Leveraging Data-Driven Analytics for Branch Network Goal-Setting

Setting goals across a branch network in a financial services organization can be a challenge. Historically, banks have taken various approaches to setting performance targets for branches, each of which has some shortcomings. Data-driven analytics opens the door to overcome those deficiencies with a new approach called “opportunity-based goal setting.”

Almost everyone is familiar with the scenario in which annual targets are driven primarily by a percentage increase over the prior year’s performance. This is referred to as the “historical” approach to goal setting. It tends to be challenging for top performers, who must constantly build upon last year’s high levels of achievement to reach even higher numbers. At the same time, it discounts the potential upside to be found among a low-performing branch network. At worst, it’s the sandbagging effect, repeated every year by the low performers in an organization.

Consider an alternative, in which goals are uniformly or proportionally allocated across all branches within an organization. For managers that operate a busy branch and dynamic market, that tends to make life easier; whereas branches in highly stable markets are challenged to keep meeting their targets.

There are other organizations that allocate corporate goals to a branch network based on the calculated size of the market opportunity for the areas that they serve. This is getting closer to the mark, but it tends to be challenging for branches located in highly competitive markets, and it results in artificially low targets in markets with little competition.

Unfortunately, all of these approaches have some weaknesses because they fail to consider all of the variables that could potentially impact branch network performance, such as the dynamics of the local market and the prevalence of competitors near the branch location.

A better approach to goal setting

At Precisely, we believe there is a better approach to setting performance goals for branches. We call it “opportunity-based” goal setting, and it’s driven by a more sophisticated view of the branch, its location, its potential customers, and even current events such as the adverse effects of the COVID-19 pandemic.

This is a data-driven analytics approach that incorporates historical performance data, branch characteristics, detailed demographic data, information about competitor locations, and more. The  data-driven analytics approach is made possible by several key technologies:

High-performance data analytics

Business leaders have access to a greater volume of data than ever before. Fortunately, data analytics tools have kept pace with this explosion of new information, making it possible to gain valuable insights about customers, vendors, products, and more.

Data enrichment

The combination of internal corporate data with externally sourced information creates a situation in which the whole is more valuable than the sum of its parts. Data enrichment provides opportunities to understand a specific business domain with greater depth and nuance. It adds new dimensions to business leaders’ understanding of customers, branches, products, and services.

Location intelligence

Finally, location intelligence provides business leaders with visibility to a profoundly important facet of virtually everything that concerns the business. Location intelligence helps banks to better understand competitors’ locations, traffic, and nearby points of interest. Location intelligence brings context. Business leaders can get a detailed view of the demographics of the areas they serve, including information like household income, home ownership, lifestyle, purchasing power, and financial stress.

Data integrity is strategic for financial services firms. Maintaining accurate, consistent, and contextual data helps with offensive and defensive strategies.  The offensive value of data integrity is in delivering growth, profitability, productivity, and other facets of fresh business value.  Read this TDWI checklist report to explore how data integrity best practices can help your financial services organization succeed.

Turning data into business performance

More data means that financial institutions have an opportunity to set branch targets with greater precision, that is, to set smarter goals. With our Perform360 product, we help financial services customers to model branch performance and to set minimum, maximum, and mean benchmarks for individual branches derived from a rich array of data.

With that information in hand, it becomes possible to evaluate branch performance in a new way. Instead of comparing achievements to last year’s numbers (which might have been low to begin with) or to other branches (which might have very different characteristics), managers are able to evaluate each branch’s performance relative to a benchmark indicating how they should be performing.

The formula for smart goal setting

To arrive at these performance benchmarks, Precisely considers over 30 different location and facility attributes. We look at hours, branch type (i.e. whether it is a freestanding, storefront, or in-store location), sales FTEs, and more.

We look at the trade area in which each branch operates, that is, the area from which each branch captures 65 to 70% of customers/balances. Each trade area is shaped by population density, competition, and location. We define distinct trade areas for consumers and small businesses. There is considerable nuance to this process; for example, new locations and branches that serve commuters must be treated differently when it comes to defining applicable trade areas.

We also incorporate demographics, including projected annual household and small business changes. Overall, we look at over 100 demographic variables including age, income, home ownership, home value, and much more. For businesses, we look at annual revenue and SIC codes, to name a few.

Next, we combine demographic and behavioral data to model product demand for each branch’s trade area. This process combines over 200 demographics in business variables with millions of actual bank and credit union records, as well as third-party data.

Finally, we measure the competitive environment using FCIC/NCUA data, measuring the relative strength of networks, and evaluating the distance of competitor locations from each branch.

A new era in marketing effectiveness

Opportunity-based goal setting is a critically important first step, but this data-driven, data-enriched, location aware approach opens the door to vastly more effective marketing practices. By leveraging high-value location data, a branch network institutions can derive insights into the behavior and preferences of customers and prospects. With that information in hand, banks can build high-performing marketing programs that deliver tangible results.

By looking at geographic, demographic, and behavioral data, we can predict customer intent with a high degree of accuracy. If we have identified a group of consumers who may be in the market for a mortgage, for example, we can present them with mobile or web-based advertising. Some financial institutions are even using ATMs as a means of delivering targeted ads to individual customers.

Data-driven digital marketing is easily measurable. In the not-too-distant past, it could take days or weeks to learn whether or not a marketing campaign was effective. In the new era, we can know within hours whether or not a campaign is working, and we can make adjustments as necessary.

Data integrity is strategic for financial services firms. Maintaining accurate, consistent, and contextual data helps with offensive and defensive strategies.  Read this TDWI checklist report to explore how data integrity best practices can help your financial services organization succeed.