In an era of retail banking digital transformation and omnichannel strategy, the financial industry is facing increased competition with the growing popularity of online banking. This is pushing banks to rethink their business model, on-site services as well as their physical branch networks as a whole.
To better meet the changing needs of customers, maximize efficiency, and maintain profitability, network right-sizing decisions must sometimes be made. This could mean downsizing or closing underperforming branches, opening new ones in growing areas, or relocating branches to more convenient locations to reach the right target audience.
Banks know their customers well and have a lot of information about them. However, to make the best property decisions, they often miss two things. First, they lack deep knowledge of the characteristics and habits of potential customers who pass by each of their branches. Second, they have no efficient way to identify, visualize, and analyze the key factors that influence the performance and profitability of their branches in order to understand the why behind the what.
This is what hyperlocal data, such as historical and near real-time mobility data, and retail banking analytics tools are for.
Data-Driven Decision-Making for Maximized Square Footage
Despite the drop in branch footfall and even if most needs can now be met entirely remotely via digital banking, some customers still prefer to access some services or conduct certain banking transactions in person. This includes cashing or depositing checks, withdrawing money, exchanging coins, or signing up for a new account or product.
According to Novantas, accounts opened digitally have a first-year retention rate of just 50%, compared with 80% for accounts opened in-branch. Moreover, 30 to 50% of people prefer to visit a branch for help with complex products and issues.
So, traditional banking is not dead. There will always be a need for brick-and-mortar locations, particularly regarding high-value-added activities and customer assistance. Thus, analyzing opportunities to right-size branch networks in order to maximize square footage is an increasingly important task that is far from easy. This is why 92% of investment organizations, whether hedge funds, private equity, or venture capital, use alternative data to support their investment decisions.
Hyperlocal data, in particular, is a useful source of information for the banking industry as it provides more insight into market opportunities, competition, customer behaviors, local and regional visibility, and physical convenience. With the right analytical tools, banks can run a variety of data analyses offering improved insights into the evolution of these factors in correlation with the performance and profitability of their branch network.
On the one hand, hyperlocal data can help financial institutions identify expansion opportunities in high-potential areas to gain market share. For example, it could be an area near a new residential development with a large portion of the target audience, a growing commercial area with high traffic, or a particularly crowded intersection.
First, demographic data, like age, gender, education, and social status, combined with consumer data will give banks a good picture of the different customer segments of a specific area. By correlating this information with their internal customer data, banks can then determine which of these segments are most likely to visit their branch and for what kind of services.
The most useful consumer data for the banking industry include:
- Disposable income
- Checking accounts
- Transactions and financial transfers
- Bank service charges
- Finance charges
- Credit card usage and memberships
- Safe deposit box rentals
Second, mobility data will provide an additional dimension to the previous data by correlating customers’ characteristics and lifestyle choices with their travel patterns. These include their departure points, their travel time and speed, and their destination. This information will validate the accessibility and suitability of the new branch’s planned location by confirming that the targeted customer segments would pass by it and that it is not too big a detour for them.
Third, to ensure that customers will be willing to stop and not just pass by, banks can look at the surrounding points of interest or other types of businesses nearby that appeal to their audience segments, as well as the presence of competitors.
It is common for financial institutions to cluster in the same commercial area, preferably in close proximity to other retailers such as pharmacies, grocery stores, restaurants, and gas stations. This phenomenon, known as “agglomeration,” plays an important role in the location strategies of the banking industry, as it allows banks to leverage the foot traffic of nearby stores.
Ideally, the site selection process should involve the real estate team and the marketing team to perform branch analytics and develop planning models. This would save planners time by not having to travel to potential new branch sites or draw maps by hand.
Closing, Downsizing, or Relocating Branches
On the other hand, hyperlocal data can support decisions to close or downsize branches that are underperforming, or relocate those that might perform better elsewhere. These are particularly important decisions considering that the percentage of banking needs handled in-branch might dwindle to only 5% over the next few years, according to a McKinsey report. In some markets, this can result in a 25% reduction in the number of branches, with the remaining branches carrying out a range of different activities.
The main factors leading to these types of decisions are demographic or socio-economic changes that alter customer needs in terms of location, size, banner type, and service offering. It can also be a drop in traffic volume in the trade area resulting in a loss of revenue or the opening of a competitor’s branch that takes traffic away from yours.
To gain this type of insight, financial institutions can compare annual, monthly, daily, and even hourly variations in traffic patterns, making it easier to track changes in branch performance. These variations can even explain unusual customer journeys or the seasonality of hyperlocal sales.
Other factors include the presence of new physical barriers that might negatively impact the performance of an existing or potential branch by discouraging customers from visiting.
Offering the Right Services According to Location
To increase footfall and usage of in-person services, as well as improve customer loyalty and financial return on investment, banks have no choice but to improve the overall customer experience. This could mean renovating or redefining the spaces in their branches and tailoring the services offered according to location and the type of customer seeking in-branch banking services.
For instance, they might decide to add more ATMs, kiosks, and drive-through lanes based on customer needs and behaviors at the micro, household, and street level, instead of the more generic ZIP code or neighborhood level. It will also be important for bank managers to ensure having the right number of employees on the floor for the predicted level of foot traffic by season and time of day.
In contrast, where branches are set to close, banks must double their efforts, for example through targeted marketing campaigns, to get customers in these areas to engage with them digitally in order to remain competitive.
Choosing the Right Data and Site Analytics Tool With Korem
Considering how the future of retail banking is evolving, tracking historical foot and vehicle traffic data is becoming increasingly critical as it provides context about customers and their travel patterns at a hyperlocal level. By combining mobility data with demographic, consumer, business, and point-of-interest data, banks can build a complete profile of different segments of customers in a given area.
In order to make the best decisions regarding branch network optimization, financial institutions will benefit from working with a geospatial company such as Korem. In addition to providing several types of data from the best suppliers, Korem’s team of experts can also extract and transform these data into a usable format through its Data as a Service (DaaS) offer.
Subsequently, Korem can assist in the design of informative dashboards that provide a tangible visualization of branch performance, for example in relation to the fluctuation of traffic flows. As an example of a real case, Korem helped one of its clients, the SAQ, make data-driven real estate decisions and optimize its total square footage using the site analytics tool, Alteryx Designer.