First were Automated Teller Machines, than came Online Banking, and yet, despite the predictions of its demise, the bank branch is alive and well. Why?
The answer, my banker says, is sales.
The branch manager is responsible for operations, customer service, and product sales. The loan and mortgage advisers are the sales floor associates. The tellers are cashiers. Sounds familiar?
It should. The bank branch functions as a retail store.
Just as the physical store remains the core of retail activity despite the flourishing online entities and Armageddon cries by some technology vendors, so does the branch for retail banking.
The Starbucks of Retail Banking
The trend to reduce the physical footprint of has been replaced by a smarter and more customer-centric view of retail banking. Most branches deal with three core segments: customers seeking professional services, people needing teller services, and those using the Automatic Teller Machines (ATM).
While private banking and financial brokerages existed for a long time, the common bank branch has only recently evolved into professional services such as credit cards, mortgages, and business loans, as the primary retail activities.
And while encounters with customers are less frequent, the required expertise and the relatively longer service time of professional services, has pushed the quality of customer service into the forefront of retail banking. There is a push towards more employee training, scheduling appointments, and designer layouts, in order to entice customers to visit the branch.
And while more simple tasks such as deposits and withdrawals, are done in the self-service area, or moved online, teller services are still needed. This is due to a variety of reasons, from customers who need the comfort of a human teller, a more complicated deposit, or a foreign currency transaction. The size of the tellers area depends on the local requirements.
Many bank branches are small, with 2 to 5 tellers sharing responsibilities for counter service inside the bank and services rendered at the drive-thru window, as well as a slate of office activities. Whether it is a main street branch with the size of Best Buy or a suburban outlet akin to a jewelry store, the challenge for the bank is to optimize limited labor resources within the objectives of customer service.
Think Starbucks serving, well, bucks…
In Behavior Analytics, we focus on activities as a function of Time and Location. Retail Banking offers a window into Waiting Time and Service Time.
Why Waiting Equals Customer Engagement
The common customer experience in a bank branch contains two parts, waiting for service, and being served. If, in a retail store, the checkout process is the last “touch point”, then in the bank, the checkout is the experience. Since Store Stay Time only consists of the Wait Time and Service Time, for a bank competing in customer service, managing queues is crucial.
According to a study from the Omnico Group, the average American can muster 8 minutes in line while Europeans have the patience for only 6 minutes wait before leaving without returning. Customers in Asia and Latin America endure long lines. For many banks, especially in America, the solution is over-staffing.
Other queue busting solutions include technologies from queue management to self-service. Winning retailers focus on designing better service models.
Queue Management Solutions: The definition of Queue Management is wide, and includes simple stanchions, take-a-ticket, and call forwarding.
Sometime, just rearranging the layout to a Linear Queue (First in First out) instead of Parallel Queues can speed up the process.
The more sophisticated, and non-intrusive, solutions deploy Thermal or Video sensors to monitor the queue. These solutions provide data on the actual number of people waiting and their waiting times, and offer how many cashiers should be active in order to prevent the formation of queues.
Self Service Stations: The 24/7 convenience of the Automated Teller Machines shifted simple transactions such as deposits and withdrawals to self-service. Some European banks offer additional services such as currency exchanges.
The caveat in self-service is how easy it is for the local customer to use. In other words, introducing self-service kiosks in downtown London makes more sense than placing ATM in the midst of a senior center in Florida. Another caveat is the tolerance for waiting for self-service kiosks is almost non-existent
Customer Service Models: By combining data from the workforce management and queue solutions, we can optimize the schedule. Sample models are:
- Teller Services: Serve 90% of customers in less than 3 minutes
- Drive-Thru: No more than 1 waiting car during non-peak hours
- Professional Services: Keep 97% of appointments on time
With Service Level metrics, each local branch is scored on how often they perform according to the corporate strategy.
When your Average Waiting Time is longer than the Average Service Time, it is time to rethink your customer service models.
Why Customers Leave
Abandon behaviors are a picture of displeasure.
Abandon behaviors are grounded in negative emotions, and are especially harmful to retail banking because most people come to the branch for a specific purpose. The nature of a bank visit implies that customers who abandon before receiving service are at significant risk of not returning.
Aside from the quality of service, customers leave when they feel the impending waiting time and the waiting environment overwhelms their need for service.
The following are common behaviors that impact the formation of queue and service bottlenecks.
Service Time: Banks, like retailers, are focused on Transaction Time. They spend resources and company energy on studying activities and procedures with the goal of making the transaction process more efficient. While a faster transaction time is to be admired, we should not confuse the efficiency of a better process with the objectives of quality customer service.
Teller transactions such as buying traveler’s checks may be subjected to time restrictions, but working with a customer on a mortgage may be better served with quality scorecards than keeping a time clock.
If the primary user of teller services is the elderly and those who do not speak the local language or have a local account, than the process of customer service, by definition, requires patience, and a longer service time.
When we formulate a service model, we account not only for the transaction process but also queue behaviors to determine the optimal Service Time.
Complex Transactions: If there are 3 cashiers, and one teller is involved with a complex transaction that significantly increases the service time, it effectively renders that cashier as unavailable. This behavior is one factors to formation of queues in non-peak periods.
Peak Periods: When the queue is especially long, the objective is to find a way to reduce how many people are in line, such as a supervisor identifying customers waiting only to deposit cash. Branches with no waiting bottlenecks during the week can be overwhelmed when 40 customers enter the bank within a 15 minute period, on a Friday afternoon.
A common complaint from bank managers is that corporate scheduling is not attuned to peak demand. In the tally of Scheduled to Actual staffing, the mishmash of operations in peak periods wreaks havoc on branch performance.
Predictive Scheduling: Robust Queue Solutions measure actual demand (how many people are in the bank), how many people are waiting for service, and how many customers are serviced, and formulate, if possible, the recommendations for real-time deployment.
Spikes in demand, complex transactions, and insufficient flexibility in the schedule are the core causes for the formations of queues.
For example, we compared two banks. The first was one of the larger branches in the chain, with 8 tellers, 3 self-service stations, and a contingent of professional services. On average, the bank serviced around 3,000 people, per day.
Just down the road was another branch of the same chain. This location was tiny. With the ATM in the vestibule, and 2 teller counters at the main entrance, the bank always looked crowded.
To everyone’s surprise, the smaller bank serviced almost 2,000 people a day, and received higher service scores than the big branch.
The audit reveled that the employees excelled in moving customers from the crowded ground floor to the less busy first floor. The quick attention to forming queues earned loyal customers.
By any measure of service, the tiny and crowded bank performed better than the big and luxurious branch. The data contradicted perceptions and presented a more balanced and complete picture of what was happening in the two banks.
This is Behavior Analytics.
The bank branch is alive and thriving because people still want to put a human face on who is holding their money.