Most institutions interested in expanding business loans to customers and prospects lament the fact that they lack the ideal complement of top tier bankers. Too often, many of their existing bankers exhibit mixed effectiveness.
There are two options: wait until one assembles the ideal banker force, a process which can take years, or deploy analytical direct marketing approaches to drive qualified business lending leads to the sales force. The second option, based on our experience, has the proven ability to stimulate businesses to independently seek out the bank to establish profitable new banking relationships and improve the sales effectiveness of bankers of all skill levels. Properly executed, these precisely-targeted campaigns and sales lists generate fully-loaded payback in less than six months.
Why is this approach not being broadly employed? The answer is the analytics and deployment processes required for success are not easy to assemble and perfect. We know. It took us years, during which we benefited from continued refinements enabled by scrutinizing results from hundreds of campaigns. Here are some lessons learned:
Using just one of the well-known third-party providers of business data is not sufficient. The campaign returns we’ve achieved in partnership with our clients have been powered by more than 15 different third-party data sources, each contributing different insights into individual business activity. Key sources include not only the largest business data aggregators, but also specialized credit and industry information such as recent business credit inquiries and activity, inferred risk indicators, SBA loan holdings, UCC data filings, commercial mortgage data, business owner lists, import/export trade filings and average industry balance sheet information.
However, this information by itself is un-actionable unless there is a process to marry each source together. Here, it is necessary to employ an algorithm flexible enough to identify similarities using a multitude of information such as legal business names, trade names, executive information, addresses and phone numbers. We use a proprietary matching algorithm based on advanced bioinformatics to pull each source together into a comprehensive data library.
Three characteristics define the target audience. The learning curve to optimize the algorithms is steep and the benefit of having executed and analyzed hundreds of campaigns is huge. Three straightforward considerations are key: selecting businesses with an acceptable risk profile; targeting businesses with an appetite to borrow; and identifying businesses with a high likelihood to respond.
Of course, the trick is being able to predict a business’ situation across these three factors. By comparing business credit activity and payment history across sources and by leveraging third-party risk indicators, banks can eliminate businesses unlikely to be approved during underwriting.
Businesses in the remaining universe can then be evaluated for their potential to borrow. Credit indicators and usage are helpful although incomplete due to the fragmented nature of business credit reporting. To complement this information, third-party data elements are available that predict how much each business has borrowed across all loan and line accounts. The most effective predictors are available at the business level, versus less precise area-level scores that can misrepresent an individual business’ holdings. To illustrate, a commercial center with a restaurant, medical office and law firm cannot be accurately characterized by one area-level business lending estimate.
Finally, a propensity model, which should be specific to the product being offered (e.g., commercial mortgage, term loan), narrows the universe to only the businesses most likely to profitably respond. Developing an effective model requires significant investments in test-and-learn until an adequately sized, representative sample can be gathered.
Execute with effective direct marketing collateral and follow up with informed sales calls. Stimulating branch traffic and inbound calls requires getting the direct marketing collateral (most often direct mail, in our experience) to the right person in the business, increasing the odds that it’s opened and then presenting a message that resonates. Many campaigns fail because they address the mail to non-decision makers, use creative formats that are unlikely to be noticed, or employ generalized messaging that completely misses the “hot button” issues that drive response.
In our tests, we’ve found that certain combinations of direct mail materials, sizes, and formats “cut through the clutter” of the mail stack and that certain messaging techniques, such as discussing solutions rather than products, are much more effective at driving proactive response by recipients.
When there are resources available to make follow-up sales calls on the direct mail, arming the bankers with comprehensive information about the businesses targeted increases engagement and significantly improves sales success. Providing extended company-specific credit usage information derived from a comprehensive data library enables business bankers to engage in informed conversations with their targets. Additionally, understanding the current situation of the business – for example, cash rich or credit needy – as well as its borrowing history and recent loan demand, enables bankers to enter the conversation better prepared to discuss relevant products, offers and solutions.
Mr. Lazzaretti is vice president with New York City-based FMCG Direct, a subsidiary of First Manhattan Consulting Group. He can be reached at email@example.com.