Over the past decade, the US financial system has reached a state of robust health. However, the market is being fundamentally reshaped by five changes that have significant implications for financial-services and financial-technology companies owned by private equity (PE) firms:
- Low yields continue to pressure margins. For example, net interest margin at US retail banks has fallen from 83 basis points in 2010 to 53 bps in 2018.1 Further, today’s economic expansion, the longest since World War II, may be entering its final phase. If so, the next downturn might be around the corner, with associated economic challenges.
- Megabanks continue to vastly outspend others for technology, marketing, and so on. Bank of America, to take one example, spends $1.5 billion annually on marketing; large US regional banks spend $80 million to $100 million on average. Most PE-owned financial-services companies are medium size and face pressure from their largest competitors.
- The behemoths are particularly focused on investments in data and analytics, the new frontier of competition. JPMorgan Chase has $5 billion earmarked for investment in fintechs of all kinds, including analytics specialists. All of this spending is creating pressure on smaller rivals and has already led to a couple of mergers of regional banks. To be sure, the playing field has leveled somewhat, as data availability has increased, and the costs of data storage and computing have fallen radically. But most smaller institutions have not yet effectively used analytics to take advantage.
- The customer’s expectations of financial institutions are being shaped by superior digital experience elsewhere. Customers have outsize expectations from their incredibly easy and even delightful experiences with platforms such as Amazon. Larger banks and insurers have made significant strides in their customer experience, but smaller ones still have some way to go to match these experiences.
- Those same platform companies (and many other e-commerce players) pose another challenge to financial firms. Digital ecosystems are forming around core consumer needs, such as finding housing, saving for retirement, and others. Banks can lead these ecosystems, or partner with others; both roles can boost growth and profits. But many sponsor-owned financial companies are doing neither.
Put it all together, and the next few years might be much more challenging than the recent past. And those rising multiples cannot be ignored: as they have crept up from 7x to about 12x , they provide a stark reminder of the market’s expectations (Exhibit 1).
To continue to justify multiples of 10x or more, PE-owned financial-services companies would need a combination of 10 percent or more top-line growth and a five-percentage-point expansion in margins.2 While we believe that combination is possible, it is meaningfully ahead of current growth rates, and it suggests that firms will have to break the link with GDP that is characteristic of many financial-services markets.
To quickly unlock value through accelerated top-line growth and margin expansion, PE-owned financial-services companies can draw on a tried-and-tested playbook. In this article, we outline a sampling of moves that address the most acute challenges faced by private owners, and we offer case studies of work done by both private and public owners. These moves include steps to build revenue, trim the fat, and capture value from digital and analytics. We focus primarily on PE-owned companies in financial technology, consumer finance, and payments.
Driving above-market top-line growth
Organic growth is critical and is synonymous with performance and survival. McKinsey research has found that companies with more organic growth generated higher shareholder returns than those whose growth relied more heavily on acquisitions.3 And growth is incredibly hard to sustain: only 9 percent of companies that grew faster than GDP were able to stay in the S&P 500 from 1983 to 2013.4 Sponsor-owned companies can take three steps to accelerate and sustain top-line growth.
Optimize pricing to drive margins
Skills in pricing optimization, a strength of many larger companies, are often not fully developed in midsize sponsor-owned companies. This untapped potential is a considerable source of value. Over the past few years, new pricing models like subscription or pay as you go have gained favor. Our experience suggests that a comprehensive overhaul of pricing, including model design, governance, frontline change management, sophisticated analytics to take advantage of the unprecedented amount of pricing data, and other measures can improve profits by 7 to 10 percent.
A good first step in pricing optimization is to develop a comprehensive understanding of the relative profitability of customers and transactions, and the underlying root causes of leakage, using a pocket-margin analysis (Exhibit 2).
Armed with that knowledge, companies can make improvements. Two recent examples demonstrate the power of pricing.
A payments company suffered from an ad hoc and informal process to approve the pricing of new merchant acquisitions. Senior managers were not kept informed, and the company did not collect much of the data available that would help understand its pricing. It developed a statistical model to segment and score deals within each of its customer segments, and then it integrated the tool into its customer-relationship-management system. The deal-scoring tool suggests cross- and upsell opportunities, and it requires no extra effort from frontline sellers, who could seamlessly incorporate it into their daily routine. Use of the tool is tied to sellers’ compensation. Once a proposal is accepted, the tool automatically administers the deal-approval work flow. The company has enjoyed an increase of 3 to 4 percent in the margins of deals covered by the tool.
Like many other fintechs, a US software provider was intently focused on acquiring customers and did not view pricing as a core revenue lever. Its sales-support processes and infrastructure were substandard: it had no list prices, no standard contracts, and little data and no process to assess pricing performance across accounts. To turn things around, it analyzed pricing across a group of similar customers managed through the call center. When this revealed significant variances, the company tested a few repricing tactics. It reset the list price at the 95th percentile of the range and established a new floor at the 20th percentile. It also activated some fees, increased others, and reset the price of certain products. For larger accounts, it conducted account-planning sessions and analyzed the pricing opportunity to set new pricing thresholds and escalation rules. All these moves helped lift margins by 8 percent.
Turbocharge sales-force effectiveness
In our experience, many privately owned financial institutions struggle with ineffective sales forces. It’s often difficult to persuade highly entrepreneurial field forces to adopt healthier sales practices. Institutions typically rely on compensation and other financial incentives, but they do not place an equal focus on culture and the need to shift away from localized practices and toward a universal best-practice approach.
A US financial institution lacked consistent sales and service practices, resulting in variability in performance and customer satisfaction. Vast distances made dissemination of best practices difficult, and inconsistent manager capabilities made traditional implementation options challenging. The bank created a program to build skills in prospecting, resource management, product knowledge, and understanding customer needs. It invited the sales force to help with the design, generating significant buy-in and regional pull. It deployed these bankwide, using digital tools to accelerate the distribution. Early results include a material positive impact on customer-satisfaction scores, up 10 to 30 percent in many branches.
Drive retention
Systematic customer retention is among the most overlooked areas in financial institutions—although if done right, it can prevent customers from churning away, and even lead to growth, typically 3 to 5 percent. Leaders in customer retention typically start with benchmarking to size addressable attrition and prioritize products at greatest risk. Industrial-strength machine-learning models that use internal and external data, often fine-tuned with managers’ expert knowledge, can predict the customers most likely to leave with surprising accuracy. Leaders can then design programs to act on the model’s recommendations and launch campaigns to spread the word and help the enterprise adopt the new behaviors.
A US bank took these steps to address a subtle form of customer attrition: retail mortgage prepayments. (While many banks routinely sell or securitize such mortgages, this institution kept most of what it originated.) It used both internal data (on the mortgage and the total customer relationship) and external data (from credit bureaus and real-estate-information providers) to build a machine-learning model featuring random-forest algorithms. The model identified high-risk customers (those most likely to prepay). The model identified 30 factors that drove risk of prepayment; applying those factors, it found the 10 percent of customers that were four times likelier to prepay. Other models helped identify the best way to reach these customers, such as phone or email contact, to explain the pros and cons of prepayment and offer modifications to payment amounts or loan terms, or home-equity loans. This resulted in a specific retention strategy and outreach approach for each client. All told, the project retained 3 to 5 percent of annual revenues that would otherwise have been lost.
Trimming the fat
Top-line growth is important, but so too is freeing money for investments to drive growth and for platform modernization. Several financial institutions have been reducing costs over the past five years, but in many cases materially improved performance has not adequately improved the bottom line.
A better approach is a comprehensive productivity transformation, which can both cut costs and change the bank’s narrative, painting an exciting vision of future industry leadership. Financial institutions that have permanently lowered their cost base have five traits in common.5
- Think like an activist. Incumbents think about their organizations differently from activists or acquirers. External actors are not attached to past decisions, which allows them to “cleansheet” costs. A fresh slate is often the only way to achieve a step-change reduction.
- Leave no stone unturned. Executive teams often pursue opportunities in a piecemeal fashion rather than programmatically. This guarantees an incremental result. Launching a bankwide effort signals this is a major undertaking, and everyone must contribute.
- Bring the ‘A team,’ and tightly manage the change. The institution must assign its best executives to the effort, to signal that this is the top priority. It is critical for the A team to focus its energy on change management to ensure that the new ways of working are sustained.
- Make tough choices. Structural change is not achieved without tough choices—whether exiting businesses or making leadership changes to create and sustain a new cost culture.
- Get granular. While aspirations should be big and broad, institutions need a set of highly specific initiatives to ensure actions are clear. Typically, institutions that structurally change their costs have hundreds of initiatives, each worth $1 million to $2 million, to ensure their efforts are sufficiently tactical.
A nonbank financial-services company recently fundamentally transformed its distribution model (including its retail network, direct-to-consumer operations, and other channels). The company banked 10 to 15 percent savings from this comprehensive effort, which included changing branch models, redesigning the entire origination journey to reduce the time to close, shifting the direct-to-consumer operating model to drive efficiencies, changing collection-process strategies, and using targeted automation to drive more straight-through processing in the back office. Typically, such efforts require two years or so to capture full value. While that’s daunting for some PE owners, 30 percent of the impact can be captured in the first year, making it a significantly self-funded transformation.
Transforming collections to stem losses
Since the last recession, US household debt has risen to $13.5 trillion, with auto loans and other non-real-estate debt at an all-time high. Yet, despite the growing risk, collections is often managed like a cost center, and a struggling one at that. The predominant practices are “carpet bombing” the customer with phone calls, lots of manual work, and some limited segmentation and analytics. None of these fit in today’s omnichannel customer model.
None of this has mattered much so far. But should a recession strike, a lax approach to collections will be expensive. To avoid such a situation, private managers should consider building a next-generation collections operation. Automating manual tasks across the loss-management cycle can free up capacity to be redeployed to more value-added tasks, such as building predictive models to identify the best way to contact customer microsegments.
A large PE-backed consumer-lending company operated primarily in North American subprime markets. New regulations had reduced effectiveness; for example, as the company moved more fully into compliance with rules regarding customer interaction, performance suffered. The company wanted to address the problem, more broadly improve its performance on collections, and prepare for rapid growth in volumes it expected from a new product, installment loans. The company identified several levers to reduce charge-offs, including analytics to segment customers based on value at risk, a program to build frontline capabilities, new multiyear payment plans, and the use of huddles and dashboards at various levels of the organization. The lender also developed significantly different strategies for payday and installment loans, including different segmentation, contact, and resolution approaches. The program is expected to improve dollars collected by up to 17 percent.
Capturing value from digital and analytics
Portfolio companies must create a basis on which to compete successfully with larger companies, defend their turf from digital disruption, and improve customer experience through investments in digital, analytics, and technology. Repositioning the business for the future involves three prominent elements (Exhibit 3):
- reinventing core commercial processes, for example, by shifting acquisition and marketing to digital platforms, applying analytics in sales, and digitizing processes
- improving operational processes, including better prediction of demand for service through analytics of third-party and internal data; better demand and supply matching through behavioral analytics in call centers, collections, and other service operations areas; and other techniques
- modernizing the core technology platforms, for example, by ensuring high data quality via a “single source of truth,” and moving more work to the cloud.
A privately owned US credit-card provider transformed itself in all three ways to address a critical decline in its customer-acquisition rate. The prospecting model it used in its main acquisition channel, based on FICO scores, suffered from deteriorating performance; other parts of the operation had also broken down. To address the issues, the company identified four changes: in customer segmentation, response to customer inquiry, approvals, and enrollment. Critically, it changed the basis for model segmentation from FICO scores to customer behaviors aligned with its marketing strategy. The provider built five distinct response and approval models for new customer segments, and witnessed a 6 to 8 percent uplift in customer acquisition.
While pursuing a digital and analytics transformation, PE-owned financial-services companies face several challenges. One of the biggest is identifying the handful of digital opportunities with the greatest potential. Another problem is finding a source of funds to invest in these digital capabilities. Managing the investment can be tough, especially as the potential for cannibalization arises when new products compete with old, and when digital channels see more traffic than older channels. PE firms also struggle at times with the need to “replatform” their legacy IT to compete successfully; most small and midsize companies have poor IT environments. Finally, attracting top tech and digital talent is harder for these companies than for their larger competitors.
To get past these obstacles, our experience suggests that a few behaviors are essential. PE owners need to be more hands-on to maximize returns. Portfolio companies will also benefit from up-front guidance and support and from pooled resources. Working closely with the business may offer a chance to create a repeatable playbook for digitizing other companies in the portfolio.
Second, the implementation approach for every digital initiative should be centered on some common elements such as rapid opportunity identification, understanding the value at stake, a repeatable approach to replatforming technology, mindfulness about sustaining skills and capabilities, and strong choices in partnerships and technologies.
From our transformation experience with many portfolio companies, we find five common actions are needed for success. Companies need to aim high, by setting ambitious targets. They should staff a change-oriented management team supported by best-in-class external subject-matter experts. Time to impact can be sped up by relying on a tried-and-tested playbook and execution coaches. Companies need to push the boundaries of what is possible by trusting data and benchmarks as opposed to gut instinct. Finally, they need to put together a governance model that emphasizes value and not process.
