A step-by-step approach to determining customer primacy

              Scott Earwood

The financial industry is missing a standard definition of customer primacy which has wheeled banks into creating their own; some have built a “one-size-fits-all” definition based on product mixes or transactions, while others establish primacy based on a threshold number of products a customer has purchased. As a result, many banks believe they have more primary relationships than they actually do, generating a false sense of accomplishment.

The biggest issue with any of these definitions is the idea of customer primacy as a binary designation, not a continuum. Many banks have a set criterion of primacy, and once that is met, they define the relationship as primary or not and move on to the next customer. In reality, these guidelines only reveal clues of “how” primary a bank is for a customer rather than “if” a bank is primary for a customer.  Banks can use these clues to develop an understanding of a customers’ levels or tiers of primacy. White Clay has created a step-by-step approach to help banks achieve a greater degree of primacy, helping them capture long-term revenue opportunities and create stickier relationships.

Understanding customer primacy starts with usable data. While this may be a challenge for most financial institutions, leveraging the expertise and knowledge of strategic partners like White Clay can help them make the most of the data they already have. There’s value in transforming raw data into a readable and usable format for bankers to better determine customers’ needs based on their product usage and transactions. For example, bankers can gain visibility into their customers’ engagement patterns, including the frequency of transactions. These patterns can present new opportunities to engage and deliver value to customers.

After combining, curating, and standardizing data across the organization, technology partners can help banks use the smart data to build accurate, household relationships. These relationships will provide insights into every customer’s critical metrics:pricing, capital consumption, risk expense, revenue, direct expense, and return on capital. With this new level of visibility, bankers can determine how to proceed with each relationship and what incremental improvements are needed to boost shareholder return in the long run.

Once client relationships and households are built, banks can have a better idea of how to price their relationships right, uncovering the most profitable path for the future.Over time, every customer relationship will become more profitable as the bank finds opportunities both large and small to provide better fitted products and services that their customers need.

The last step is to enhance all these efforts with AI, helping banks determine which opportunities can increase customers’ primacy. With an AI engine, bankers can gain a better understanding of account activity and can make more informed recommendations when speaking to customers. This information can help them improve the customers’ banking experience and overall financial health.

For example, the AI engine might find that a customeris spending $20,000 on their American Express card monthly;or that they have taken out a car loan with Wells Fargo; or that deposits are up in the past quarter, indicating that their business is going well. This newlevel of detailwill help bankers understand potential banking needs based on behaviors, finding ways to make it easier for their customers to bank with them.

This approach will also eliminate the manual processes used to determine primacy today, taking the analysis, compilation and aggregation work out of bankers’ hands, enabling them to focus on building and nurturing relationships with their clients.

Penn. – based First Keystone Community Bank, a White Clay client, followed this process and uncovered new useful insights about their client relationships. For example, the bank found that customers that become regular transactors stayed 5.5 years longer with the bank and produced $2,500 more in annual net income. Moreover, First Keystone discovered that each primacy tier increased the lifetime value of a customer by 10%. These insights delivered specific customers to target and triggered new opportunities to engage with those customers to drive revenue growth.

For financial institutions, customer primacy can help them increase profitability and lifetime value, as well as drive shareholder return. For bankers, this information helps them optimize and understand the entirety of their relationships, saving them time spent analyzing customer primacy. And for customers, being a primary customer will help improve their financial health and save them time. Having an accurate methodology of determining customer primacy, and enhancing it with AI, is a win-win-win situation for everyone.

About Author:
Scott Earwood is the Director of Bank Solutions at White Clay, a company that provides financial institutions with a single source of truth to optimize revenue and deliver value to customers and shareholders. Scott helps banks optimize capital and revenue through pricing and profitability of new and existing clients.