The data and analytics edge in corporate and commercial banking

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With revenues of $2.3 trillion, corporate and commercial banking accounts for a significant share of total global banking revenues (Exhibit 1). In the United States, for instance, corporate and commercial banking revenues have advanced at double the rate of GDP growth.1 However, succeeding in this sector has never been more challenging. Corporate and commercial clients are no longer satisfied with the traditional lineup of loan and credit options. They are looking for personalized offerings drawn from a wider array of services—including transactional, fee-based services such as digital, real-time payments and beyond-banking features like spend analytics and granular liquidity and cash forecasting. Clients also expect banks to have the capabilities and industry-specific expertise to work with them across their global supply chains and help them tackle new challenges, including decarbonization. Adding to the challenge are fintechs, which are vying for market share in domains such as payments, lending, and securities trading.

Corporate and commercial banking will continue to represent a significant share of overall banking revenue.

Much of the responsibility for meeting such complex client demands falls upon a bank’s frontline staff, its relationship managers (RMs). These employees, who are responsible for the organization’s largest customers, already have a lot on their plates. The intensity of today’s needs often leaves RMs too busy with a subset of existing customers and without enough time to develop new opportunities for the bank or find the right solutions for other valuable clients. According to McKinsey interviews with executives at more than 15 banks, many report that they struggle with client acquisition, with a majority of their RMs acquiring less than five new clients a year. This shortcoming leaves considerable revenue on the table, both from new opportunities and from untapped value propositions that could benefit existing customers.

Next-generation frontline technology

Over the years, banks have introduced a range of technological tools to help RMs serve clients more efficiently and effectively. Many of these solutions, however, are neither user friendly nor comprehensive in their use of data to generate insights for RMs. Account planning, client potential, and pricing solutions, for instance, are not fully automated and require additional time and effort from RMs. In addition, the data inputs are often incomplete and seldom merge internal and external sources to create a full picture of a client’s needs and circumstances.

Some banks are now taking a different approach. Instead of providing the front line with many tools for different tasks (at one bank, we counted more than 30), they are using data and advanced analytics (AA) to build a unified, easily navigable, and intuitive platform within the customer relationship management (CRM) system. This approach can help RMs better understand their customers and lead to new sales in each customer relationship.

Powered by data, these AA-enabled workbenches offer a variety of tailored insights, such as new client opportunities and next products to buy, calculations of client revenue potential, detailed pricing guidance, and identification of clients at risk of churn (Table). Designed to be a hub that includes all the information needed by an RM or team leader, these platforms help frontline employees navigate the complexity of client expectations and serve clients better.

Leading corporate and commercial banks use advanced analytics to empower relationship managers in key areas.
Pre-client meeting—preparationClient meeting—negotiationPost-client meeting—tracking
Corporate and commercial banking processProspecting
Product offering
Target tracking
RM needsIdentify likely-to-convert prospects, assess client potential, recommend products, and pinpoint customers likely to churnPrice multiple products at the same time and support price negotiations and what-if scenariosSet targets based on client potential, monitor sales execution, and identify behavior tied to high performance
Advanced analytic modelsProspecting wallet sizing
Next product to buy
Reference pricing
Pricing leakage
Target setting
RM effectiveness
Churn management
Source: McKinsey & Company

In a McKinsey survey of 70 corporate and commercial banks conducted in September 2022, all respondents indicated that they have accelerated their adoption of AA-enabled digital frontline solutions across geographies and tiers over the previous two years. But many organizations are still learning how to create and implement these solutions. Roughly three-quarters of the surveyed banks said they were still in the experimentation phase, with some struggling to reap the rewards of their investment (Exhibit 2).

Banks are at varying stages in their implementation of frontline advanced analytics.

Impact of a successful advanced analytics initiative

An integrated, data-driven approach represents a step change in how frontline employees use technology, and can lead to significant performance and efficiency gains. According to pilot testing McKinsey undertook with multiple banks, RMs who used AA workbenches had 9 percent portfolio growth over a 12-month period, while control groups who did not use workbenches saw 5 percent growth. They also spread this growth across more clients, received five times more cross-selling ideas, and spent 90 percent less time on account planning.

An integrated, data-driven approach represents a step change in how frontline employees use technology, and can lead to significant performance and efficiency gains.

This level of performance can generate significant top-line growth for a bank. A leading European bank, for instance, used client data to build an AA model that streamlines account planning and quantifies the revenue potential of every existing and prospective customer in the market. Putting the data into the hands of RMs led to revenue growth that was three times faster than the market.

Similarly, a regional bank in the United States used its AA workbench to boost the number of new customer opportunities by five times and generate three to four new suggested actions for each existing client. As a result, the bank’s corporate and commercial revenue is on track to increase by more than 20 percent over three years. And at a leading bank in the Middle East, AA models improved lead conversion by 20 percent and enabled top-line growth four times greater for pilots than for control groups.

In addition to these results, banks adopting AA workbenches have seen other benefits:

  • better prioritization of clients based on their intrinsic needs and their value generation potential for a bank, rather than segmentation by wallet, enabling RMs to devote the appropriate amount of time and attention to accounts
  • more time for RMs to focus on value-adding activities
  • an improved coverage model that encompasses all product offerings
  • key information at RMs’ and team leaders’ fingertips, enabled by a new front-end workflow in the CRM system
  • transparent RM performance management and accountability, with the ability to set escalation paths to sales managers, monitor initiatives, and benchmark frontline performance against targets and peer institutions

Implementation matters

In AA workbench initiatives, the dominant factor associated with success is frontline adoption. Even the most robust and sophisticated tools are of little consequence if RMs don’t see the benefit. The best way to set up an initiative for success is to place the experience and perspectives of frontline employees at the center of the process. The following seven practices are common at banks with high-impact AA workbench initiatives.

Presenting new front-end tools as part of an overall strategic growth program

Giving RMs and other frontline workers an AA workbench as a stand-alone initiative is far less appealing than positioning it as an important pillar of a broader strategic plan. If RMs know why the workbench tools were developed and how the tools will help them deliver on a bank’s three- to five-year agenda, they are far more likely to want to adopt them.

Starting with use cases prioritized by the front line

Although a bank’s analysis may identify a set of high-value use cases, selection of initial use cases should not rely solely on financial criteria. Banks can motivate the front line by inviting RMs to discuss priorities and select the three to five use cases they consider most important. In practice, frontline-prioritized use cases have included client acquisition (at a bank in China), trade finance cross-selling (a bank in the Middle East), client potential (a North American bank), and markets/foreign-exchange cross-selling (a European bank).

Once the workbench is up and running, the bank can add functionality prioritized by bank management. In our September 2022 survey, we asked managers to identify their top two use cases for advanced analytics. The largest percentages selected customer potential or wallet sizing and RM productivity (Exhibit 3). Environmental, social, and governance (ESG) use cases, including net-zero analytics, also are emerging as a priority.

Corporate banking leaders say the highest-priority use cases will be assessing customer potential and relationship manager productivity.

Investing in RM user experience

The ideal AA workbench is seamlessly embedded into the front line’s daily workflow. We have seen significant differences in RM adoption based on how much effort banks put into user experience. Some banks, for instance, have assigned their best designers (such as those who build mobile apps) to create RM journeys that are easy to use, intuitive to navigate, and free from complicated messaging. At one bank, additional data were added to the main client screen in a way that immediately shows RMs the upside potential across different products.

Another important success factor that improves the RM user experience is a streamlined technology stack. Instead of adding yet another frontline tool, banks see a much more positive response from RMs when they use the opportunity to merge all existing functionality into one simple log-in and replace legacy tools, such as spreadsheets for lead-tracking, with digital ones.

Creating a dedicated AA team for the job

Since talent in data, analytics, and digitization is scarce, banks sometimes charge their digital teams with serving retail banking as well as corporate and commercial banking. However, learning to speak the language of RMs and getting domain expertise in corporate and commercial banking often requires full immersion. Banks likely will find it more effective to deploy a small, dedicated team of five people than to shuffle 15 to 20 people across businesses.

Winning RMs’ trust

Since RMs remain a primary driver of client satisfaction, the initial version of an AA workbench must resonate with them. Successful MVPs not only deliver actionable insights but include high-interest information such as the names of other RMs who recently won comparable deals with similar clients. In a 2022 McKinsey Finalta Corporate Banking Digital Benchmark survey, 63 percent of banks reported they had integrated real-time financial monitoring that allows RMs to track their own performance. In addition, more than 50 percent said they have embedded next-action recommendations tailored to specific clients.2

Enrichment over time

Successful AA workbench initiatives have a clear road map for the enhancement and augmentation of use cases. This includes upgrading them with new and granular data sources (for example, transaction data) and more sophisticated modeling techniques. It also entails adding new use cases, such as ESG analytics, and expanding the use case delivery channel, which lets RMs deliver ideas directly to clients and follow up through digital channels.

Adapting the operating model

Data and analytics alone can’t boost banking revenues. To maximize the benefits of advanced analytics, banks need a holistic approach that includes training RMs in getting the most out of a new workbench. They also should align incentives between RMs and product specialists, such as through shared client visits and other joint key performance indicators.

As corporate and commercial banks strive to serve increasingly complex client needs, data and analytics have become table stakes. Initially, leading banks saw success from using data- and analytics-powered frontline tools to better serve their midsize business clients. They are now in the process of scaling this success across the rest of the corporate and commercial sector, from global multinationals and large domestic companies to small businesses.

The next generation of data- and analytics-powered frontline tools helps RMs prioritize clients and opportunities. They also free up RMs to focus on value-adding activities and provide transparency on key information, including RMs’ performance. Cracking the code on digital and analytics is hard, requiring banks to successfully tackle strategy, talent, agile delivery, technology, data, and their operating model. But getting it right can lead to disproportionate impact and enable banks to compete in the high-growth areas.

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