, a cross-sell/up-sell offer, a discounted product or fee, a retention offer or a service prescription.

In the past, banks have relied on basic segmentation and static offers delivered through standard scripts from their service representatives. These have often focused largely on retention and product promotion campaigns, and static decision trees that are used for all customers, regardless of an individual customer’s past history with the bank. The new breed of NBA solution takes into account all the known information about the customer, including interactions or events, to arrive at optimal next best actions in the form of real-time recommendations, or real-time automated actions. Sophisticated NBA solutions also consider the optimal channel for the offer or interaction based on current channel, channel preferences, and geolocation, be it the branch, Web, contact center, ATM or smart phone.

NBA can be divided into two high-level parts – predictive insight and action delivery. The insight uses all of customer data available to analyze and predict the optimal action to take. The resulting action is then delivered to the customer, whether it’s launching a campaign, performing customer service or recommending a product.

Driving Big Value

The key value big data provides to NBA is two-fold. First, the upfront insight can be significantly enhanced with big data capabilities by analyzing additional customer data that banks have not previously leveraged – often because they did not have the technological capability to handle the volume, variety or velocity of some of the valuable information available to them. With improved big data capabilities, banks can now analyze more volume and myriad types of data than ever before to deliver additional customer insight. Using big data technologies, banks can enrich their customer analytics for NBA using data such as transactions, interaction history (including emails, contact center free-form notes and voice recordings, Web chats), demographics and segmentation, preferences, behavior, channel usage, social media, Web click streams, geolocation, third-party data and more.

The second way big data can add value to NBA is through real-time insight and decision-making, triggered from a variety of customer actions or events, which can automatically execute an action or series of actions. The speed of delivering the action is critical – the faster the bank can take action, at the point of interaction, the better the odds are of a positive outcome.

Leveraging big data for better business outcomes involves a number of challenges, both business and technical. But this transformation in analytic capabilities presents a unique opportunity. Banks that can harness big data can develop competitive advantage by deriving more insight about their customers, their business and their markets.

Learn how banks can develop competitive advantage via the insights provided by big data. Download "Getting the most out of big data: How banks can gain fresh customer insight with new big data capabilities."