Using Mobile Technology and Big Data to Transform Customer Relationship and Improve Profitability in the Banking Industry: The Future of Digital Banking in Africa
Years after the global financial crisis of 2007, banks are still struggling to determine how they will return to pre-crisis profit margins. The combination of ultra-low interest rates, continued instability in financial markets, stricter regulation and lower-performing assets are all impacting top and bottom lines. To increase profitability, many banks are looking to expand the value proposition they offer to customers. This new value proposition is based on the banks’ ability to develop new products and services that generate alternative revenue streams by fitting seamlessly into customers’ daily lives.
To deliver this value proposition, banks need to leverage big data and analytics to better understand a customer’s behavior and needs. Banks have always benefitted from customer information based on account activity and segmentation.
With the advent of big data technologies, banks can understand their customers in greater depth and predict their needs by analyzing all available customer information.
Armed with new capabilities to make sense of this data, banks can gain the insight necessary to provide personalized products and services in real time. For example, many banks have lacked the data and analytics to examine the correlation and timing of purchases; when a customer buys groceries, does he also buy fuel for his car? That data—and related insight—is now available. The era of big data in banking has arrived. Banks can capture and analyze larger volumes of data from a variety of data sources and types relating to their customers, including correspondence, social media, web clicks and transactional information across multiple channels. Employees can derive insight from free-form contact center notes and take action in real time. Banks can monitor customer behavior, update campaigns and deliver relevant, in-context offers immediately— and take smart advantage of growing channels such as smartphones and tablets.
Capitalizing on Customer Mobility
Customer preferences for interacting through multiple channels and using mobile devices are driving banks to adopt a mobile digital strategy that makes the most of big data. Mobile devices, including tablets and smartphones, are overtaking web and branch touch points, a trend that shows no sign of stopping. Mobile interaction represents an opportunity for banks to provide their customers with the ultimate multichannel experience of doing business anywhere, anytime using ubiquitous cellular technology. To foster lasting connections with customers, banks need to expand capabilities to capture and manage data across all touch points and implement the most appropriate marketing, social business and mobile technologies
Benefitting from a Smarter Mobile Banking Strategy
For banks, smart means moving beyond mobile banking as a mere destination: that is, one more place to go for routine transactions such as balance inquiries or payments. Instead, mobile banking must be seen as an interactive service that works with customers based on their needs. By coupling applications for big data with a smart mobile strategy, banks can increase wallet share and assets under management while lowering the organization’s operating ratio by using more efficient channels.
Mobile technologies enable a bank to extend the customer experience by interacting with customers during their day-to-day experiences instead of simply responding to discrete channel touch points. Big data analytics enable the bank to better predict the customer’s most likely actions, determine next best actions and offer consultative advice in financial matters. Anticipating customer needs before the competition can lead to improved customer relationships and enhanced revenue streams.
Interacting through a Customer’s Daily Experiences
What does the intersection of big data analytics and mobility look like from a street-level view? Here is an example. Rotimi and his wife bought their home with a mortgage from ABC Bank. They recently decided to remodel. An avid cook, Rotimi heads out on a weekend morning and buys a set of chef’s knives for the new kitchen using a bank card.
The bank’s system recognizes that Rotimi is making a purchase related to his home and analyzes his available financial data—including spending patterns, income, savings balance, available credit, loans, credit score and level of risk. The system also analyzes his related activity on social media and discovers that Rotimi loves to cook, enjoys gourmet restaurants, blogs about his dining experiences and “Would love to have a new, restaurant-style six-burner gas stove.”
Key capability: Big Data–Empowered Analysis of Client Specific Spending Patterns
As data volumes increase, banks require cost-effective horsepower to perform this type of personal spending analysis across their entire customer base—not just creating categories of spending ranges that contain many customers, but individually understanding each customer. Achieving this granularity of understanding across many individual customer dimensions, particularly where information about each dimension may require analysis of unstructured data, requires economies of scale in technology that are not possible with traditional warehousing approaches.
Using big data capabilities and predictive analytics to analyze all of the available data about Rotimi, the bank anticipates similar home purchases, but also knows that Rotimi is nearing his credit limit. To seize this business opportunity before Rotimi is offered a credit card from a retailer, the bank sends him an offer to extend his line of credit. Rotimi receives this offer on his smartphone. He reviews and accepts the terms using his smartphone. The line of credit is automatically added to his account overview along with his mortgage, checking and savings.
Key capability: Big Data–Detailed Analysis of Client-Specific Spending Patterns
Leveraging a detailed understanding of Rotimi enables the bank to make more granular decisions about the amount of risk it will be taking when it offers him a line of credit. The bank can choose a set of terms and a credit limit optimized for both Rotimi and the bank—resulting in a customized, personal offer.
Rotimi is pleased with the proactive service from the bank, and uses the credit line increase to purchase a stove. The banking system identifies this large purchase and prompts Rotimi to take advantage of the bank’s free Digital Vault. Using this service, customers 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.
Key capability: Operational Decision Management; Real-time Messaging and Alerting based on an Event
The prompt allows Rotimi to go into the application and post a photograph of the receipt. Optical character recognition (OCR) technology recognizes this as a home-appliance purchase and offers an extended warranty based upon his street address. Rotimi accepts and purchases the warranty on his new appliance.
Key capability: Content analytics and content management for OCR analysis and text analytics
It’s now 11:30 a.m. previously; Rotimi opted into a service provided by ABC Bank offering discounts at many of the bank’s small business customers. Analyzing Rotimi’s regular lunchtime purchase behavior and preferences, the bank sends him a personalized offer from one of its nearby merchants, Sunday Popcorn. This analysis is based on time of day, location, past purchase history and promotional appeal (cash back vs. points).The offer appears on Rotimi’s smartphone. Rotimi shares the offer with his friends through social media: “Anyone up for lunch at Sunday Popcorn? 20% cash back from ABC Bank.” Multiple likes and comments come in, and several friends join him. As Rotimi pays his bill, the bank sends an alert to verify the purchases made today—knives, stove and lunch—preventing fraudulent charges to his account. Rotimi authorizes the transaction, avoiding the embarrassment of a denied charge.
Key capability: Big Data–Detailed Analysis of Client-Specific Spending Patterns and History, Combined with Available Offers within Geographical Proximity
Later that day, Rotimi receives an alert and logs into his account through his tablet. He looks under the “My Offers” tab to see personalized offers just for him. He sees that the bank is offering him its Smart Sweep service. Rotimi wants to learn more about Smart Sweep, so he starts a video chat to learn more about Smart Sweep in the banking application on his tablet.
Key capability: Big Data, Operational Decision Management– Detailed Analysis Combined with Rules that Determine when money should be Automatically Transferred
A video window pops up, and Rotimi is greeted by a bank customer service representative who offers to share more detail about the Smart Sweep service. Because the representative knows that Rotimi was reviewing this bank feature on his tablet, he can anticipate and better answer Rotimi’s questions. The rep explains that Smart Sweep is a bank account that automatically transfers amounts that vary from a certain level into a higher interest-earning investment option at the close of each business day. The result is that the system transfers money from a checking to a savings account. Rotimi adds this feature to his account, and also checks his balances. For this and other customer interactions, big data is essential for cost-effectively analyzing hundreds of detailed attributions per customer, each constantly changing based on daily customer activity. In “My Offers,” Rotimi also sees a home equity line of credit (HELOC) offer based on predictive analytics—including analysis of not only Rotimi’s micro-segment, but also his real-time behavior and social media. Rotimi accepts, scans his driver’s license and his most recent pay statement, and signs the agreement digitally. The bank approves the HELOC
Key capability: Big Data–Detailed Analysis of Client-Specific Behavior Compared with Aggregated Anonymous Data from other Customers
While Rotimi is still logged in, he also views the bank’s new Spending Manager feature to gain insights into how his spending changes from month to month. Rotimi can compare his spending to his financial peers in his geographic location, income and age bracket. With help from big data analytics, banks can develop new products and services that help customers manage their finances, save money, and benefit from services and offers that fit seamlessly into their daily lives. These capabilities enhance the customer experience and promote customer retention, while generating new revenue streams for the banks.