The past few years have been no picnic for bankers with increased regulations, a challenging rate environment and soft loan demand. The economy is improving in most regions but competition will continue to pressure NIM and profitability in most banks. Yet for many banks, there is a huge untapped opportunity to increase revenue by data mining customer information already in the bank's system. Even for banks that have implemented data mining strategies, many have been slow to effectively utilize this data, for purposes such as determining the profitability of customers or prospects. The following strategies identify revenue enhancement opportunities that can be used to improve your data mining tactics and increase your bank's bottom line.
Too often community banks waive fees for unprofitable customers and this can result in a significant loss of revenue. At institutions without a disciplined approach to fee waivers, almost three quarters of all waived fees may be given to unprofitable customers. An average $500mm bank waives over $300K in fees per year to unprofitable customers. To remedy this, the first step is to identify the customers with waived fees and stratify them according to profitability. For customers with overall profitable relationships, fee waivers may be appropriate. However, for unprofitable relationships, bankers should implement strategies to eliminate or at least reduce the number of waived fees.
Loan renewals are another fertile area for new revenue growth. As renewals come up, banks can use data mining to determine the profitability not only of the customer's loans, but of the overall relationship as well. Banks should use that information to offer attractive terms to the most profitable customers but also know where to price a little higher. One $600mm bank with 25K customers conducted a lending profitability assessment which showed that only 20% of more than 300 customers with either a new loan or a renewal over the past 6 months were profitable. Another 5% were marginally profitable, but the remaining 75% were made to completely unprofitable customers. For this bank, improving profitability on 50% of its client relationships created an opportunity to generate $1mm in additional profit. The study showed that if unprofitable customers are made even slightly more profitable, it can have a dramatic and positive impact on the bank's bottom line.
Somewhat astonishingly, more than 50% of banks have customers with only 1 account and almost 25% of these do not have a checking account. Studies consistently show that the more products a customer uses, the lower the likelihood they will leave and therefore the higher the profitability of the customer. To take advantage of this, banks can use data mining to gather information about customer accounts and, in turn, use that data to sell additional products. Cross-sell ratios for community banks under $2B generally range between 2.1 and 2.7 products per customer, significantly lower than the big national banks. Banks should target the lowest hanging fruit first and that is probably those customers who have only one account with the bank.
Want to learn more about data mining, loan pricing and measuring overall customer profitability? PCBB's ProfitIntel team will be hosting a webinar on how to approach getting more out of your bank's existing data. The dates of the webinars are Tuesday March 17 at 9:00am PT or Thursday March 19 at 1:00pm PT. Look for links in body of the accompanying email to register for either time.
Understanding the profitability of your bank's customers and using that knowledge to improve the earnings of the bank just makes sense. It's far easier to increase business from existing customers who already know you than to bring in new ones, so don't forget to dig for the gold already present in your bank's data.