As we emerge from turbulent economic times, the desire to return to sustained portfolio growth has been a clear trend in mature credit markets.
Prior to the economic downturn, credit unions and other financial institutions met this objective with aggressive marketing offers to attract new members and customers through loan acquisitions.
Today, however, they’re increasingly looking to deepen their relationships with existing customers in order to better serve them.
By identifying the needs of current customers and matching them to individual credit risk and affordability, effective cross-sell strategies can help lenders achieve portfolio growth while simultaneously increasing customer satisfaction and promoting loyalty.
This, in turn, can lead to deeper and more valuable customer relationships.
The need to optimize customer touch points and provide the best possible customer experience is paramount to future performance, as measured by market share, customer satisfaction, and long-term profitability.
For example, the more of your products a customer uses, the less likely this person is to leave you for a competitor. Also, by responding rapidly to changing customer credit needs, you can build trust, increase wallet share, and profitably grow your loan portfolios.
Although the benefits to enhancing existing relationships are clear, implementing a successful cross-sell strategy involves several challenges:
In addition to these issues, few financial institutions have lending strategies that differentiate between new and current customers. Most of the time, new credit requests are processed identically for both.
The problem with this approach: If fails to capture and use the power of existing customer data, leading to suboptimal decisions.
There are several components to successful cross-selling:
1. Evaluate credit risk. The ability to accurately predict a consumer’s default risk is paramount. The most successful strategies balance the predictive power of data for existing customers with data from third parties (credit reporting agencies) to feed predictive models.
These models rely on an appropriate definition of a customer. At first glance, this may appear simple. But the definition of a customer becomes more complex as you consider joint or multiple account holders or customers with both a consumer and a small-business relationship.
Effective strategies bring these data sources together in an optimal manner to feed predictive models of credit default risk.
2. Calculate customer affordability. The capacity to absorb increased credit commitments affordably is both a key component of the solution and a legal requirement. Approaches in this area have developed significantly in recent years.
Successful strategies incorporate a mechanism for capturing consumer income and deriving their disposable income from this information. Disposable income incorporates expected monthly expenditures (bills, food, tuition rent, insurance, gas) with the customers’ known credit commitments (mortgage, cards, car loans, credit lines) to derive disposable income.
3. Offer the right products. By combining the evaluation of credit risk with the calculation of affordability, customer-level credit limits can be used to set maximum thresholds for each customer holistically. These customer limits can be apportioned among different product families (revolving credit, fixed-term loans).
The comparison of maximum customer limits with existing commitments allows residual lending limits to be calculated, forming the basis of the cross-sell offer.
4. Effective operational deployment. The decision elements used to create the c-sell offer are positioned in a user interface for branch managers of agents. This mechanism permits the branch manager to identify a credit amount for which the existing customer has been preauthorized.
As such, credit requests for new customers can be fast-tracked, resulting in improved servicing times and enhanced customer experience.
As with any effective strategy, the ability to deploy in a test-and-learn environment is key to successful implementations.
By continually testing new approaches for the components of the solution and monitoring their success, organizations can continually improve and learn, drive portfolio growth, and enhance the customer experience, leading to greater loyalty and, ultimately, greater profitability.