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.