Data-Driven and Aligned to the Bank’s Customer Base

Data is plentiful in the world of banking. Not only are customers interacting digitally with their banks (and sharing data along the way), but banks are also obtaining third-party data about their customers’ interests and hobbies outside of banking.

External providers who do not have a relationship with the customers collect third-party data. The data comes in many forms and is collected from websites, apps, and social media platforms, and it helps inform marketing campaigns, personalized offers, and the bank’s statistical models. For example, a bank would love to know when one of its checking customers was shopping for a new home. By linking data on Zillow or Redfin searches to individual customers, the bank can know exactly the right time to customize and present a mortgage offer.

Or perhaps a bank customer recently purchased a trampoline for his or her children. Given the higher risk of accidents associated with trampolines, the bank may want to use that knowledge to provide a perfectly timed inquiry for a revised homeowners insurance policy. By using all available data to craft programs and offers that align with the specific needs of each customer segment, banks can increase penetration and customer retention.

Proactive and Evolving

If we learned one thing from the COVID-19 pandemic, it’s that banking needs (i.e., digital self-service capabilities, instant payments, or face recognition) are rarely static. As such, customizing a relationship program to evolve as customer needs evolve is critical. Successful relationship-based programs are engineered to proactively identify customer needs at each life stage based on what customers value most.

For example, a customer that just closed on a house might find value in a Home Depot or Lowe’s gift card, while one who is planning a wedding may be more interested in travel or airline rewards. Once again, use third-party data to proactively identify your customer needs.

System Enabled

Inadequate systems have long been a pain point in implementing an effective relationship strategy. The early relationship banking process was highly manual and time-consuming, and it required face-to-face interaction. Over the last five to 10 years, however, banks have rolled out their versions of “relationship programs.” And while these programs attempted to be broad enough to hit the masses, they often missed the mark due to insufficient system integration, especially with recent acquisitions.

In many instances, a portion of the process for calculating a full customer’s relationship is done manually, which can lead to errors (i.e., understating of assets) or delays in the process — and ultimately an unhappy customer. For example, a bank acquiring another bank has automated access to its legacy entity’s customer assets but is missing the assets for the newly acquired company. This requires someone to remember to manually add the acquired company’s assets, which can result in misses.

From a strategy perspective, after determining your relationship program (i.e., which products are in scope, which customer segments?) and pricing strategy (i.e., differentiate pricing for more price-elastic customers, use average pricing or price to conservative return hurdles), it is essential to ensure that your systems can: 1) pull in the full customer relationship in an automated manner; 2) price to the desired level of granularity or segmentation; and 3) track effectiveness of the relationship program over time.

Here, it’s important to ask: Can my current systems calculate my customers’ banking, savings, and all lending products? Can they provide a different price for a customer based on asset band? And can my systems tell me if a customer has defaulted on one of the lending products they maintain with the bank?

If your current systems don’t allow you to accomplish all three goals, you’ll need to decide early in the process whether to configure them to align to the new strategy or go with a vendor software solution. Either way, not having effective systems can result in errors, inefficient processes, and a poor customer experience.

Incentivize Future Behavior

Many relationship programs fail because they simply reward existing behavior. It’s a nice sentiment, but one that often leads to suboptimal returns. For example, structuring a program to provide a $200 discount on auto insurance for customers that have $100,000 in assets with the bank. Unless your program is structured to change the underlying behavior of your customers, the result is simply a reduction in profitability for the bank. In this case, the customer already brought the $100,000 to the bank and would no doubt have obtained the auto insurance without the $200 discount. While the incentive likely made the customer happy, it didn’t do anything other than reduce the bank’s bottom line.

To balance profitability and customer satisfaction, it is important to structure a program that incentivizes future — not current — behavior. Examples include providing incentives (e.g., the same $200 discount referenced above) for customers who bring $100,000 in new assets to the bank or offering an interest-rate booster on a new bank product once the customer demonstrates strong repayment performance on a lending product (i.e., two years of consecutive on-time monthly payments). Structuring your programs to incentivize changes in behavior will lead to improved profitability for the bank and an enhanced experience for the customer.

Customer Profitability Mindset

Finally, many banks are still organizationally aligned by individual banking products — e.g., checking accounts, credit cards, home loans, auto lending, etc. As such, many banking leaders take a siloed approach and are concerned primarily about the profitability of the product(s) for which they are responsible. An auto lending product lead, for example, will be focused on the profitability of the auto lending product, but less concerned about implications to the card profit and loss (P&L). As a result, they may not often have the best interest of the customer, or the enterprise as a whole, in mind. A decision that may be best for enterprise profitability may go unmade if it is depletive to the individual product P&L.

In a siloed structure, a home loans incentive of 75 basis points for a customer bringing $150,000 in new assets to the bank may increase the overall profitability of the bank. Since it may hurt the home loans P&L, however, the home loans product lead may not want to take the hit by offering the incentive. Revamping organizational structures, incen- tive plans, and/or transferring P&Ls to reflect a customer profitability mindset will result in programs and offers that best serve the customers and should also help banks achieve higher profitability. $