Data is the Secret Weapon for Successful M&A

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The topic of data and analytics at financial institutions typically focuses on how data can be used to enhance the consumer experience. As the volume of M&A in the banking industry intensifies to 180 deals this year, first-party data is a critical asset that can be leveraged to model and optimize M&A decisions.

There are more than 10,000 financial institutions in the U.S., split in half between banks and credit unions. That’s a lot of targets for potential acquirers to sift through, and it can be difficult to determine the right potential targets. That’s where a bank’s own first-party data can come in handy. Sean Ryan, principal content manager for banking and specialty finance at FactSet, notes that “calculating overlap among branch networks is simple, but calculating overlap among customer bases is more valuable — though it requires much more data and analysis.” Here are two examples of how that data can be used to model and select the right targets:

  • Geographic footprint. There are two primary camps for considering footprint from an M&A perspective: grabbing new territory or doubling down on existing serving areas. Banks can use customer data to help determine the optimal targets for both of these objectives, like using spend data to understand where consumers work and shop to indicate where they should locate new branches and ATMs.
  • Customer segmentation. Banks often look to capturing market share from consumer segments they are not currently serving, or acquire more consumers similar to their existing base. They should use data to help drive decision-making, whether their focus is on finding competitive or synergistic customer bases. Analyzing first-party transaction data from a core processor can indicate the volume of consumers making payments or transfers to a competitor bank, providing insights into which might be the best targets for acquisition. If the strategy is to gain market share by going after direct competitors, a competitive insight report can provide the details on exactly how many payments are being made to a competitor and who is making them.

The work isn’t done when a bank identifies the right M&A target and signs a deal. “When companies merge, they embark on seemingly minor changes that can make a big difference to customers, causing even the most loyal to reevaluate their relationship with the company,” writes Laura Miles and Ted Rouse of Bain & Co. With the right data, it is possible that the newly merged institution minimizes those challenges and creates a path to success. Some examples include:

  • Product rationalization. After a bank completes a merger, executives should analyze specific product utilization at an individual consumer or household level, but understanding consumer behavior at a more granular level will provide even greater insights. For example, knowing that a certain threshold of consumers are making competitive mortgage payments could determine which mortgage products the bank should offer and which it should sunset. Understanding which business customers are using Square for merchant processing can identify how the bank can make merchant solutions more competitive and which to retain post-merger. Additionally, modeling the take rate, product profitability and potential adoption of the examples above can provide executives with the final details to help them make the right product decisions.
  • Customer retention. Merger analysis often indicates that customer communication and retention was either not enough of a focus or was not properly managed, resulting in significant attrition for the proforma bank. FactSet’s Ryan points out that “too frequently, banks have been so focused on hitting their cost save targets that they took actions that drove up customer attrition, so that in the end, while the buyer hit the mark on cost reductions, they missed on actual earnings.” Executives must understand the demographic profiles of their consumers, like the home improver or an outdoor enthusiast, along with the life events they are experiencing, like a new baby, kids headed off to college or in the market for a loan, to drive communications. The focus must be on retaining accountholders. Banks can use predictive attrition models to identify customers at greatest risk of leaving and deploy cross-sell models for relationships that could benefit from additional products and services.

M&A can be risky business in the best of circumstances — too often, a transaction results in the loss of customers, damaged reputations and a failure to deliver shareholder value. Using first-party data effectively to help drive better outcomes can ensure a win-win for all parties and customers being served.

Identify Your Customers Based On Need, Not Revenue

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segmentation-3-28-16.pngFor banks that don’t specialize in a particular market, it can be difficult to truly know every customer’s changing wants and needs. And while there’s significant customer research available on retail consumers and large corporate clients, there’s less help available when it comes to understanding mid-market corporate customers.

Despite the lack of information readily available, mid-market companies are a fast-growing segment of customers that banks can’t afford to ignore. In fact, a recent Citizens Commercial Banking survey found that a quarter of mid-market companies, defined as having $500 million to $2 billion in annual revenues, are actively engaged in raising capital, while another 40 percent are looking for opportunities to do so. Additionally, more than half of the mid-market companies in the US alone indicated they are actively seeking M&A deals in 2016.

In an effort to capture and better understand commercial customers, banks have historically tried to segment companies based on the value of their annual sales or revenue range (e.g. less than $5 million, $5 million to $20 million, etc.). However, these revenue estimates are extremely unreliable, because typically, mid-market companies aren’t public companies. They have no obligation to report revenue and are not subject to strict audit guidelines. This means that the main metric banks are using to understand their mid-market customers is self-reported, without any independent validation.

But more important than yielding unreliable data, revenue segmentation really doesn’t give banks much insight into a customer’s needs, aside from their credit need or credit worthiness. This is a severely flawed approach to understanding customers because there are so many non-credit products that banks can profit from.

Take payments, for instance. With payments, the needs of a $5 million construction company have little in common with the needs of a $5 million healthcare services company. While technically in the same revenue segment, the two companies have vastly different payment transaction numbers, payment processes and workflow, payables vs. receivables, and enterprise resource planning and accounting systems.

Simply put, revenue is a misguided way for banks to segment their corporate customers, particularly when it comes to the mid-market. Except in rare cases when revenue estimates are actually reliable and indicative of customers’ needs, the knowledge gleaned from a single revenue figure is minimal, and it doesn’t help banks better understand and serve their customers.

The good news is, there are other ways for banks to effectively target customers and strengthen customer relationships. One approach is to use transactional data as a means to develop detailed portraits of customers and their needs. By identifying and segmenting customers by need (rather than revenue), banks can establish stronger relationships and drive new fee income by offering solutions to address those needs. For example, banks could learn a lot about a customer by looking at their outgoing payments. How many payments are they making each month? What methods are they using to make these payments—paper checks, ACH, credit cards, debit cards?

Understanding the volume and value of payments for specific businesses can be extremely valuable for determining how to market and sell existing products more effectively. It can also expose areas where a bank might be failing its customers and losing good grace with otherwise loyal organizations. For example, seeing that a large group of customers is making payments through third-party solutions is an obvious sign that it’s time for a bank to develop a new or better payments solution of its own.

Banks are sitting on literally millions of customer records that can offer invaluable insights into customers’ wants and needs, however this data is often unused or under-leveraged. It’s an unfortunate reality, but one that can be easily addressed.

In today’s golden age of big data and analytics, banks need to leverage far more than just revenue figures to better understand their customers. By failing to fully understand customers, banks won’t be able to serve customers well, and they’ll run the risk of losing customers to hungrier and more innovative competitors as a result. Luckily, the treasure trove of existing transactional data can provide banks with infinite ways to better segment customers, and the breadth of that data will allow them to serve their customers more precisely and comprehensively.

Identify Your Customers Based On Need, Not Revenue

Written by


segmentation-3-28-16.png

For banks that don’t specialize in a particular market, it can be difficult to truly know every customer’s changing wants and needs. And while there’s significant customer research available on retail consumers and large corporate clients, there’s less help available when it comes to understanding mid-market corporate customers.

Despite the lack of information readily available, mid-market companies are a fast-growing segment of customers that banks can’t afford to ignore. In fact, a recent Citizens Commercial Banking survey found that a quarter of mid-market companies, defined as having $500 million to $2 billion in annual revenues, are actively engaged in raising capital, while another 40 percent are looking for opportunities to do so. Additionally, more than half of the mid-market companies in the US alone indicated they are actively seeking M&A deals in 2016.

In an effort to capture and better understand commercial customers, banks have historically tried to segment companies based on the value of their annual sales or revenue range (e.g. less than $5 million, $5 million to $20 million, etc.). However, these revenue estimates are extremely unreliable, because typically, mid-market companies aren’t public companies. They have no obligation to report revenue and are not subject to strict audit guidelines. This means that the main metric banks are using to understand their mid-market customers is self-reported, without any independent validation.

But more important than yielding unreliable data, revenue segmentation really doesn’t give banks much insight into a customer’s needs, aside from their credit need or credit worthiness. This is a severely flawed approach to understanding customers because there are so many non-credit products that banks can profit from.

Take payments, for instance. With payments, the needs of a $5 million construction company have little in common with the needs of a $5 million healthcare services company. While technically in the same revenue segment, the two companies have vastly different payment transaction numbers, payment processes and workflow, payables vs. receivables, and enterprise resource planning and accounting systems.

Simply put, revenue is a misguided way for banks to segment their corporate customers, particularly when it comes to the mid-market. Except in rare cases when revenue estimates are actually reliable and indicative of customers’ needs, the knowledge gleaned from a single revenue figure is minimal, and it doesn’t help banks better understand and serve their customers.

The good news is, there are other ways for banks to effectively target customers and strengthen customer relationships. One approach is to use transactional data as a means to develop detailed portraits of customers and their needs. By identifying and segmenting customers by need (rather than revenue), banks can establish stronger relationships and drive new fee income by offering solutions to address those needs. For example, banks could learn a lot about a customer by looking at their outgoing payments. How many payments are they making each month? What methods are they using to make these payments—paper checks, ACH, credit cards, debit cards?

Understanding the volume and value of payments for specific businesses can be extremely valuable for determining how to market and sell existing products more effectively. It can also expose areas where a bank might be failing its customers and losing good grace with otherwise loyal organizations. For example, seeing that a large group of customers is making payments through third-party solutions is an obvious sign that it’s time for a bank to develop a new or better payments solution of its own.

Banks are sitting on literally millions of customer records that can offer invaluable insights into customers’ wants and needs, however this data is often unused or under-leveraged. It’s an unfortunate reality, but one that can be easily addressed.

In today’s golden age of big data and analytics, banks need to leverage far more than just revenue figures to better understand their customers. By failing to fully understand customers, banks won’t be able to serve customers well, and they’ll run the risk of losing customers to hungrier and more innovative competitors as a result. Luckily, the treasure trove of existing transactional data can provide banks with infinite ways to better segment customers, and the breadth of that data will allow them to serve their customers more precisely and comprehensively.