4 Goal Setting Methods to Identify Untapped Opportunities

If you operate a financial services organization, you want your individual branches to perform at their best.

Historically, banks and credit unions have used a variety of methods to determine performance targets for each branch location. Each of these goal-setting methods has its advantages, but none provide a complete picture of opportunity.

Today, with access to richer data and pre-linked business and location insights from solutions like Precisely Data Link for Dun & Bradstreet, financial institutions can go beyond traditional approaches to uncover hidden market potential and make more confident, data-driven decisions.

By using industry-leading datasets and analytical techniques, you can overcome those limitations through an approach called “opportunity-based goal-setting.” Simply put, that means evaluating each branch’s unique market dynamics, competitive environment, and facility characteristics to set attainable sales targets for each branch in the network.

This approach builds on proven goal-setting methods but takes them further – using data-driven insight to help your organization find untapped opportunities and set goals that truly reflect each branch’s potential.

Legacy Goal-Setting Methods: What’s Worked, and What’s Missing

Before exploring some best practices in creating an opportunity-based approach, let’s review some of the legacy methods for developing branch performance targets:

• Uniform goal-setting applies the same percentage increase goal to each branch. For example, if the Finance/Product teams need home equity revenue to increase 10% in the coming year, each branch would receive a 10% increase in their home equity target.
  • Rewards: Branches in growing, dynamic markets as more opportunity is readily available
  • Challenges: Branches in stable or declining markets as fewer opportunities exist to increase performance
• Historical goal-setting is driven by a simple uplift based on last year’s branch performance. Branches might simply be given targets to exceed the previous year’s numbers by 10%.
  • Rewards: Low-performing branches, as they won’t be asked to increase their performance
  • Challenges: High-performing branches, as it asks them to continue to perform at a high level
• “Total wallet” goal-setting allocates performance targets based on the market opportunity in each geographic area. This comes closer to being an equitable approach by incorporating market-based data, but it still falls short because it doesn’t take into account the competitive environment for each branch.
  • Rewards: Branches in less competitive markets, where this sets an artificially low bar
  • Challenges: Branches in highly competitive markets, where targets can be unrealistically high.

Each of these approaches has its own particular strengths and weaknesses. At Precisely, we’ve found there’s a better way – we call it “opportunity-based” goal-setting.

Keys to Understanding Opportunity

The opportunity-based model is data-driven. It’s built on a more sophisticated view of the factors that contribute to potential branch performance. In the opportunity-based model, we focus on several key methods to get a better understanding of market potential.

Define the Trade Area

First, it’s important to clearly define the playing field on which each branch competes. This is typically the area that encompasses 65% to 70% of a branch’s customers.

We start the process by using customer data as a foundation, looking at households and household balances in each block group surrounding the branch. Keep in mind that trade areas should be created separately for consumer households and small businesses, as they usually differ.

New and commuter branches are typically excluded from the traditional definition of the trade area. In the former case, there’s limited data to analyze, and in the latter, the catchment area is simply defined very differently than for most other branches.

Understand the Market

Trade area demographics

Once trade areas are defined, it’s time to look more closely at the people and businesses within them.

Consumer data starts with household turnover, then incorporates over 100 key variables including age, income, and home value. Business data is used to understand revenue size and industry type.

Now, financial institutions can take that analysis even further. With Data Link for Dun & Bradstreet, teams can easily combine trusted business intelligence with detailed geographic context — helping you identify underserved markets, evaluate regional investment conditions, and understand the stability of nearby businesses.

This pre-linked data helps analysts move faster, reduce manual data prep, and deliver richer insights into both customer and market potential.

Product demand

Equipped with data about the trade area demographics, you can explore potential product demand within the targeted geography. This involves combining demographic and behavioral data using over 200 individual data points for each individual or business in the area, along with purchasing and usage behavior for millions of banking and credit union households.

That combination gives you a clear “total wallet” view of accounts and balances within the individual branch’s trade area.

Behavioral segmentation can be especially valuable in delivering extra clarity at this stage in the process, helping distinguish digital-only customers from those who prefer in-person service, or customers who bank near their workplace versus closer to home.