Executive Summary

Compared to other U.S. industries, commercial banking is late entering into the age of analytics. A handful of large U.S. banks are making big investments in data analytics teams and platforms that will soon begin to deliver results. Over the long term, analytics could transform many aspects of banking and render most traditional middle-market and small-business banking sales practices obsolete. In the short term, early-adopter banks hope to achieve benefits such as increased effectiveness in identifying attractive market opportunities and high-quality sales leads. This, in turn, will drive efficiencies in customer acquisition and retention.

To that end, banks are starting to capture and leverage internal data, matching it to additional data from external sources. However, the biggest challenge still lies ahead: extracting value-adding insights from those data streams and delivering those insights to internal staff in a timeframe and format that impacts decision-making and changes behavior. Banks that succeed in that quest will achieve more than just a satisfactory ROI on their analytics investments. They will create a significant competitive advantage over their rivals.

In this paper, Greenwich Associates:

  • Assesses the current state of the “front end” data analytics movement among U.S. commercial and business banks,
  • Identifies the areas that will be impacted most and in the near term by analytics, and
  • Provides recommendations on how banks can leverage analytics in sales and other business functions to ultimately boost revenue growth and profitability.

Executive Summary

Compared to other U.S. industries, commercial banking is late entering into the age of analytics. A handful of large U.S. banks are making big investments in data analytics teams and platforms that will soon begin to deliver results.

Over the long term, analytics could transform many aspects of banking and render most traditional middle-market and small-business banking sales practices obsolete. In the short term, early-adopter banks hope to achieve benefits such as increased effectiveness in identifying attractive market opportunities and high-quality sales leads. This, in turn, will drive efficiencies in customer acquisition and retention.

To that end, banks are starting to capture and leverage internal data, matching it to additional data from external sources. However, the biggest challenge still lies ahead: extracting value-adding insights from those data streams and delivering those insights to internal staff in a timeframe and format that impacts decision-making and changes behavior. Banks that succeed in that quest will achieve more than just a satisfactory ROI on their analytics investments. They will create a significant competitive advantage over their rivals.

In this paper, Greenwich Associates:

  • Assesses the current state of the “front end” data analytics movement among U.S. commercial and business banks,
  • Identifies the areas that will be impacted most and in the near term by analytics, and
  • Provides recommendations on how banks can leverage analytics in sales and other business functions to ultimately boost revenue growth and profitability.

Introduction

Banks that fail to leverage analytics to target their highest-value growth opportunities risk falling behind competitors that embrace analysis and execution informed by big data.

In today’s hyper-competitive market, commercial banks have two main avenues for revenue growth. They can lure clients and market share away from rivals or extract more business from existing clients. Neither avenue is easy. Winning business from competitors requires extensive research and sales calls to companies that may or may not be open to a change in providers. Expanding “share of wallet” requires a tough-toachieve blending of relationship manager, customer service and product resources. Both methods require effective strategic planning that identifies goals, allocates resources and measures progress.

A handful of banks recognize the potential of data analytics and have launched initiatives to integrate analytics into their strategic planning and operations. As these banks learn to apply data analysis to identify the highest-potential markets, clients and prospects, their resource allocation will become more efficient. Over time, they will gain a significant competitive advantage over rivals using less sophisticated methods of planning, prospecting, client segmentation, and selling.

Best-in-class banks will aim higher and seek to leverage analytics to create lasting advantage

The message to senior bank management is clear: The analytics arms race is on. In the past, commercial and business banks could afford to put off technology and analytics investments in favor of relationship managers and other staples—those days are over.

By the end of 2018, some of the fastest-moving banks will be using data analytics platforms to identify and target the highest-quality leads, resulting in significant gains in key metrics such as sales win rates and client retention—and ultimately in revenue growth and profitability. All other banks will be forced to match those efforts just to keep pace. Best-in-class performers will aim higher and seek to leverage analytics to create a lasting advantage.

An Industry Late to the Party

As a whole, the middle-market and small-business banking industry is late to the analytics party, most notably in the sales process. Advanced analytics have been adopted at a much faster rate in a range of other industries— including retail banking. Why has it taken so long for commercial banks to adopt these advances?

Analytics are expensive

Analytics are expensive. In an environment of scarce resources, senior management at commercial and business banks have been hesitant to spend money on analytics technology and talent when the ROI on a new relationship manager (RM) hire has historically been more predictable and certain.

Insightful data is very hard to get

Insightful data is very hard to get. Although commercial and business banks do collect plenty of data, most is intended for use in risk management and portfolio management—not other forms of customer data analysis. In addition, existing data usually lacks even the most cursory organizational features, such as a universal client ID number across platforms that links every piece of data to an individual company. As a result, using and analyzing this internally generated data is time-consuming and expensive.

Existing internal data is very hard to use

Existing internal data is very hard to use. Bank technology systems were not designed with data firmographic analysis in mind. In fact, many bank systems were not really designed at all— they were cobbled together from legacy platforms and the tech systems of acquired rivals. The existing data on these platforms is therefore isolated, hard to identify and even harder to capture on a timely basis. The other option—acquiring data from external providers—can be expensive, if not effectively used.

Many customer data points are private.

Many customer data points are private. Not all the data that banks need exists internally. Much of the most valuable data needs to be obtained from outside sources. Access to this data is complicated by the fact that much of it is private. For example, only larger syndicated transactions become public, leaving out a large portion of company-identifiable commercial credit activity.

Banks often don't employ talented data analysts

Banks often don’t employ talented data analysts. For the most part, bank technology professionals have not been on the cutting edge of commercial customer analytics. Without hiring outside talent, banks “don’t know what they don’t know” and lack the vision to connect disparate data sources like UCC filings, equipment finance databases, company databases, etc. Furthermore, organizing, extracting, matching, and analyzing these vast pools of data requires experience.


Analytics Will Make Traditional Approaches Obsolete

Despite these challenges, technology and analytics are about to redefine customer relationship-building. Until very recently, building an analytics capability was such an expensive and daunting task for a bank that achieving an attractive—or even acceptable—ROI was never a certainty. But improvements in bank CRM systems and the development of middleware and other technology that help bridge silos and extract data have lowered costs and made success more attainable.

At the same time, competitive pressures have made performance improvements offered by data analytics all the more valuable. Prompted by these factors, some banks have committed to significant investment in systems that will begin delivering results in the next six months.

In the coming era of big data, analytics will change the way banks operate and banking professionals do their jobs. The graphic below provides just a few examples of how the rise of data analytics—both inside and outside the banking industry—is making traditional approaches to middle-market and small-business banking obsolete.

Big-Data-Transforming-Banking

Analytics Impact Both Strategy and Operations

To be competitive in the future, banks will have to integrate analytics across their entire organizations, at both the strategic and operational levels. To be effective, an analytics platform must deliver data and insights relevant to each user/function, and in time to inform decisions and guide action.

Customized Data/Insights, Delivered Just In Time

When creating a data analytics system—or any other technology platform for that matter—the primary question is: How will people use this system? In other words, what are the current workflows, and how can we create a system that integrates with or significantly improves the way we currently do work? Companies that lose sight of the “usability” question often end up with technology platforms that are underutilized and a poor return on investment.

For banks starting out with data analytics, perhaps the most important usability question is, “What data is needed to improve decision-making and performance in specific roles?” The hard truth is that your employees will not regularly dig through reams of data to find the information they need. Instead, they’ll go without and fall back on traditional methods.

To ensure usage and to maximize the effectiveness of the analytics initiative, banks should create data that is customized to the needs of various constituencies: the C-suite, strategic development, sales, lineof- business, relationship managers, customer service, etc. Insights from this data must be delivered on time and in an-easy-to-use format that does not require staff to make dramatic upfront changes in the way they work. Remember: The most effective analytic platforms are the ones employees actually use to drive action.

To have an impact at the strategic level, data insights must be delivered to C-suite executives, business development heads, and line-of-business leaders, in addition to the heads of sales and customer service. Specific data insights will be applied in strategic analysis, growth planning, resource alignment, and goal setting/performance measurement.

Analytics insights will play the same role in day-to-day operations. Lineoperation professionals in all areas of the bank must receive insights customized to their specific roles. If properly integrated into workflows, these insights will contribute to improved performance in a wide range of areas across the bank, such as targeting high-value accounts, identifying poor competitor performance, improving client relationships, deepening cross-sales, and increasing average wallet size per client.

Analytics-Will-Power-Strategic-Operational-Functions

High-Impact Analytics in the Bank Sales Function

Although analytics will eventually transform nearly all aspects of banking, many commercial and business banks see the short-term ROI case for analytics as most compelling in sales. Best-in-class organizations are focused on creating new efficiencies across the sales function. The most competitive and forward-thinking financial institutions are investing in processes, technologies and people to get more out of their business development efforts.

The chart below lays out a typical bank sales process and identifies specific functions in each segment that can be enhanced with data analytics.

Analytics-Transforming-Bank-Sales-Function

Identifying High-Quality Sales Leads

Within the sales function, the most obvious and direct application for analytics is in segmenting prospects and clients and identifying the best targets and highest-quality leads for sales professionals. There are three levels of data that banks can assess in ranking potential commercial and business banking prospects. Examples include:

Level-1-Data-for-Banks-to-Rank-Potential-Prospects

Information on these and other basic factors are generally available from public sources and third-party vendors. Much Level 1 data can be obtained from traditional business information providers, and include information like contact names, addresses, phone numbers, and email addresses.

These traditional business information providers are not standing still as the analytics world evolves. Rather, they are using technology such as “bots” to upgrade their own offerings. However, to date, most of those efforts have been focused on making existing data easier to obtain and integrate into internal systems. Online portals, mobile tools, integration with bank CRM systems, and other technology advancements are making these sources easier to use by bank sales and business development professionals.

Level-2-Data-for-Banks-to-Rank-Potential-Prospects

Despite improvements in the quality and usability of data from traditional third-party information providers, these companies simply don’t have access to the additional information banks need to fully assess and qualify the potential of prospects and clients. For that reason, information on critical Level II factors can be much more difficult to obtain. There is no single source that can provide all this data. Some data would have to be purchased or obtained individually, some is difficult to obtain at all. Wherever it is found, it is typically expensive.

Level-3-Data-for-Banks-to-Rank-Potential-Prospects

Level III data are the highest-value factors that, if properly analyzed and integrated into a client segmentation and prospecting system, can lead to dramatic improvements in sales effectiveness. The key is to integrate Level III data with Levels I, II and other bank data to inform and drive action across all constituencies.

Armed with more extensive information on customers and prospects, relationship managers can better:

  • Prioritize their time and the time of their product partners on highpotential opportunities
  • Identify product usage gaps and strategies to fill them
  • Cultivate deeper client relationships by demonstrating increased knowledge of issues impacting their client businesses
  • Predict the next relevant offering to proactively bring to the customer

Synthesizing the data will be an important new skill set required of RMs. Equally important will be the ability of managers to train and coach (on an ongoing basis) on ways to best use this information to meet growth targets and keep satisfied customers. The institution that can do all this the fastest will be at a distinct competitive advantage in the markets they serve.

Case Study: Greenwich Associates Data Analytics—Explorer and Focus Tools

A relationship manager for a top 10 U.S. bank learned that a major competitor was planning to exit a certain product set. The RM worked for a bank that partnered with Greenwich Associates for access to a new data analytics platform (EXPLORER and FOCUS) designed to improve bank success rates in customer prospecting and sales.

The analytics application utilizes a broad spectrum of market and customer demographic data, including Level I market data aggregated primarily from public sources, Level II data that includes information on individual company use of bank products and loans, as well as current rates paid, and hard-to-get Level III data on individual companies’ total spend on banking products and services, and satisfaction with current providers.

Using this platform, the RM identified a list of companies that used the competing bank for the soon-to-beabandoned product. After analyzing this list using a set of additional and unique metrics, he pinpointed more than 20 companies that would make the most attractive targets for sales calls. Over the course of two weeks, he made introductory calls and visits to nearly all of them, making sure to showcase his bank’s capabilities in the relevant product set.

Later that month, the competing bank did indeed announce its exit. The RM capitalized on his analyticsenabled groundwork by picking up four of the companies as new clients, with the companies’ need to fill the gap in product coverage as the primary driver. These new relationships provided a quick and meaningful win. According to the RM, the sales allowed him to make “significant progress” toward hitting his sales objective, simply by leveraging one small bit of market knowledge and some highly effective advanced analytics from Greenwich Associates.

Conclusion

Banks that have not launched serious data analytics initiatives should do so immediately. Because the industry as a whole has gotten a late jump on analytics, it’s not too late for even the least analytical banks to catch up. That window will not remain open forever. In fact, it’s closing fast.

A handful of U.S. commercial banks are investing now in platforms that will leverage multiple data sets with proprietary analytic models to drive more efficient prospecting. Large numbers of banks have achieved the goal of feeding high-quality Level I data into CRM systems, and many banks are now working on the next step of creating platforms that deliver Level II data to inform decisions at both the operational and strategic levels.

For analytic late-starters, clearing that hurdle will not be easy, and it will take time. However, once banks start integrating insightful analytics into line operations and strategic decision-making, the gap between analytic high achievers and the rest of the industry will widen quickly.

That gap will get even larger when banks start working with external providers and consultants to integrate Level III data on company spending, satisfaction and relationship quality with current providers (or competitors). Armed with insights from this high-value data, these banks will create a competitive advantage that will be difficult for rivals to surmount.


About Greenwich Associates Analytics Solutions

Level III data are available but are not always invested in or integrated into the broader data set that can drive efficient decision-making by senior management, sales teams and line operations. That’s because this private competitive information is held tightly by companies and their providers. A unique offering from Greenwich Associates provides this and other high-value data to U.S. commercial banks, along with extensive consulting support on the construction and operation of data analytic platforms.

Greenwich Associates interviews thousands of small businesses and middle-market companies about their banking relationships every year. Companies are asked about the products they use and their satisfaction with providers. Using the results of this research, and combining it with Greenwich Associates unique access to customer spend on financial products, we can estimate how much individual companies spend on cash management, credit and other banking products and services, and how happy they are with these products, services and providers.

By packaging estimates on spending with data on company satisfaction with current providers, Greenwich Associates provides a comprehensive and accurate list of prospects that can be ranked by size of wallet and propensity to change providers. While this information is of critical value to bank sales teams attempting to prioritize prospects, it is of equal value to banks monitoring the status of their existing clients. Which big clients are dissatisfied and should be classified as “at risk”? Which clients are the most attractive prospects for cross-sales?

To find out how to leverage data analytics to answer these and other critical questions, contact Duncan Banfield at +1 203-625-5049