Historical sales methods in commercial banking—those rooted in selling through relationships and networking with centers of influence—are proving no match in today’s economic environment. Few banks possess a methodical plan to expand, repopulate, curate, and filter the network on an ongoing basis to ensure ample and effective referrals. The financial results—historically low win rates, sporadic cross-sale success, and in many cases heightened levels of sales personnel attrition—are proving this.
The need for banks to adopt a standardized sales methodology to unlock the magnitude of their organization is apparent. Without a consistent and data-driven methodology, sales efforts aren’t repeatable or scalable, proving both costly and ineffective in business development. As banks grow inorganically, these challenges become compounded and further complicate growth.
Now more than ever, a scalable sales methodology is required to effectively identify, pursue, and sell to targeted, existing, and new prospective clients.
In the context of commercial sales, data and analytics (DnA) is the process in which we examine and analyze raw data to glean insights and draw conclusions that enable us to predict certain outcomes with higher probability.
Banks often believe that DnA exists within their sales programs, pointing to data in pipelines or tracked call activity for each banker. In reality, these are simply basic data points with little to no insight extracted to drive focus, accelerate activity, or close net new business.
For many financial institutions, DnA often exists first as data housed in disparate systems, typically accessible with special extracts from the bank’s core system, data warehouse, or its CRM tool.
More mature DnA capabilities allow banks to shift from manual examination and analysis to automated insights based on stratified portfolios, application of probability theories, or automation for assigning sales tasks within relationship sales plans based upon opportunity type and target close date.
How does your bank currently utilize DnA? Here are a few questions to ask:
If you answered “no” to any of these initial diagnostic questions, there are a few ways to pivot and find scalability in your DnA capabilities. We often advise clients to start with a more defined sales methodology, rooted in optimizing activity around the highest-value, highest-probability targets. To take this methodology further, we suggest using DnA to refine your segmentation and hone investments for the highest probability opportunities.
To begin exploring DnA within your sales organization, consider assessing these five key areas and how DnA drives revenue maximization:
Banks are notorious for segmenting customers based on loan size or Aggregate Credit Exposure (ACE). Instead of a traditional segmentation process, DnA-powered segmentation creates a new approach in two critical ways.
Like customer segmentation, DnA allows banks to expand beyond traditional segmentation criteria (geo and industry codes) and include key accounts, accounts with high levels of future opportunity, and prospects of significant value, all of which have varying opportunity levels and revenue impact. By incorporating DnA and curation criteria that aligns product opportunities by industry, bankers may begin focusing efforts on a targeted basis.
While commercial banks manage pipelines in many ways, most fail to drive insight and improve the sales conversation rate. By harnessing insights and trends across the pipeline, sales leaders can push and pull on opportunities that require attention to reach the finish line.
With advanced pipeline data and management, banks have found greater success, accelerated time to closing (e.g., shorter sales cycle), and experienced a significantly greater conversion ratio (e.g., closed-won ratio). With DnA and insights stemming from pipeline management, banks can leverage and deploy this process across markets, enabling scalability and accelerated growth. Each organization must identify the insights most important to their teams. That may include:
Initiate better cross-selling: By combining layers of data, banks can also correlate products commonly sold in tandem or even measure the duration between cross-sales which leads to an actionable road map for cross-selling in the near future. In addition, a robust tool will automatically conduct this exercise for the sales team, leaving no chance for miss or error by the banker.
When coupled with a robust CRM tool that is built for analytics, sales teams will be able glean insight and deliver expert-level managerial sales coaching to bankers.
Many banks price loans according to a rate sheet or evaluate profitability through the loan spread and corresponding revenue at the loan level. Unfortunately, this myopic view fails to integrate the full value of the relationship to the bank both from a historical perspective and in consideration of future opportunities. As banks begin to consider the value of a relationship, which is significantly different than loan revenue, banks are seeking ways to assess full relationship profitability by aggregating disparate data into a model to assess profit on multiple levels.
Sales organizations whose methodology is infused with data and analytics stand to drive more focused teams and scale their growth. Imagine a sales ecosystem, enabled by DnA, that could streamline, automate, and standardize sales activities. That type of environment would have the ability to provide insight to drive leading sales behaviors, monitor and manage through transparency and accountability, and create the foundation for scalability, all of which drives accelerated growth.
We’ve written before about the need to adopt a standardized sales methodology. To bring those methodologies to the next level, analyze data and analytics maturity—considering the variables listed above to form an accurate understanding of where your bank stands and which areas it must invest in to extract the full potential of its sales capabilities and effectiveness.