Diagnosing your bank’s sales data and analytics maturity

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. 

Diagnosing your commercial sales DnA maturity 

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: 

• At the outset, do you understand who your segments are, what their priorities are, and why they are what they are? 
• Do you leverage multiple data points and criteria beyond loan size and profitability to segment customers and prospects? Do you consider the customer lifetime value (CLV) and the probability of conversion to close in the next 12 months? 
• Do your sales and relationship development plans for key accounts include comparisons to products sold within a comparable peer group of customers based upon industry or location? 
• Will your CRM system automatically populate a sales pre-call plan with targeted, solutions-based selling questions aimed to better understand the customer’s needs, ultimately leading to a higher quality sales call and accelerating duration to close? 
• How many sales calls from the prior month were focused on high-opportunity clients with new sales opportunities as opposed to the summation of your medium- and low-value clients?  
• Is the relationship manager’s sales activity aligned with opportunities having the highest product conversion rate within the team or even the entire organization?
• Does your dashboard highlight the sales status for closing cross-sell opportunities in your top-3 high-opportunity clients?  

Ways to find scalability and DnA in your bank’s sales programs   

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:   

• DnA powers prioritization and segmentation  
• DnA drives scalability in prospecting and repeatability in new markets  
• DnA can reinvent pipeline management  
• DnA provides powerful insight into sales behaviors and lifts revenue 
• DnA drives profitability improvement and sustainment 
• DnA powers prioritization and segmentation  

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. 

• Prioritization: DnA can help identify and categorize customers in terms of future sales opportunities and CLV. Banks often overlook the fact that once the customers has “purchased” the loan, they likely won’t purchase another similar loan for some time. Thus, DnA identifying which customers might purchase different types of loans or cross-sell opportunities for additional products can yield greater sales activity for a banker. 
• Activation: With the scalability of CRM technology embedded in the process, banks can create meaningful, tactical sales relationship plans that convert early stage discussions to pipeline opportunity, followed by automatic integration to a task engine that prompts and assigns to bankers’ sales activities at critical moments in the sales incubation process. 

Key Considerations

• How many total products/categories exist in the commercial banking product set?
• How many products have you sold to each portfolio client?
• What was the average wallet share gain per client last year?
• Is your CRM system smart? Meaning does your CRM system automatically generate and assign sales tasks to RMs when an RM enters a new opportunity? 

DnA drives scalability in prospecting and repeatability in new markets   

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.

• Enhanced marketing: DnA enables marketing support throughout business development campaigns to drive strategic messaging and awareness to the stratified portfolios and prospects, whether directly or indirectly through trade, advertising, or thought leadership publications. 
• Scaling referrals: Referrals in commercial banking are the most common way for acquiring net new customers. By leveraging a robust CRM platform and DnA tool, banks can harness the reciprocal nature of referrals between commercial bankers, accountants, and attorneys.

Key Considerations

• What is the ration of business awarded to a vendor, law firm, and business received from the law firm?   
• To achieve prospecting scalability, has the bank established criteria to curate and prioritize prospects?
• Is there strategic directive to pursue new prospects vs. selling deeper into existing relationships?
• What was the won-closed ratio for the bank’s most recently completed sales campaign? 

DnA can reinvent pipeline management 

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:

• Enhanced transparency and accountability: Technology that sends notifications to the stakeholders when sales stage (e.g., lead, opportunity, qualification, proposal, etc.) exceeds standardized durations.  In addition, managers have the ability to create coaching moments and assist bankers in advancing a new opportunity that might otherwise have stalled and moved to closed-lost outcome.

• 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. 

Key Considerations

• Does your pipeline report provide a list of opportunities, close dates, and probabilities?
• Does your pipeline report include insightful data such a sales stage duration?
• How does your bank create transparency regarding underperforming sales results?
• Does your CRM system automatically predict possible cross-sell opportunities based on existing products sold, borrower industry, or revenue size? 

DnA provides powerful insight into sales behaviors and lifts revenue 

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. 

• High functioning sales teams: Leading practices assess sales call activity as linked to relationship development plans, opportunities sales stages and monitored against the duration an opportunity resides within a sales stage. One way in which DnA can help is to analyze trends across types of calls using the entity type called upon and actual detail in the post-call debrief.  Scalability happens as automation can be leveraged to drive this insight enabling sales leaders to manage a greater number of personnel than traditionally assigned in the past, thus reducing and combining the number of teams into one larger sales team.   
• Revenue lift: When built within a digital CRM tool, DnA leads to greater sales effectiveness and improved sales results. In our experience, we expect as much as 25% revenue lift driven by a 50% increase in wallet product share per key customer relationship. 

Key Considerations

• How does the bank define a sales call?  Does it delineate between a sales and service call? What constitutes a high-quality sales call?
• What is the most noticeable trend in sales call activity at the bank and did your CRM system automatically provide that trend insight?
• Has your bank leveraged DnA insights to reduce sales manager administration time, thus freeing capacity and creating the ability to optimize the organizational structure?
• In what ways do your sales managers use sales call data and specifically call notes to coach during skill building sessions with RMs? 

DnA drives profitability improvement and sustainment  

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. 

• Profitability and sustainability: Relationship profitability strategies are emerging as a newer innovation, particularly with strategic renewal-based pricing that considers all interest and fee income sold, especially through cross-sale gains from within the commercial bank as well as additional revenue and profit created through Product Partner cross sales in Wealth Management or Consumer Banking, often to owners and guarantors of the business.

Key Considerations

• What is the average gross profitability of a relationship in the bank’s portfolio?
• Does the bank measure profitability at the loan, relationship or household level?
• Does the bank effectively aggregate disparate data from the bank’s core loan system, treasury management fee system, and other bank databases or systems within capital markets, consumer banking, private banking and even wealth management to create a comprehensive profitability view? 

Conclusion 

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.