new equation for success in the digital era...retail-banking-2016.aspx . 4 | customer profitability:...

Customer Profitability: New Equation for Success in the Digital Era O R A C L E W H I T E P A P E R | F E B R U A R Y 2 0 1 8

0 | CUSTOMER PROFITABILITY: NEW EQUATION FOR SUCCESS IN THE DIGITAL ERA

Table of Contents

Introduction 1

Determining Customer Worth 2

Omni-channel Everywhere 2

Seeking a Complete Picture 4

Customer Insight Quandary 5

Breaking Down Barriers 6

Conclusion 7

.


Introduction

In an era of growing regulation, tighter margins, competition from new players, and changing customer

expectations (especially from Millennials), financial services organizations are focused like never

before on optimizing customer profitability.

Not all customers represent the same opportunity. In fact, the majority of customers are not profitable,

according to industry studies. The goal today is to rapidly identify which customers represent the

greatest potential and then effectively cultivate them to optimize their value. Unfortunately, large-scale

success on this front has proven elusive for many financial institutions.

Customer insight is fundamental to cracking the code and achieving new levels of profitability. Many

firms, however, find that—even after years of work—their legacy business intelligence (BI)

environments cannot deliver the actionable insight they require to invest wisely in their customers, and

they still struggle to make the most of valuable data that resides within and beyond their organizations.

As financial organizations look to redefine their approach to short- and long-term customer profitability,

they require an integrated analytical infrastructure that includes a unified financial services data model,

shared analytical computations, a comprehensive, easy-to-use BI platform, and the ability to scale

rapidly. This carefully crafted approach enables financial institutions to gain a broader view and a

deeper understanding of their customers and unlock new opportunities.


Determining Customer Worth What is a customer worth to you? This question is nowhere near as simple as it appears, especially for large financial services organizations that continue to work to create a single view of each customer across their enterprise. As such, many firms struggle simply to calculate the profitability of a customer relationship, let alone understand the customer’s life time value or, more important, how to maximize its potential.

Expanding insight into customer profitability is increasingly essential to today’s financial services organizations, which are buffeted by unprecedented and unyielding change. More prescriptive regulatory requirements—which drive up compliance costs and diminish fee-based revenue—continue to hinder top-line growth. At the same time, product differentiation is diminishing, and financial institutions face growing competition from non-traditional players, ranging from Apple Pay and Stripe in mobile payments to Lending Club in peer-to-peer lending and Kabbage for small business loans.

Fintechs represent a significant threat to optimizing customer profitability and revenue for several reasons. They typically benefit from modern infrastructures that provide greater insight into customer behavior. Customers also are increasingly embracing relationships with these organizations and are reporting positive experiences with them.

Omni-channel Everywhere Customer expectations continue to changing dramatically. Today’s consumers (especially Millennials)—used to immediate transactions and personalized content delivered to their fingertips—expect to “have it their way,” even when it comes to financial relationships. An omni-channel experience—in which customers can move seamlessly and at will between channels and even simultaneously leverage multiple channels to optimize their experience—is now essential.

While most banks have adopted a multi-channel strategy, few deliver the seamless experience that customers have come to expect in other sectors, such as retail. Consider the use of mobile applications. Many financial institutions continue to lag when it comes to their ability to optimize mobile—from their use of real-time alerts to warn customers of suspicious activity to the ability to dynamically present personalized offers based on a customer’s recent transaction activity.


As the cost to acquire new customers remains high for traditional institutions, banks are shifting their focus to loyalty and retention. In addition, firms increasingly struggle with hidden defection—customers who are purchasing a new product from a competing bank or fintech. While the primary bank wins 64% of all purchases on average, some of the more lucrative opportunities—credit cards, loans, insurance, and investments—are going elsewhere.1

In addition, marketing budgets remain tight, so firms look to focus their efforts on those customers with the potential for optimal profitability in the short and long run.

To gain a clear picture of customer profitability, financial services organizations need to look above and beyond the monetary value of a relationship. They also must understand the cost of acquiring and maintaining a customer, associated risk, as well as lifecycle potential. To do this, enterprises require the ability to integrate and analyze—in real time—many different types of data from many disparate systems. And, they cannot stop with identifying profitability—the goal is to optimize it with the highly personalized products and customer interactions that today’s customers demand. Herein, resides the challenge for many financial services organizations.

1 Customer Loyalty in Retail Banking: Global Edition 2016, Bain & Company, http://www.bain.com/publications/articles/customer-loyalty-in-retail-banking-2016.aspx


Seeking a Complete Picture For many years, the financial services industry has ascribed to the 80/20 rule—that is, roughly 80% of customers fail to turn a profit and the 20% that were profitable could yield significant revenue. In the post-financial crisis world, this is no longer the case—driving firms to pursue a more strategic approach when it comes to customer profitability.

First, financial institutions must identify the value and profitability of each customer. This starts with a 360-degree view of each customer and their relationships with the bank. To determine profitability, the bank must consider the current financial value of the customer across the enterprise as well as the costs associated with obtaining and maintaining that relationship—including on-boarding, customer service, marketing, and more. It is important to understand how a customer interacts with the firm, as there are dramatic cost differences between serving a customer in a branch versus via an online portal or mobile channel. For example, “each mobile interaction incurs a variable cost of about 10 cents,” while a teller or call-agent assisted transaction hovers around $4 per interaction. One also cannot neglect factoring in the potential risk associated with each customer.

In most organizations, the data needed to gain this comprehensive insight is locked in disparate systems—with limited integration between them.

Calculating profitability is just the start. Firms must then work to stratify customers, identifying not only those that are currently most profitable, but also those that have the potential to be. Big data and predictive analytics factor heavily in this process.

Financial institutions are focused on getting to know their customers and delivering highly personalized experiences. And, they would seem to be ideally suited to deliver this personalization due to the volume and diversity of customer and transactional data that they capture across their enterprises. External sources, such as social media profiles and engagement patterns, hold the opportunity to capture additional rich data, such as information on life events that might drive financial purchases – including relocation, marriages, the arrival of children, and a new job. Conversations with agents are yet another vital form of big data, holding the potential to reveal critical information about customer sentiment, as well as changing financial needs. Natural language processing capabilities are vital to unlocking insight from this valuable data, which previously was impossible to achieve.

The ability to manage and analyze this growing universe of structured and unstructured data—internal and external—is essential to moving to a new level of customer intimacy and profitability.

The final component of optimizing customer profitability is executing—using the insight garnered to deliver highly personalized products, services, and channel experiences that are fine tuned to each customer’s unique needs and preferences. The retail industry can provide some valuable lessons on this front.

The best retail organizations, for example, know what consumers want and need—often even before they do. Consumers expect that during their online or mobile experience they will be served up products that will be of interest to them based on current browsing, previous searches, or past purchases. Similarly, brick-and-mortar stores can deliver highly personalized offers at point of sale and via e-mail or mobile marketing to drive immediate and future purchases. Consumers increasingly expect the same level of personalization and convenience from their financial institutions.


Customer Insight Quandary Many culprits—old and new—haunt institutions in their quest for greater customer insight and profitability.


To summarize—firms face complex and most often imperfect data gathering and integration. Data is inconsistent due to siloed organizations and systems, and most products currently in the market are designed to meet the need of individual silos. In addition, analytic outputs are highly resource dependent and are not timely, which hinders informed and immediate business response.

Breaking Down Barriers Financial institutions are eager to better understand and optimize customer profitability across their organizations. To do this, they must expand customer insight and gain the ability to act more rapidly, decisively, and productively—all while investing wisely.

Customer profitability initiatives are data- and insight-intensive undertakings. As such, IT plays a critical role. When assessing a firm’s legacy environment with an eye toward new requirements, financial services organization should consider the following:

» Do we have a comprehensive, industry-specific, and unified data model that spans the enterprise? To gain a 360-degree view of the customer, which is critical to calculating and optimizing profitability, firms require a unified—industry-specific—data model that can accommodate information—structured and unstructured—from all critical sources, including customer relationship management, risk, and enterprise performance management systems; social media channels; and more. The creation of a unified data model supporting all critical applications enables financial services organizations to improve data quality and quantity, thereby, boosting user confidence in the results. It also ensures that all parties across the enterprise are “speaking the same language” when assessing profitability and other customer dimensions. Creating the data model is one of the most critical parts of any successful enterprise analytics initiative as it forms the foundation for all insight. It is also historically one of the most expensive and time-consuming components. Firms can save considerable time and costs with a commercially available data model, such as Oracle Financial Services Data Foundation, which is purpose built for the industry and incorporates the company’s vast experience in the financial services sector.

» Does our legacy environment provide pre-built analytical models that support more meaningful communication and engagement that expands relationships and reduces attrition? For example, can we readily calculate an attrition model/score and cross-sell score as well as capture churn analysis and predict the customer’s future earnings to arrive at net present value and customer lifetime value projections? Pre-built models save time and reduce costs, and firms can have confidence that they are industry proven.

» Can our systems support real-time analytics that incorporates machine learning? Predictive analytics, which leverage big data to enable enterprises to model and project future behavior, are a requirement as it is no longer adequate to simply consider historical data. For example, enterprises are looking to use transactional and social media information to identify customers most likely to switch banks as well as those who may be preparing for important life events, such as a relocation or new job, marriage, or baby. This critical insight equips financial institutions to engage more meaningfully and individually with their customers—and capitalize more efficiently and cost-effectively on new opportunities. When looking to optimize customer profitability, an organization’s analytical environment must be able to provide insight from multiple perspectives, including institutional performance, retail performance, channel experience, as well as transactional history.

» Do our analytical applications put the power of real-time insight in the hands of line of business managers? Business intelligence ages rapidly in today’s dynamic financial services sector. As such, business users need real-time information. To achieve this goal, firms must give them the power to access information as well as create and run reports on demand without the need for IT team intervention. In addition, the ability to quickly create robust personalized dashboards, which incorporate drill-down capabilities, further extends executive insight and should be an important part of any profitability optimization initiative.

» Do we have deep, native, and/or flexible integrations between our enterprise resource planning; enterprise risk management; governance, risk, compliance; and enterprise performance management environments? Complete and reliable integration between these transactional and analytical applications enables firms to rapidly connect business intelligence with business processes and deliver the agility needed to bring new levels of intimacy to client relationships. Firms should seek solutions with pre-built integrations as well as a service-oriented architecture to accelerate time to value and simplify ongoing management.


» Can our infrastructure deliver the extreme performance and flexibility that we need? Organizations are amassing data at an unprecedented pace and must deal with more types of structured and unstructured information than ever before—including images, video, and social media content. To reap the full benefits of this valuable data and deliver real-time insight and intelligence to associates in the front and back offices, as well as to automated systems, firms today need high performance infrastructures that can deploy rapidly, scale easily, and deliver cost efficiency from day one.

A comprehensive customer insight architecture includes industry-specific data model, pre-built statistical models, robust customer and channel analytics, and user-friendly, rules-based reporting and dashboards for a complete solution that enables organizations to understand the potential of opportunities and relationships with unprecedented accuracy.

Conclusion Financial institutions can no longer afford to tolerate the status quo when it comes to customer profitability strategies. As firms focus their efforts on driving down cost-to-income ratios, sales and marketing operations find their budgets under unprecedented pressure. In this environment, the choice is clear. They must invest wisely to engage with customers and build on profitable customer relationships—making every dollar and interaction count like never before.

Customer insight is the key to understanding, predicting, and, most important, optimizing customer profitability. Many organizations, however, continue to struggle with disparate systems and vast realms of unstructured data that preclude the much desired, but often elusive, 360-degree view of a customer relationship. As organizations rethink their approach to customer profitability, they seek next-generation analytical infrastructures that break down silos and give them full command of the extensive and valuable data within their organizations. As important, firms require robust, yet user-friendly, analytical solutions and dashboard capabilities that enable them to not only accurately report on the past but also predict the potential of future customer relationships with greater accuracy—exposing a world of new opportunities and sustainable profitability.


Oracle Corporation, World Headquarters Worldwide Inquiries 500 Oracle Parkway Phone: +1.650.506.7000 Redwood Shores, CA 94065, USA Fax: +1.650.506.7200

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