Teller Workforce Utilization Study

Unique Insights | Implementation Guidance | Strategic and Tactical Direction | Immediately Relevant

fmswhitepaper

By Michael ScottPresident and CEO, FMSI

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Teller Workforce Utilization Study By Michael Scott, President and CEO, FMSI

Executive summary This white paper is based on data from 10,000 financial services employees from over 1,000 branches across the United States. Performance management information, such as is presented in this white paper, will enable bank and credit union executive management teams to take this most labor-intensive area of their organizations and better understand what is critical in order to implement changes to optimize workforce utilization in their retail branch networks. Introduction Over time we often hear the terms workforce scheduling, workforce management, workforce capacity utilization, employee satisfaction, customer/member satisfaction, workforce productivity, labor cost reduction, and workforce optimization (WFO). With today’s ongoing and ever changing challenges, financial institutions (FI) find it necessary to rethink how they manage and allocate their workforce in order to reduce labor costs. Considerations must fully integrate staffing levels and capabilities to ensure scheduled staff maintains the appropriate competencies in order to facilitate customer/member satisfaction, as well as improve productivity. Managing change in a robust business environment requires a well thought out response to those changes in staffing needs to ensure the best all-around decision regarding staff usage. Implementing a WFO management and scheduling program, such as FMSI’s Teller Management System™ or other solutions available in the marketplace, enables financial institutions to gain access to data that shows staff workforce utilization based on their position. Key metrics, such as the percentage of time spent processing teller transactions and the associated labor costs per transaction by position, are tracked and become actionable information.

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Definition–workforce utilization (WFU): Measurement of branch staff performance—taking into consideration the following: primary role of a teller, productivity, labor costs, customer/member satisfaction, processing hours versus actual available hours, and the impact of utilizing platform or certain supervisory staff for processing teller transactions. Revenue per year, per employee—Google, Apple & FIs When comparing other industry global companies, such as Google and Apple, to FIs, one would expect to see a substantial difference in the revenue per year, per employee (See figure 1.1 below). Clearly they have different business models, with retail banks demanding enormous amounts of employees to run their branch networks. However, the difference in revenue per year, per employee may not be so vast if banks and credit unions did not operate in outdated staffing models where workforce utilization numbers are often below 72% (See figure 1.4 on page 6).

figure 1.1

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What can FIs do to close the revenue per year, per employee gap compared to other industry companies?

Closing the gap—Workforce utilization (WFU) management approach Managing with valuable performance management information, like the workforce utilization percentage, can lead to significant expense management enhancements for an FI. Currently, the vast majority of FIs are not closely managing their WFU through detailed monthly analysis reports. A recent study by Celent estimates that just 3% of North American FIs use WFU solutions. This type of report compares actual processing hours to paid (or available) hours by position/title (see figure 1.3 on page 5), which allows for insights into how well the staff is using their time based on their intended role. The related impacts on productivity and labor costs can be derived through analysis—for those employees dedicated as tellers. The higher the WFU percentage, the more the teller was utilized as expected, performing their primary objective. FMSI’s recommended benchmark is a WFU of 75% or higher. The below represents the top performers from the WFU study.

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Matrix chart top 10 accounts

How do FIs gather and prepare the WFU data? Gathering the WFU data When examining ways to increase utilization of their front-line staff, FIs are often faced with many challenges including, how do they measure and track the meticulous details surrounding teller activities—and equally important—how do they dissect and analyze the WFU data once they have it? The following is a workflow demonstrating the process of preparing monthly WFU reports.

* WFU Report is distributed monthly as part of FMSI’s solution.

figure 1.2

figure 1.3

March 2011 – May 2011

Top 10 average is 82.17% WFU

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FMSI 2011 Workforce Utilization Study The study is a compilation of statistics from community banks and credit unions in geographic regions all over the United States. The 2011 study encompasses over 10,000 financial services employees at over 1,000 branches from March 2011 through May 2011. The payroll data and transaction processing data are collected by FMSI from its client’s HR and core processing systems respectively. This detailed information allows FMSI to review WFU percentages at the employee, branch and institution level. Due to the nature in the way FMSI receives and collects the data for this study, there is a slight variance (+/- 3%) in some of the processing data due to varying administrative practices among the financial institutions participating in the study. Workforce utilization percentage breakdown Processing Hours ÷ Available/Paid Hours = Workforce Utilization % Analysis Figure 1.4 illustrates the utilization results of the FMSI study for the teller position. The “bottom five” bar, for example, indicates on average 64% of the teller paid hours are expended processing volume related transactions. Conversely, 36% of teller paid hours are consumed by other non-volume related activities, including administrative time and idle capacity, during a teller’s average work day.

figure 1.4

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Excess waiting for work time (or idle time)

The below excess waiting for work histogram (see figure 2.1 and 2.2) provides a graphic measure of idle time, which reflects the percentage of time spent in lower productivity (transactions per hour) ranges due to overstaffing. Figures 2.1 and 2.2 shows the EWFW impact in yellow. When managed properly, figure 2.2 shows much less EWFW.

Analysis When comparing figure 2.1 to 2.2 you can see a 68% reduction in EWFW via better scheduling that led to the following retail branch improvements: • 39% increase in productivity (TPH) • 26% decrease in cost per transaction

figure 2.1

figure 2.2

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Non-volume hours breakdown The total non-volume labor for each employee in the organization is defined as the difference between the paid hours less available hours/processing hours. Once converted by labor rates, any difference in these numbers is the summation of non-volume labor costs, administrative costs, and idle capacity costs. The below charts represent the difference in non-volume hours between three groups in the WFU study.

figure 3.1

figure 3.2

figure 3.3

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Managing down idle capacity The foremost step in managing teller line idle capacity is managing volume related activities (transaction processing and scheduling staff to the volume needs). This is achieved by measuring and then minimizing excess waiting for work (EWFW) time through a scheduling process. EWFW (caused by overstaffing) is the difference between actual processing hours for all staff versus the amount of processing hours staff should have—based on standard productivity goals established by different transaction volumes per branch. It is important to be aware of the non-volume activities, which include productive tasks; administrative non-productive, but required activities (such as training/vacation/sick time); and idle capacity. As you improve your WFU percentage, your non-volume related activity will go down and your volume related activity will go up. (See scenario 1 compared to scenario 2 below.) The study shows that volume and non-volume percentages can vary drastically between different financial institutions. Oftentimes, this is caused by a lack of focus on optimizing staff utilization through an automated scheduling process that is based on aligning the right number of staff during times that reflect high forecasted transaction volumes.

figure 4.1

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Actual institution workforce utilization metrics (FMSI client institutions) It is important to know what you expect from certain positions as an organization and have those expectations clearly defined and monitored. The teller position is typically a position that should have a much higher WFU percentage due to its primary nature as a processor of transactions. Conversely, teller supervisors, branch managers, and other non-teller positions will have a much higher non-volume percentage compared to tellers. Standard performers – FMSI client – WFU 78.72%

figure 5.1

Note: Staff and client names have been changed for privacy purposes. (figures 5.1

and 5.2)

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Low performers - FMSI client — WFU 54.8%

When branches or call centers are properly staffed to meet volume needs, there will be a shift to higher productivity while minimizing EWFW and reflecting a higher WFU percentage of those staff scheduled to meet activity needs. Lower productivity indicates an overstaffed teller line or undermanaged staff performance, which results in a lower WFU percentage. Again, this indicates too many staff waiting for work or waiting to process transactions. This also provides an opportunity for coaching underperforming staff if volumes are being considered for scheduling and the desired expectations are not met.

figure 5.2

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Recommended steps to increase WFU percentages Step 1 — Determine Internal vs. outsource Determine what is the most successful approach to execute your workforce optimization initiative: • Manage the project through internal resources (determine needed internal resources) • Outsource this function to a professional and specialized company that

excels in this topic, such as FMSI. Step 2 — Establish WFU guideline for non-tellers What staff positions are you using to cover teller line volume? Is it appropriate use of their time based on their intended role/position? Could the volume be handled more efficiently by those staff whose objective is to perform as tellers, processing customer/member facing transactions? Set the expectation for the use of supervisory, managers, or platform staff with regards to teller line coverage. The impact is also associated with higher paid employee positions, such as a teller supervisor or branch manager, processing a transaction that would normally be processed by a full- or part-time teller. What are employees doing in their different roles? Are their talents being used appropriately given their role? For example, is the expectation for a head teller to spend the least amount of time processing transactions? If so, their percent of time processing should be the smallest WFU percentage. • A coaching discussion with teller supervisors/managers to set expectations they have about their role. • Does the data indicate your expectations of how each role should be utilizing

their time? • If you expect a platform staff member to focus on sales activities and not teller

transactions, but the utilization percentage shows a high percentage of teller transaction processing, then it is a good opportunity to outline clear expectations.

• Ask your branch managers why their platform staff would have a higher WFU percentage than some of the actual teller staff.

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Step 3 — Establish Teller WFU guidelines Based on your source of data (internal or outsourced) review staff performance (whose primary function is to perform the teller transactions) and compare their actual paid hours to processing/available hours. • What are the tellers doing when they are not processing teller area transactions? • Is it work that adds value to the organization, branch, and/or customers/ members? • Am I scheduling my employees as efficiently as possible to meet the volume needs? • Does the actual WFU percentage meet your expectations for that employee or that specific position/role? Step 4 — Incorporate WFO scheduling process Implement industry best practices to schedule full-time and part-time tellers first on the schedule (utilize internal resource or an outsourced online scheduling tool—like the below— that creates forecasted schedules based on multiple months historical trends) and utilize higher paid positions only when absolutely necessary to ensure service excellence and cover volume forecasted schedules. Financial institutions will increase the WFU percentage through better forecasted scheduling to historic volume trends and traffic flow needs—not the “because we’ve always done it that way” practice of scheduling to payroll hours.

Step 5 — Re-evaluate all teller non-volume tasks

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Consider streamlining or reallocating some of the teller line ancillary responsibilities that may be taking too much time and keeping the tellers away from their primary role of providing front line transactional service to lobby and drive-thru customers/members. Step 6 — Establish part-time tellers at 90% WFU goal Utilization percentage (processing time vs. available/paid hours) of part- time tellers should reflect a higher percentage (90% on average). Part-time staff are scheduled during the peak volume demands to ensure the highest level of service experience for your customers/members. Full-time tellers would be optimally utilized at 75% or higher. The use of part-time tellers to handle the durations of higher transaction volume will reduce excess waiting for work (EWFW) time; or the idle time tellers are waiting to process transactions. Conclusion/Summary The financial services market is in volatile times. Closing the revenue per year, per employee gap compared to other industry companies is a realistic goal for those institutions that better manage the most labor intensive areas of their business by scheduling staff to volume needs. Having access to actionable business intelligence with the exact decision-support information needed to establish productivity goals, accountability, proper staff scheduling, and workforce utilization, will lead financial institutions to have a reduction in the average labor cost per transaction processed. _______________________________________________________________ Sources Referenced in the Workforce Utilization White Paper: 1. All account and employee names have been changed in the FMSI charts to

protect the privacy of those involved (pages 4, 7, 10, 11) 2. Revenue Per Year, Per Employee (page 3) Navy Federal Credit Union 2010 Annual Report (https://www.navyfederal.org/ pdf/publications/annual-report/NFCU_ar2010.pdf)

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Navy Federal Credit Union number of employees taken from iBank.net (http://www.ibanknet.com/scripts/callreports/getbank.aspx?ibnid=usa_617677) HSBC numbers found on cnn/money HSBC web page profile (http://money. cnn.com/magazines/fortune/global500/2006/snapshots/639.html) BOA numbers found on the cnn/money BOA web page profile (http://money. cnn.com/magazines/fortune/global500/2010/snapshots/2580.html) Google numbers found on the cnn/money Google web page profile (http:// money.cnn.com/quote/quote.html?symb=GOOG&source=story_quote_link) Apple numbers found on the cnn/money Apple web page profile (http:// money.cnn.com/quote/quote.html?symb=AAPL&source=story_quote_link) 3. Celent figure stated on page 3 can be found in the BAI Banking Strategies on-line article, “Branch Transformation: A Work in Progress”, by Bob Meara. _______________________________________________________________ W. Michael Scott – President / CEO of FMSI (mikes@fmsi.com) began his banking career in 1968 and encompassed 15 years of managing all operational areas of a large financial institution. Since 1983, he has been involved in delivering management consulting services to institutions across the nation. In 1990, Mr. Scott incorporated FMSI as a firm specializing in Performance Management Information Reporting. Atlanta-based FMSI has assisted over 600 nationwide financial institutions in optimizing their branch networks through applying actionable business intelligence from the analysis of transaction data. The average FMSI client has saved $30,000 per year, per branch. All FMSI clients receive extensive monthly reports—including a ranking report that consists of all their peer’s productivity metrics that are utilizing the FMSI solution—for comparison benchmarking purposes. For more information, contact Gordon A. Williams, EVP Business Development, @ 877.887.3022 or gordonw@fmsi.com. www.fmsi.com

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