Most institutions interested in expanding business loans to customers and prospects lament the fact that they lack the ideal complement of top tier bankers. That leaves them with two options:
- Wait until the bank assembles the ideal sales force, a process which can take years.
- Deploy marketing approaches that can both directly drive new originations and improve sales force effectiveness.
Let’s look at the second option: tactical marketing tools that enable precision targeting and tailoring of marketing stimulus and sales calls. This gives banks the ability to:
- Stimulate businesses to independently seek out the bank to establish profitable new banking relationships
- Improve the sales effectiveness of bankers of all skill levels.
The success of campaigns and lead lists allows us to provide a business case for what bottom-line impact will be achieved quarter by quarter. Our clients typically enjoy fully loaded payback in 6 to 8 months, with healthy returns on their marketing investments.
Why don’t more banks employ this approach? The analytics and deployment processes required for success are not easy to build and perfect. It took us years, and after scrutinizing and refining hundreds of campaigns, here’s what we’ve learned:
- Using just one of the well-known third-party providers of business data is not sufficient. We find that it is surprisingly suboptimal relative to what can be accomplished if one invests the effort to assemble 15+ sources of data/insights on businesses
- The learning curve to optimize algorithms that operate on the data is steep and the benefit of having executed and analyzed hundreds of campaigns is huge
- The benefits of tailoring direct mail messaging and call follow-up to various business segments is often underappreciated and rarely used. The results of our years of champion / challenger testing demonstrate how this can increase returns substantially.
One Data Set Is Not Enough
A complete picture that enables an understanding of each business’ needs must be built from many different data resources.
The campaign returns our clients have achieved were fueled by more than 15 separate 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, average industry balance sheet information, and more.
However, this information by itself is unactionable unless there is a process that marries all the data streams together. Here it is necessary to deploy an algorithm flexible enough to identify similarities using a multitude of information: legal business names, trade names, executive information, addresses, phone numbers, and other data. Our proprietary matching algorithm pulls each source together into a comprehensive data library.
3 Characteristics Defining the Target Audience
After executing and examining hundreds of campaigns and thousands of business responders, three key considerations have become clear:
- Selecting businesses with an acceptable risk profile
- Targeting businesses with an appetite to borrow
- Identifying businesses with a propensity 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. Third-party data elements are available that calculate 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’s holdings. To illustrate, a commercial center with a restaurant, medical office, and law firm cannot be accurately characterized by one, area-level business lending model.
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 test-and-learn investments until an adequately sized, representative sample can be gathered.
Execute With Effective Direct Marketing And Follow-up With Informed Sales Calls
Stimulating branch traffic and inbound calls requires getting the direct mail 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 direct mail pieces to non-decision makers, or use ineffective creative formats that won’t get noticed, or employ generalized messaging that completely misses the hot button issues that drive response.
After hundreds of tests, we’ve found that certain combinations of direct mail materials, sizes, and formats “cut through the clutter,” and that certain messaging techniques 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 field with comprehensive information about the businesses targeted increases the engagement of business bankers and significantly improves sales success. Providing extended firmographic and credit usage information derived from a comprehensive data library enables bankers to engage in informed conversations with their targets. Additionally, understanding the current situation of the business, e.g., cash rich or credit needy, as well as its borrowing history and recent loan demand, allows bankers to enter the conversation better prepared to discuss relevant products, offers and solutions.