Turbocharging revenue growth in US treasury management

Turbocharging revenue growth in US treasury management

By Paul Hyde, Akshay Kapoor, Kuba Zielinski, and David Stewart

Revenue growth in US treasury management tends to be lackluster. Enter new data sources and advanced analytics.

Fee income is always important to commercial banks’ profits, but especially so in an environment where loan margins have been under considerable pressure for a long period. McKinsey research indicates that fee income—predominantly from treasury-management services—is the primary factor separating top-performing commercial banks in the United States from the rest of the field.

Despite its importance, treasury management—comprising cash-management services such as wire, cash deposits, or online banking that deliver fee-based income—often does not receive sufficient leadership attention, with the result being revenue growth that tends to be lackluster. However, banks can quickly change their growth trajectory by tapping into new, high-fidelity data sources created specifically for treasury management (such as those from GC Insights) and by applying advanced analytics, such as machine learning, to that data. When they focus these capabilities on well-defined growth areas—such as pricing, share of wallet, and customer attrition—banks can achieve first-year revenue gains of between 10 and 15 percent, in many instances dropping directly to the bottom line. For an average regional bank (with a portfolio of about $150 million), the revenue-growth potential is $15 million to $20 million, and for a top-five bank, the revenue impact can exceed $100 million.

Capabilities for growth

To capture this growth, banks need a structured approach and capabilities in three key areas:

Comprehensive and granular data. Benchmarking internally, or against a few peer banks, is an approach that lacks both scope and precision. Forward-looking banks are accessing broad-based, internal and external transaction-level data for treasury-management services.

Advanced modeling. Machine-learning models can identify and predict nuanced patterns that elude traditional statistical methods. Families of models focus on separate business use cases—such as pricing, share of wallet, and customer attrition—and are automatically updated as new data become available. These models allow for precise client segmentation (5,000-plus microsegments based on 20 to 30 variables) and generate highly targeted “segment of one” pricing recommendations, enabling managers to all but eliminate revenue leakage caused by inaccurate information.

Empowering relationship managers. To build sustainable top-line growth, relationship managers must be equipped with actionable insights on their clients’ revenue potential and prepared for productive client conversations, especially on the sensitive topic of pricing. Well-designed repricing programs enable relationship managers to identify competitive dynamics and propose new pricing while minimizing client attrition.

Two high-potential growth areas

Successful banks do not build these capabilities in a vacuum. They create them by focusing on growth areas—such as new approaches to pricing treasury services, maximizing share of wallet, and minimizing customer churn. Moreover, they adopt an end-to-end approach and create systematic programs that tightly integrate the new analytical insights with rigorous execution. This is how they achieve both a rapid lift in fee revenues and the cultural change required to sustain it.

Pricing

Securing fair compensation for treasury-management services has always been a challenge; the services are varied and complex, and banks often set prices in the context of broader client relationships, making revenue trade-offs against other services and products. There is also a lack of visibility into the prevailing market, as the actual prices customers pay are not public. Moreover, relationship managers, who know the most about clients and competitors, are often inclined to grant price concessions to close deals. The net effect is that most banks systematically underprice their treasury services. In fact, according to research from GC Insights and McKinsey Analytics, almost 50 percent of price increases lead to revenue destruction (Exhibit 1).

Treasury repricing programs are nearly as likely to be value destructive as value additive.

Advanced analytics give banks tools to overcome these challenges. Machine-learning techniques in particular can bring an unprecedented degree of precision to the pricing of treasury-management services and flag clients that are underpaying for specific services. In place of standard pricing approaches based on industry size and regional segmentation, banks can bring 20 to 30 relevant variables to bear in delineating 5,000 or more microsegments, essentially achieving a “segment of one” level of specificity. Pricing engines also can take into account clients’ price sensitivity and their risk of attrition and guide informed choices among prices and volumes across the full range of commercial-banking products. (The same approach can boost the effectiveness of “next-product-to-buy” recommendations.)

Banks can then develop interactive tools to share the insights with relationship managers, allowing them to drill down for a granular view of the services individual clients use and for precise recommended prices (Exhibit 2). Armed with these insights, relationship managers can propose higher prices and capture fair value, while almost eliminating client attrition. Moreover, they can price new deals far more effectively from the start.

An interactive tool can give relationship managers precise price recommendations, helping to minimize client attrition.

To capture the full opportunity, relationship managers should be trained to use the tools and interpret the new pricing information. Importantly, they also need to be well prepared for difficult conversations with clients on raising prices. Beyond training, field leadership needs to set clear targets based on the recommended prices, assign clear accountability, and actively track revenue progress.

Here, too, interactive tools can be powerful: by rolling up revenue opportunities from the client level into a portfolio view, they provide leadership with the transparency needed to set targets and manage delivery at the level of individual relationship managers and their clients. Successful repricing programs backed by advanced analytics have boosted some banks’ treasury-management revenues by 10 to 15 percent.

Share of wallet

US commercial banks often find that product promotion campaigns for treasury-management services have limited success in expanding wallet share. A primary reason is the failure to address customer needs. McKinsey analysis finds that the probability of presenting customers with the right additional product at the precise moment when a client is open to buying it is extremely small.

Advanced analytics can transform this approach, by providing reliable information about specific customer needs and identifying which customers are likely to buy a given product. For example, banks can apply machine learning to discover new patterns in customers’ use of treasury products and to create Amazon-like recommendation engines that identify the next product that their customers are likely to buy.

As with pricing, simple, interactive tools can give relationship managers real-time access to this information. They can then focus client conversations on the most promising products, rather than promoting products indiscriminately. As a result, relationship managers’ mind-sets shift from the product set to customer needs, and sales efforts become more productive.

When executed properly, a share-of-wallet growth program can generate high-single-digit revenue growth.

Questions for management

As a first step to changing the treasury-management growth trajectory, leaders should debate the following questions:

  1. When was our last comprehensive refresh of target and realized prices for each service code and client?
  2. Does our pricing capture a fair value for all treasury services and for all clients, relative to costs incurred and competitors’ prices?
  3. What is our share of wallet for each customer?
  4. What internal and external data and what analytics are we applying to grow treasury-management revenue?
  5. Do we translate analytics insights into rigorous treasury-management growth programs with clear and measurable results?

High-fidelity data sources and advanced analytics are becoming a must-have capability for growing revenues and expanding margins in treasury management. While a greater share of wallet and effective pricing are the natural focus, the full range of applications is broader—for example, anticipating and preventing client attrition and improving customer service. If commercial banks can quickly generate new insights and translate them into action, they can achieve a significant and sustainable competitive advantage.

About the author(s)

Paul Hyde is a senior partner and Akshay Kapoor is a partner in McKinsey’s New York office, David Stewart is a senior solution leader for GC Insights in Chicago, and Kuba Zielinski is a partner in the Boston office.

The authors wish to thank Matt Fitzpatrick and Sofia Santos for their contributions to this article.
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