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Capturing Share of SME Revenue Potential: What’s in Your Model?

In the race for growth in small business banking, many regional banks are operating with broken compasses. Improved analytic tools are needed for prospecting and cross-sell.

As regional banking companies consider the growth outlook going into next year, a pressing question is how to improve competitive stance in the banking market for small and medium-size enterprises (SMEs). Squeezed on the one side by the national banks, with their powerful brands and mega-networks, and on the other side by community banks, with their deep local ties, regional banks have been in retreat.

In various markets nationwide, our research indicates that regionals’ share of local SME customer relationships often is 30% lower than the potential indicated by their share of local branches. Worse, regionals often show a 50% under-performance in capturing SME deposit balances, relative to local presence. Meanwhile, national and community bank players are chewing up the market, selling way beyond the potential indicated by their local footprints.

In considering the options to respond, one essential for regionals is to sharply improve the precision of their marketing and sales efforts. The goal is to leverage scarce resources and also boost the odds for marketing and sales success, via specific preparation to pitch those products and services that best align with the current and anticipated needs of target customers.

But when it comes to understanding SME customer needs and revenue potential, many regional banks are operating with broken compasses — crude models that stack up small businesses on basic firmographics, such as industry sector and annual sales, and then try to extrapolate the banking opportunity. Typically lacking in specifics about the type and magnitude of likely product needs for each target company, these models are starved for actionable detail.

As a result, marketing teams are handicapped in generating targeted messages based on specific client needs and sales potential. Meanwhile sales teams have precious little guidance on what to emphasize during the fleeting moments of face time with clients and prospects. Because of these analytical shortcomings, regionals typically are under-utilizing between 20% and 30% of their SME marketing and sales resources today.

The situation highlights a clear need for precision revenue modeling. This includes total estimated banking needs and associated revenues; the probability and associated value of serving a greater portion of those needs; and guidance on the specific products and services to emphasize with each client and prospect.

The idea is that by setting customer priorities based on a combination of high-value needs and high purchase propensity, the bank can guide SME marketing and sales activities on the basis of growth potential, instead of today’s typical “guesstimates” based on rough guidelines. Some regionals are already on this path; others will likely follow, given the widespread need to improve competitiveness in the SME space.

Customer Engagement
Precision revenue modeling is an important piece of a larger puzzle in SME banking — customer engagement. It is impractical to deploy an army of bankers to pursue the full sales opportunity in small business banking, not only because the market is fractionated across a vast number of small-dollar opportunities, but also because digital channels now must be harnessed in reaching the modern multi-channel customer (who often eschews the branch).

Instead of a high-touch relationship model, as seen in middle-market commercial banking, SME banking success increasingly will depend on a multi-channel customer engagement model that relies more on analytical smarts. This outreach blends data-driven insights, marketing technology and CRM platform tools to coordinate marketing and sales across the full spectrum of company sizes, types and channel usage patterns.

Fig_1_Applications_of_Precision_Revenue_Modeling

Precision revenue modeling is essential in this framework (Figure 1: Applications of Precision Revenue Modeling). As the bank progresses in applying data and analytics, it is positioned to pursue at least six types of revenue opportunities in SME banking:

Deposit expansion. Among businesses with less than $5 million in annual sales, most emphasize checking in their first two years with a new bank. Yet there are significant growth opportunities via balance expansion. Our research indicates that about three-fourths of the newcomers have the potential for incremental deposit balance growth of at least 10% in the early going — an important opportunity to capture stable, low-cost deposits.

Ancillary services. While under-$5 million companies present fewer lending opportunities, there is substantial cross-sell potential with treasury management and merchant services over the life of the customer relationship.

Credit cross-sell. Larger businesses with $5 million to $10 million in sales are far more likely to borrow, both initially and within the first two years of a new banking relationship. More than 20% of these clients begin a new banking relationship on the lending side, and nearly a third are borrowing from the bank within 24 months of the onset of the banking relationship. It is important to understand latent demand to guide marketing and sales.

Cross-sell to SME households. In the typical consumer customer base, up to 8% are small business owners who do not yet have a commercial relationship with the bank, representing a prime cross-sell opportunity. An additional 7% of consumers own businesses and already have dual retail/commercial relationships — prime candidates for relationship expansion.

Client prioritization. The use of relationship managers can be fruitful in pursuing cross-sell with larger established clients, but RMs need to be selective in how they use their time. Some accounts are strong but offer little further expansion opportunity; others much more so. Where is the “sales headroom” within the current customer base?

Prospect prioritization. For new-to-bank SME customer acquisition, the bank should be able to rank the potential across all markets served, then drill into a given geography to identify key prospects and their likely specific needs for various banking products and services. Typical lead generation lists do not meet this need.

Modeling Traction
A robust revenue model requires substantial care and feeding. To succeed, it must be able to compile salient information on virtually all companies within a potential multi-regional service footprint, not only general company traits but also specific breakouts on banking and financial services usage. This includes loans, deposits, treasury management, merchant services and other products (e.g. equipment financing).

Some of the data can be acquired from national business databases. Other inputs are based on an extrapolation of survey findings to a broader population. A third major input stems from analysis of the bank’s current clients, again permitting an extrapolation of findings to a broader population (with limitations, since internal information does not reflect banking business done elsewhere). These elements require careful quality control, integration, validation and ongoing replenishment. The model outputs also require careful calibration and testing.

Because of the complexities and expense, banks almost never attempt precision revenue modeling on their own. Most struggle along with imprecise lists, with priorities based on loose inferences about likely banking needs relative to company type and size. But even when turning to a sophisticated third-party solution, the bank still needs the right management and process framework to take full advantage.

Cross-sell. For cross-sell and relationship expansion, the process begins with a scan and modeling of the current small business customer base to identify clients with the kind of high-value composite needs that provide “headroom” to develop larger banking relationships (this step is primarily quantitative).

The analysis is usually managed and executed centrally, typically by an analytics, finance or marketing function supporting small business. Along with generating revenue model scores by product, the analysis draws on statistical regression to identify the correlated characteristics of small business clients which are likely to expand their relationships over time. Also there is a review and evaluation of the overall criteria used to qualify relationships as cross-sell ready.

The output is a quantitative, per-customer ranking of cross-sell potential, tiered at the individual market level. Then the responsibility goes to the various local marketing and sales teams within the network footprint. Their collaboration is crucial.
Patterns of specific product needs must be gleaned from the global ranking, and marketing will want to consider correlations with other client attributes (e.g. industry sector, length and current depth of relationship, channel usage and touchpoints, etc.).

Meanwhile the sales team must provide essential feedback on specific clients, and on the realistic conversion possibilities based on their daily experiences in the market.

As permitted by revenue modeling, all of this preparation positions the bank for a series of market-by-market cross-sell initiatives, with sales goals and responsibilities allocated among bankers, branches and across digital channels as appropriate.

Customer acquisition. Many of the steps are similar in this application of revenue modeling. A quantitative master ranking of SME prospects can be generated and tiered locally, then progress depends on targeted multi-channel campaigns. Sales teams typically find that the leads are much more useful than the simpler, firmographic-based prospecting lists commonly in use today.

As an example of how these concepts are put to work, one community bank in the Southeast invested in revenue modeling in a bid to reverse an anemic trend in customer acquisition and shift the portfolio mix to higher-value relationships. Customer acquisition and cross-sell goals informed the design of a series of targeted multi-channel campaigns.

Based on modeling guidance on high-potential clients and prospects, the initiative was coordinated across e-mail, direct mail, community events, a calling program and on-the-ground sales efforts. The marketing and sales emphasis was centered on a re-launched small business checking product and suite of small business services. In the first two years of the program, the bank has seen hefty double-digit annual increases in small business checking and deposit balances, plus an improvement in the quality of newly acquired relationships.

Flying Blind?
For many regional banks today, the small business problem comes down to “so much opportunity, so little knowledge.” It is unacceptable that local share of SME client relationships and deposit balances widely lags share of local branches. Yet in attempting to respond, players are relying on crude data and analytic tools that actually contribute to the problem. Among the shortcomings of off-the-shelf or home-grown wallet models today:

  • They are generally inaccurate, providing only “guesstimates” of relationship revenue potential based on industry sector and annual sales.
  • They provide only vague lead lists, insufficient for targeted marketing initiatives based on specific, high-value customer needs, providing nothing that helps to advance the sales dialogue with individual clients and prospects.

Correcting these deficiencies is vital in the overall push to a more analytically-driven outreach in small business banking. What’s in your model?

Tony Coretto is a Managing Director in the New York office of Novantas Inc. He can be reached at tcoretto@novantas.com.

For more information, contact Novantas Marketing

+1 (212) 953-4444