Smarter Digital Banking: Leveraging Information and Mobile

Global Banking Industry Marketing, Big Data, IBM

Part 2 in a 3-part series: Big Data and Next Generation Banking – Leveraging information and mobile to drive revenue and expand the customer value proposition

In the first part of this series authored by Marc Andrews, we discussed some of the challenges that are causing banks to adapt their business models, and “develop new, innovative products and services that add value to their customers in new and different ways.” We outlined that banks have an incredible, yet often untapped opportunity in their accumulated customer information. In this second part of the series, we will seek to answer the questions posed at the end of Part 1. How should banks leverage this information? What new products or services can they offer as a result of gaining insight from this information? And importantly, how do banks offer their customers new and differentiating value as a result of the insight gained from leveraging big data capabilities?

bank.jpgIt has long been the goal of organizations to accurately anticipate and predict customer needs and behaviors. This enables delivery of timely, personalized and enhanced interactions that build profitable relationships and improve customer experience. Key variables in this quest are the accuracy of the prediction. The accuracy of the models for predicting customer needs and behavior are exceedingly dependent upon the depth, granularity and timeliness of customer insight.

Banks leveraging big data technology can now explore and exploit customer information for valuable insight with the depth, speed and accuracy not previously available. They can:

  1. Process and analyze a much greater volume of customer information than ever before. Big data technology can process extremely large data sets with the power and speed to drill down to the individual level, as opposed to relying on sampling of groups. As well, the capability of analyzing huge amounts of data allows an organization to explore all data for gems of insight, not just specific data that is believed to be important. Now years of transactions, payments or interactions can be analyzed to discover patterns that can be leveraged.
  2. Analyze a variety of data sources and types. Banks can gain additional insight into customers by analyzing data from sources not traditionally included in their analysis, including correspondence, social media, contact center notes, chats, and geolocation.
  3. Analyze customer data at speeds not previously possible – 10x, 100x, even 1000x faster – or when necessary, in real time as the data streams. For example, this speed of analysis can enable identifying a customer behavior in real time and take action at the point of interaction, in time to influence a customer decision.

So how do we harness big data to, paraphrasing what we stated in Part 1, generate significant revenue streams by deriving insight from information about consumers and their interests, and provide a value-added service?

An excellent example of a business use case where customer insight can be improved with big data capabilities is optimizing offers and cross sell. In the current state of predicting and making offers, valuable customers are often treated the same as any other customers in their segment. Of course, information is already being analyzed for segmentation. However, the information that feeds segmentation can be limited, and it often includes a very small subset of the overall information that can be harvested about the customer as an individual. As well, there can be a significant time delay in including any changed customer information. However, when we take full advantage of the depth and breadth of information available, as enumerated above, analyzing ALL information available, we can better understand the needs and behaviors of the customer as an individual, rather than just a member of a segment. Armed with a better insight about individual customer needs, attitudes and behavior, offers can be more targeted, personalized and more likely to be accepted.

So with big data providing the capability to better understand customers and create offers that better target the individual, how can banks apply this to increase revenue or even generate new revenue sources? Certainly revenue can show a marked increase by improving offer acceptance. But to drive incremental revenue while at the same time fundamentally improving the customer experience requires more than an improved ability to target offers. It requires rethinking how this significant improvement in customer knowledge can transform current processes and monetize information.

One of the areas ripe for opportunity is to take advantage of the banking customer’s massive move to the mobile channel. According to the U.S. Federal Reserve, mobile banking penetration as of 2012 was 21%, and 47% of smartphone user’s conduct mobile banking.¹ Mobile banking is also becoming a key criteria when consumers select a bank.

Mobile banking must be seen by customers as an interactive service that anticipates their needs and provides compelling services that intertwine with their daily financial lives.

Mobile interaction represents an opportunity for banks to provide their customers with the ultimate multichannel experience of doing business anywhere, anytime. But banks must move beyond mobile banking as a destination for routine transactions like balance inquiries or payments. Instead, mobile banking must be seen by customers as an interactive service that anticipates their needs and provides compelling services that intertwine with their daily financial lives. In a paper published by the Federal Reserve Banks of Boston and Atlanta,² the authors assert that “value-added services are becoming more important than the actual payment transaction…For sustainability, the value proposition of mobile commerce will need to include concrete value-added services beyond payments.” From the banks perspective, mobile offers a nearly always-on channel to intelligently approach customers with offers and services that, when done right, can provide their customers with added value and an improved banking experience.

Banks must take care to not have their offers and services on the mobile channel be perceived in the same light as annoying and ubiquitous coupon offers. The best way to achieve this is to have a value exchange with the customer. The bank must balance offers with value-enhanced mobile services. They must engage the customer with services where there may not be an immediate or obvious revenue impact, but the customer sees them as value-added. An example might be a Digital Vault, where the customer can take a picture of their receipts and warranty cards at the point of sale and store them safely in a bank database for future retrieval. Yet another example may be an online financial tool that acts as a budgeting or spending manager that allows the customer to compare their spending against peers or "communities” – for example by profession or other classifications. The point is to create useful, compelling services that allow the bank to become part of the customers’ daily lives. That makes customers much more receptive to intelligent bank/merchant offers or to accept targeted campaign offers for bank products or services.

For an offer to be considered intelligent, the bank must use the improved insight gained from new big data capabilities to analyze data from all available sources. An example of intelligent offers that fit into a customer’s daily life might go like the following scenario.

Peter, a successful 39-year-old professional, recently indicated in social media that he and his wife were beginning to look at purchasing a new home. His bank, using analysis of massive amounts of publically available social media, was able to cross-reference that social data with their account base and match Peter’s social identity with his profile at the bank. Based upon that insight, Peter’s bank sent him an attractive mortgage offer, comparisons of pricing for local homes, and information about mortgage pre-qualification. Peter found a home he liked, accepted the mortgage offer and purchased a new condominium.

Based upon this home purchase life event and Peter’s spending patterns, his bank was aware that Peter was shopping to furnish his new home. On a recent Saturday morning he made another household purchase. Based on a detailed analysis of his spending patterns and his current financial situation, and importantly, his current real-time transaction, the bank anticipated further household spending and offered a special line of credit to cover additional purchases. The bank presented the credit offer directly to his smart phone as he was still shopping. He was able to immediately accept or reject it on-screen. The value to Peter is he received financial help when he needed it most, without hassle. The bank received value by using big data and analytics to anticipate a customer’s needs, gain wallet share, and possibly head off a credit offer from the store.

Bank and merchant partnerships can also add value for the customer and the bank when intelligent offers are made based on deep analytics from numerous data sources. Using Peter again, a scenario might go like this.

After making his purchase at the store, Peter’s bank prompted his smart phone with a special offer it negotiated with a restaurant nearby. Based upon analysis of Peter’s debit card purchasing patterns, the bank knew he often dines at this particular time of day when he shops, and from analysis of Peter’s social media posts, they knew he enjoys this type of food. Through geolocation of his phone, the bank knew Peter’s current location, and sent an offer to his smartphone in real time, before he even realized he was getting hungry. This intelligent offer goes far beyond typical discount coupons. It considers real-time customer behavior, transactions and past customer preferences to deliver a level of offer personalization that is more likely to be seen in a positive light and accepted.

These are just a few examples of how new capabilities in big data and analytics can change the game for banks wanting to not only create a compelling customer experience, but significantly enhance revenue and customer retention.

In Part 3 of our series, we will examine the platform requirements and functionality needed to support new big data capabilities. We will also look at the common road blocks banks face in leveraging these new capabilities to support Smarter Digital Banking.

To learn more


1. Mobile Banking and Payments Survey of Financial Institutions in the First District – Summary of Results, Federal Reserve Bank of Boston, March 5, 2013

2. U.S. Mobile Payments – Two Years Later, May 2013. Crowe, Pandy, Tavilla, Jenkins.