Community banks pride themselves on superior customer service. Our own informal survey has established that 90% of all community banks believe that they provide an above-average level of customer service (the mathematical irony is not lost on us). However, this corporate misperception is supported by a Bain & Co. survey that found 80% of companies across industries rate themselves as providing superior service, but only 8% of these companies truly deliver such service. In this article, we explore a potentially better model for delivering superior service and looking at letting your customers self-select and the various methods to rank your customers.
Service Is Likely To Get Worse, Not Better
As the economy expands, there becomes a greater disconnect between corporate perception and reality. In the later part of a business cycle, the average level of service at banks usually declines based on both internal customer satisfaction surveys and third-party poling. The CFI Group’s index for customer satisfaction index, for instance, shows lower satisfaction last year compared to the previous year (HERE).
As the economy expands, quality employees are harder to find, less time is devoted to training them (because of increased business activity) and existing resources at banks become strained. This occurs just when the number of customers expands and the total number of interactions increase. Banks must be even more vigilant in the later parts of the economic expansion to maintain their level of service.
Rather than spreading customer service resources uniformly, banks may want to start categorizing their level of customer service based on a ranking. This ranking can be based on profitability (our preference), the frequency of product use, by product or by those that appreciate plus require superior service. The point here is to better use the bank’s resources to accomplish a bank’s goals.
Seeing The Big Customer Picture
Providing superior service across the entire bank customer base is not necessary and even counterproductive. If the goal is to increase profitability, then service should be provided to customers that contribute to the bank’s profitability. In banking, as in most industries, 20% of customers account for 80% of profits for a bank. In some banks, only 10% of customers account for 120% of profits, and the remaining 90% of the customers subtract profitability from the bank.
Few banks can afford to deliver superior service across their entire customer base. Rather, to be successful, banks must segment relationships based on some profitability measure. Our favorite and a favorite of many larger banks is the customer lifetime value ranking (CLVR).
By allocating service resources to customers with the highest lifetime value ranking, banks can allocate limited resources to customers that generate the most profit for the bank. While this can be done in many ways, there are two popular and simple ways that community banks can measure or segment their customer base to identify where to allocate finite service resources.
One way is for banks to measure shareholder value added (SVA) for each relationship. The second way is for banks to use a recency, frequency and monetary (RFM) technique. Both approaches are discussed below.
Shareholder Value Added
Ranking customers on shareholder value added (SVA) is a mathematically simple way to rank customers for banks with a risk-adjusted return profitability model. Using the SVA methodology, banks subtract their cost of capital from net operating profit after tax for each client. For example, two customers may show a 20% return on equity (ROE) for the bank, but customer A has $100k in deposits and $500k in loans, while customer B has $1mm in deposits and $5mm in loans. Customer B would then have 10X the SVA compared to customer A.
Using this technique to segment customers on CLVR is mathematically pure but difficult for many community banks. Most community banks do not have the database required to measure SVA or a uniform view of their customer base. Furthermore, most community banks do not measure ROE for each relationship on a fully-allocated cost basis, or on a risk-adjusted basis.
Recency, Frequency, and Monetary
An alternative to using SVA for customer lifetime value ranking is to use a simple technique that has been used in industries for over 50 years to predict customer behavior and measure profitability. The most profitable bank customers are loyal, use multiple bank products and have larger relationships with the bank. By measuring when the customer had the last interaction with the bank (recency), how many interactions in the last year (frequency), and the size of the deposit or credit relationship (monetary) a bank can segment customers to be able to apportion service to the most profitable relationships. For the banking industry, we recommend weighing monetary measure as primary importance (50% weighted), frequency as intermediate (35% weighted), and recency as least important (15% weighted) in assessing customer lifetime value ranking.
Goal Focusing, Testing, and Self-Selection
Bank can choose a wide variety of items to rank on. A few banks we know hyper-focused on deposit generation so for them it would be appropriate to consider ranking just by deposit value created. Other banks may feel their branch must strategically remain the focal point of their institution and so will look for ways to just reward those frequent branch customers. The key here is to make your ranking system fit your goals. The advantage is that not only does it increase your chance of hitting your goals but the data and calculations required tend to be simplified.
Another idea is to test which customers best respond to service. Those that value service and are sensitive to it should respond by increasing business with the bank. This methodology tends to be the most quantitatively rigorous and time intensive, but it can payoff. Using the Testing Method, banks sample a pre-set number of customers, apply the higher level of service and then see over the course of 12 months (usually) how these customers have responded. Those that have responded well are kept in the program and those that show little sensitivity are moved out while new customers are moved in.
Finally, some banks may take the Amazon Prime approach and let their customers self-select into a category where they are charged more for an account, but receive superior service in return. Using this approach, customers that want and/or need superior service can pay for it.
Putting it into Action
Banks cannot deliver a WOW experience to every customer that walks in the door, and, nor should they. Targeting certain customers, ranking customers, letting customers self-select or testing customers can help banks better match their service resources to those customers that need or value service. This effort can start simple at first and then expand as more data within the bank is brought together and algorithms refined.
Community banks do not have unlimited resources and may want to think about optimizing service resources proactively. The key takeaway is to have “intent” with service delivery. By segmenting customers and focusing resources towards where they can do the greatest good, community banks can maximize customer service and create a consistent WOW experience for the most profitable customers at the bank.
Submitted by Chris Nichols on June 11, 2018