Customer Data: A Priority for Successful Bank Strategy

By Mohammad Nasar and Christopher J. Shifter, PMP

The management and governance of customer information has become one of the most critical success factors for today’s strategic banking initiatives.

Whether their priorities involve increasing deposits and wallet share, improving the customer experience, transforming digital and mobile offerings, innovating through machine learning and artificial intelligence, or meeting regulatory compliance and privacy requirements, successful financial services organizations are finding that well-managed and well-governed customer information often is the first and most critical ingredient.

Today’s changing landscape

Because financial services are so integral to virtually all other forms of commerce, the banking industry is sensitive to general economic trends and developments. In both good times and downturns, accurate and timely customer information is always an essential component of successful bank operations.

Looking beyond the immediate economic circumstances, several longer-term trends are having a lasting impact on the banking industry. Each of these macrotrends has a direct impact on the ways that banks maintain and use customer data:

Changing customer expectations. The banking industry can learn from the examples of leading technology companies, which often register higher levels of customer loyalty than conventional industries typically achieve. Indeed, many technology innovators are now direct competitors, offering customers ways to blend their digital and physical experiences. This ability to integrate digital and in-person interactions and erase the lines between their online, mobile, and in-person experiences is one of the features many of today’s customers find most appealing about fintech competitors. Clearly, customer retention will be higher at banks that interact with customers in ways that reflect their evolving preferences.

Margin pressures and macroeconomic issues. Margin pressures are a constant in the banking industry, but they are being exacerbated by macroeconomic issues. Even before the financial system encountered major new stresses, some economists were expressing concerns that the long-term economic trends were showing signs of an impending slowdown.

Rapid advances in digital technology. In response to customer expectations for simple and immediate digital interactions, the banking industry is rapidly deploying strategies that rely on the newest data science technologies such as artificial intelligence, machine learning, and robotic process automation. Such advances rely on complete and accurate customer data for their models, but many bank chief information officers (CIOs) commonly cite the limitations of their legacy systems as significant barriers to adoption of these powerful tools.

Recognizing the customer data challenge

As banks develop and adapt their strategies for addressing these issues, having ready access to complete, accurate, and timely customer data will be integral to their efforts. Yet many banks recognize they still have significant work to do in this area.

For example, when a large group of bank executives participating in a recent Crowe webinar was asked to evaluate their banks’ ability to understand their customer relationships, only one in five (19.7%) said their data was accessible, well-managed, and immediately available to their customer-facing resources (Exhibit 1).

Exhibit 1: Banks’ understanding of customer relationships

Source: Online survey of Crowe webinar participants, March 4, 2020

Only a small number went so far as to admit that a lack of critical customer data caused them to ultimately disappoint their customers. Although that sounds encouraging, it must be noted that more than half of respondents (56.5%) recounted having to engage in time-consuming data searches, while another 20.5% encountered other challenges in having the information they needed to effectively engage with their customers.

Such shortcomings represent a significant drain on resources. More to the point, they also pose a direct impediment to their organizations’ ability to successfully implement their long-term strategies.

Customer data drives strategy

In the same webinar, participating bank executives were asked to list their institutions’ top priorities in the coming year. Their responses, as illustrated in Exhibit 2, indicate that their immediate plans are to focus their attention on initiatives that directly relate to various aspects of the customer experience, including enhanced digital interactions, privacy and security, and improving their ability to anticipate customer behaviors.

Exhibit 2: Banks’ strategic priorities

Source: Online survey of Crowe webinar participants, March 4, 2020.

Ultimately, the chances of banks successfully achieving these priorities directly depend on the effectiveness and maturity of their underlying customer data management and governance practices. Customer data plays an obvious role in any initiative growing out of their top priorities.

Customer experience, loyalty, acquisition. Well-managed customer data, enhanced with insights from machine learning and artificial intelligence, is essential to providing tailored, value-added information and guidance to the customer.

Digital platforms, enhanced mobile and online. Incomplete customer information in many digital platforms can seriously damage the customers’ interaction experience, especially in comparison to their expectations from other online and mobile experiences.

Customer behavior, insight, prediction. Having a 360-degree view of customer information is no longer enough. The new differentiator is the ability to use that data to predict what customers will do so that interactions can be tailored for maximum effectiveness.

Customer relationship management (CRM) impact, increasing wallet share. In most cases, the greatest barrier to effective CRM and cross-sell practices is not the capability of the platform but rather the reliability, management, and integration of the customer data.

Loan origination, impact, efficiency. Effective customer data management is essential to maintaining and expanding lending relationships, but efforts to modernize loan origination platforms often encounter significant data quality challenges that slow implementation.

Balancing regulatory spend with value. Achieving the right balance between value protection and value creation can be accomplished only through data-driven insights that, in turn, require accurate, complete, and timely customer information.

Privacy and security. Customers are becoming more savvy about the privacy and security of their personal and financial data; at the same time, the regulatory environment is evolving rapidly. Well-secured, accessible, and properly managed customer information is at the center of these concerns.

Data management and governance disciplines

To meet the customer data-related requirements critical to their strategic priorities, banks must address a wide range of specific data and technology issues. In broad terms, these issues and initiatives can be grouped into four general categories: data quality, data storage and accessibility, data management and integration, and data governance.

Data quality must be addressed aggressively for any data-dependent effort to succeed. Data users need to be confident that the data they encounter is complete, accurate, consistent, and timely. Such confidence is impossible if data sources vary. For example, the various segregated and distributed applications that house data within the bank all must agree, even on such basic details as names, occupations, and dates of birth. All too often, even these basics are found to be inconsistent in both content and format across various departments.

The enforcement of data quality must begin at the source – the frontline customer interactions that are the genesis of all data. A dedicated data quality initiative can provide some short-term improvement, and third-party data services can help, but at some point, it is frontline personnel with “hands on keyboards” who populate data that will be used for advanced analytics and other improvements. Obviously, there is a cost associated with achieving quality data, but CIOs and other responsible executives must be prepared to measure and justify the costs by articulating the benefits to the organization.

Data storage and accessibility also are critical components of an effective data strategy. Banks must first understand and evaluate the nature and characteristics of today’s various available storage systems. These include data warehouses, where data is stored in structured, defined circumstances best suited for standard operational reports and repetitive analysis. Data lakes, on the other hand, store data in its rawest and most unstructured form; other, more advanced technology tools are needed to manage and apply such data.

Two other common data storage structures are data marts, where data is stored for a specific business function, and operational data stores, which retain data for short-term analysis or to feed other systems. Each of these storage regimes has its place and purpose, and each raises its own access questions. Most banks will find all types of data storage systems are needed for various specific functions, and each of these systems must be structured in a way that provides appropriate access.

Data management and integration are necessary whenever customer data is acquired from multiple sources containing at least some conflicting data. This effort requires more than just the right technology. Effective data management is possible only within the context of a broader strategy designed to establish systems of trust and appropriate data stewardship.

A master data management strategy is necessary to help identify duplicate records, determine trusted data sources, and establish a trusted customer record. This record will be integrated from its various sources, which include the bank’s core platform as well as its wealth management, CRM, and lending platforms, to name a few. To achieve this, it is necessary to establish a master data management approach, with defined algorithms for data integration, and a metadata management program that helps identify the lineage and ownership of data so that users know where to go to get answers and resolve inconsistencies.

Data governance can be described as a convergence of data quality, data management, data policies, process management, and risk management controls. The key is to establish a culture in which customer data is handled as a business-critical asset, rather than as an element that must be managed and resolved independently for every new initiative.

The first step in establishing effective data governance is to develop an understanding of the complete life cycle of customer data – from collection, to integration and storage, to its ultimate consumption or application. Achieving this is not simply a technology challenge. Rather, it involves all three critical business pillars – people, processes, and tools. In most instances, most data challenges can be addressed most effectively by working on the people and processes elements – that is, clearly establishing organizational roles, placing responsible people into those roles, putting into place processes for managing the data program, and controlling its maintenance over time. With those elements in place, the tools and technology components can be applied much more effectively.

Without effective data management and governance, every new project must begin by identifying, collecting, cleaning, and verifying the relevant data – a costly and time-consuming exercise that often delays or destroys many important and worthwhile initiatives. By shortening or eliminating this recurring, burdensome, and momentum-killing practice, banks can streamline and accelerate their many data-dependent improvement programs such as new product or service offerings, customer acquisition and retention initiatives, new regulatory compliance programs, and other strategic and tactical projects. Rather than starting every such project with a “fire drill” to clean up the data, effective data management makes it possible to begin realizing the results earlier – and at less cost. This advantage becomes even more crucial when applying advanced analytics technology such as artificial intelligence and machine learning.

As customer expectations increase and the velocity of change accelerates, banks will find it is increasingly important to overcome inertia and begin critical customer data improvement initiatives. With both short-term and long-term economic issues adding to the industry’s pressures, those banks that ultimately reach their strategic objectives and achieve sustainability will find that well-managed customer data has been an integral part of their efforts.

Mohammad Nasar

Principal, Financial Services Consulting

+1 630 574 1846

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