Most lenders have a decent handle on their data—at least, the data they know. However, many lenders struggle with accessing additional data that can help them get more loans and increase penetration in certain loan products. And because current data is frequently siloed, lenders who are unable to “read between the lines” of the data miss out on the ability to identify tangible outcomes.
At Cornerstone, we find that lenders who struggle to translate data models into quantifiable loan growth typically reside in two different camps. Lenders in the first camp have a decent grasp on data for the “pulse of their business” (e.g., application and funding counts, pull-through rates, Net Promoter Score), but that’s it.
Those in the second camp – and this is the minority – have much more granular performance metrics (e.g., turn-times by process phase, product NPS segmented by assigned employees and customer tenure, number of file touches by underwriting) with monthly and year-over-year trends.
But here’s the rub: Even lenders with access to granular data are using lagging performance information that does nothing to help them dial into loan growth or improve customer engagement. For example: during the mortgage origination process, can the institution determine whether a borrower is digitally engaged and if that engagement has any positive correlation?
The difficulty is that the meaning of “digitally engaged” can vary from lender to lender, and most are unable to track metrics related to digital engagement during the loan process even if they want to.
Creating manageable and usable data sets with actionable key performance indicators is key to helping lenders improve performance and grow loans. Successful targeted marketing depends on meaningful data, data that provides insight into variations in performance across diverse customer segmentations and generational demographics.
Here are a few examples to illustrate where data can add value:
If you know that Gen X customers are four times more likely to be digital customers versus traditionalists (i.e., relying heavily on branch and non-electronic means of support) and they have higher credit scores and three times the credit card penetration rate of Baby Boomer customers, you could generate an online, pre-approval campaign targeted specifically to borrowers who currently don’t have a credit card.
Imagine you discover that customers who have been banking with you for less than one year are 80% less likely to have multiple products but also have five times less in total deposits and are 40% less likely to have a mortgage. You can combine this with credit score and digital preference data to develop targeted mortgage or deposit campaigns that will best get these customers’ attention.
Consider if you were able to identify a segment of customers with consistently increasing credit scores who have auto loans at other financial institutions with higher-than-market interest rates. Triggering a pre-approved campaign directly through these borrowers’ preferred method of contact for a loan that could significantly minimize their monthly payment would likely have a high conversion rate and go a long way toward cementing them as long-term customers.
Effectively mining and using data can be highly unique from one organization to the next. But to execute on this, lenders must ensure they have access to the right information and are both viewing and presenting it in a way that provides context between seemingly unrelated data points. In this way, they can identify strategically actionable opportunities.
Being able to answer specific questions can help turn smart lenders into smarter lenders with a winning data analytics game. We suggest starting with these:
1. How many customers use you as their primary financial institution and what are their loan penetration rates?
2. What is the ratio of customers who use digital versus traditional channels and what are their loan penetration rates?
3. Can you identify declining engagement (e.g., checking, cash management) in your best loan customers?
4. Can you see loan payments to other financial institutions to target potential loan offers?
5. What loan products are different borrower demographics (e.g., Baby Boomer, Gen X) most likely to have and how do they tend to find you?
6. What correlation do you see between average customer tenure and the type and number of loan products they hold?
7. Can you monitor ongoing improvements in customer credit scores and potential product opportunities and trigger real-time, targeted cross-sell campaigns?
In short, 1) identify customer segments (“personas”) with a high degree of correlation, 2) determine the attributes that make them good customers, 3) target the same customer personas who are missing those same key products, and 4) overlay that approach with targeted outreach in each borrower’s preferred method of contact.
To enhance lending and better leverage data, lenders must first understand the data. And to understand the data, they must first be able to get to it. Many lenders struggle with this due to system constraints or a fragmented data strategy.
For lenders who can figure out how to obtain accurate data, analyze it correctly and then queue up the right offer at the right time to the right customer, the opportunities are limitless.