Using Industry
Benchmarking Data
to Maximize
Teller Results
Stronger Performance,
Sharper Earnings
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Stronger Performance,
Sharper Earnings
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Table of Contents
Introduction ...............................................................................................1
Four Different Staffing Approaches..............................................................2
Validating Your Staffing Approach vs. Making Decisions in a Vacuum ........ 3
Benchmarking Data and SMART Goal Setting ............................................ 4
Balancing Productivity, Sales, and Service with Benchmarking Data .......... 5
Community First Credit Union of Florida – Case Study ............................... 7
Table of Contents
Conclusion ................................................................................................ 9
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Introduction
Having the right Business Intelligence (BI) in the branch environment is critical
to achieve optimal sales, service, and productivity performance. Whether
gathering and preparing data internally or working with a third party industry
expert, BI can come from many different sources. One of the strongest types
of BI is industry peer benchmarking data. Executives in financial institutions
already often use industry data when analyzing high level metrics, such as:
efficiency ratios, market share percentages, loan growth, and asset growth.
However, an often overlooked category for leveraging the power of industry
benchmarking data is branch network staffing optimization. On a monthly basis,
a significant number of financial institutions (FIs) utilize industry benchmarking
data to compare teller transactions per hour, labor cost per teller transaction,
salary and benefit rates, and part-time utilization. With this valuable information,
FIs are able to monitor and improve their branch network staffing performance.
Benchmarking industry data validates to financial institutions that increased
teller productivity is achievable. Many organizations believe they are
performing adequately, but often are shocked by their workforce optimization
performance when compared to their industry peers—realizing they can do a
lot more with less. These benchmarks become critically important when setting
realistic and high performing productivity goals.
Introduction
This white paper will address how industry benchmarking data can play a
vital role in the branch performance improvement process and will cover the
following key topics:
•
•
•
•
Your teller line productivity performance compared to industry peers
Enhancing the effectiveness of SMART Goal Setting
Using industry data to help balance productivity, sales, and service while focusing on workforce optimization in your branches
Reviewing a detailed case study highlighting the use of industry benchmarking data to achieve a 23% decrease in teller labor cost per transaction
(1)
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Four Different Staffing Approaches
Four Different Staffing Approaches
Determining the staffing levels in a branch network can be divided into four
basic categories.
•
•
•
•
Asset Size—Allocating number of staff based on the asset size of each branch.
Budget—Basing staff expense on an amount that is in-line with how much an institution spent in previous years (plus three percent for inflation).
Capacity—Filling every available teller-window in the branch network from open-to-close.
Demand—Optimally scheduling both full-time and part-time tellers to be
in-line with forecasted transaction volumes per branch.
Asset
Size
figure 1.1
Demand
Branch
Staffing
Budget
Capacity
Analysis
As the single largest non-interest operating expense and most significant
controllable line item in an institution, slight efficiency gains in staffing the
branch network can quickly add-up to significant cost savings. With this in
mind, most institutions have integrated a combination of the above staffing
approaches in their overall scheduling strategy to help curb some of these
enormous staffing costs. However, the most impactful approach, scheduling
to demand, is bar none the most underutilized category of the four. According
to a recent study by Celent, just three percent of North American financial
institutions (FIs) currently use a monthly reporting and scheduling solution.
Perhaps the other 97% are not staffing their branches based on demand,
because they are not comparing their teller productivity performance to other
institutions and, subsequently, seeing how much money they are potentially
leaving on the table.
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Validating Your Staffing Approach vs. Making Decisions in a Vacuum
Validating Your Staffing Approach
Most organizations have not compared their staffing efficiencies with other
similar institutions. Instead, they prepare their staffing budgets in a vacuum
with the best information available to them (see figure 1.1 on page 2). Often
unbeknownst to the organization, this process leads to institutions being
inefficiently staffed or overstaffed. With the same budgets in place going forward,
they may be perpetuating their overstaffed conditions and not even realizing it.
To gain a better understanding of actual staffing efficiencies, validated by a third
party source, banks and credit unions can work with workforce optimization
vendors—such as FMSI, to gain a better understanding of their workforce
optimization performance. One way this is achieved is by comparing their
actual performance on the FMSI Benchmarking Data reports to other institutions
by name every month. Below is the top 10, top 25%, and top 50% chart from
FMSI’s monthly ranking report.
Top 10: TMS Benchmarking Data Report
Ranked
By TPH Institutions
1
2
3
4
5
6
7
8
9
10
28.4
23.7
24.0
22.3
22.2
21.9
21.6
21.1
21.1
20.8
*RCs
First Trust Credit Union
Sunrise Federal Savings
Coastal Shores Bank
Medlock Federal Credit Union
Sound Metro Bank
Middleton Savings & Loan
Ackerman & Main Credit Union
Trooper Bank & Trust
Morningside Federal Credit Union
Fulton Main Credit Union
TMS Top Ten Average
TPH: 22.8
15
26
17
17
22
13
12
10
11
17
figure 2.1
City, State
Glendale, AZ
Detroit, MI
Chicago, IL
Fort Worth, TX
Cleveland, OH
Bellview, FL
Columbia, MD
Rochester, NH
Rockford, IL
Greenfield, WI
Cost Per Trans: 0.70
Cost Per
Trans
Salary
Benefit Rate
Part to Full
Time %
Value / Rank
0.57 | 1
0.58 | 2
0.77 | 19
0.64 | 4
0.77 | 20
0.65 | 6
0.79 | 25
0.79 | 24
0.68 | 10
0.75 | 15
Value / Rank
16.23 | 64
14.37 | 37
18.54 | 86
14.25 | 33
17.15 | 71
14.24 | 32
17.08 | 70
16.67 | 68
14.33 | 36
15.43 | 55
Value / Rank
0.35 | 51
0.43 | 27
0.09 | 92
0.48 | 18
0.40 | 33
0.38 | 41
0.41 | 31
0.32 | 57
0.46 | 23
0.48 | 16
Salary Benefit Rate: 15.83
Part to Full Time %: 0.38
Names have been changed for privacy purposes.
TMS Top 25% Average
TPH: 20.6
Cost Per Trans: 0.80
Salary Benefit Rate: 16.28
Part to Full Time %: 0.39
TMS Top 50% Average
TPH: 18.8
Cost Per Trans: 0.84
Salary Benefit Rate: 15.65
Part to Full Time %: 0.37
Typically, institutions start on the FMSI system between 11 and 14 TPH,
therefore, the senior management team gets a real “eye opener” when
comparing their numbers.
Analysis
The BI industry benchmarking data from figure 2.1 above gives senior
management a point of perspective on how well their institution is performing
compared to their peers. For example, number seven in the above chart,
Ackerman & Main CU, can quickly see that the number one institution, First
Trust CU, is outperforming their transactions per teller hour (TPH) by 23%.
With this information Ackerman now knows what is achievable and can decide
whether or not to strive for that goal. The labor cost savings from an incremental
productivity gain can save a mid-sized institution tens of thousands of dollars
per month.
(3)
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Benchmarking Data and SMART Goal Setting
Benchmarking Data and SMART Goal Setting
Utilizing the SMART Goal system in conjunction with industry benchmarking
data is very helpful towards maximizing your performance improvement results.
SMART is an acronym for Specific, Measurable, Attainable, Relevant, and Timely.
Description
Benchmarking Data Impact
Specific
Choosing a general goal will result
in vague results. Do not just set the
goal of improving teller productivity.
Instead, set the specific goal of
increasing your Transactions per Hour
(TPH) by 35%.
Use benchmarking data to help
establish a specific TPH goal based
on similar sized institutions and their
geography.
Measurable
Tracking your monthly progress is
critical to achieving your goal and,
most importantly, you must select the
right metrics to track. There are many
Key Performance Indicators (KPIs) you
can track in a workforce optimization
initiative, including: TPH, Labor Cost
per Teller Transaction, and Excess
Waiting for Work percentage.
Use benchmarking data to help identify
some of the metrics you would like to
track. Along with your own institution’s
monthly progress, you can use the
ranking report as a point of reference
during your analysis for these different
metrics.
Attainable
Choosing unattainable goals can lead
to bad employee morale and a notion
that an initiative is not successful—even
though it could be making substantial
progress.
Use benchmarking data to help see
what is possible from other institutions.
If others are able to achieve these
results, can you? Validates what is
possible, yet challenges staff to reach
highter performance standards.
Arguably the most important of the
SMART goal setting guidelines— a goal’s
relevancy should closely align with
the overall strategy of your institution.
For example, the financial stability of
an organization relies on operating
efficiently, which improving workforce
optimization directly complements,
such as decreasing the labor cost per
teller transaction.
Use the top 10 institution average and
the top 25% and 50% list average from
the benchmarking data to help assure
you that your goals are in-line with your
top performing industry peers.
Defining deadline expectation on
periodic goal achievements is critical to
accomplishing your goal in a desirable
time frame. It’s important to remember
that, if your tellers are producing at 10
transactions per hour, they won’t be
able to achieve 25 transactions per hour
overnight.
Use benchmarking data when
monitoring your periodic goal
achievement. Take a close look each
month to see how similar institutions
are improving. Has your institution
moved up five spots in six months while
another rival institution stayed the same
rank?
Relevant
Timely
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Balancing Productivity, Sales, and Service
Balancing Productivity, Sales, and Service
Service and sales are critical to the success of a branch network and some
believe these areas would suffer setbacks in conjunction with workforce
optimization. However, an institution can still score high in service and sales
while improving teller productivity at the same time. Where do you focus your
efforts on the teller line?
"Technology continues to drive convenience or electronic banking
over the edge, which has resulted in decreased foot traffic in our
branches. That’s why it’s so important for frontline employees, such as
our tellers, to be attuned to their customer needs by balancing sales,
service, and productivity with each and every transaction. " –Lisa A.
Monthley, SVP, New Windsor State Bank
Management Focus on Categorical
Teller Line Performance
figure 3.1
Service
Sales
Labor Cost per Teller Transaction: $1.21
Productivity
Analysis
Optimal Performance
with Sales, Service, and
Productivity
Labor Cost per Teller Transaction: $0.81
The institution’s labor cost per teller transaction can be dramatically different
when comparing whether or not management has a focus on teller line
productivity performance—as evident in the above figure 3.1 where there is
a $0.40 labor cost difference per teller transaction. When extrapolating this
cost difference across a typical 10-branch institution with an average of 6,000
transactions per branch, per month, the savings are $288,000 annualized. An
institution of this size would have to significantly increase their branch sales to
equal these productivity savings.
So how are some institutions able to achieve these productivity savings while
improving desired service and sales levels?
(5)
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Balancing Productivity, Sales, and Service
An Overlooked Opportunity
Disregarding a workforce optimization initiative in the name of sales and
service can lead to considerable overstaffing—a condition commonly going
unnoticed due to the lack of performance management information and industry
comparisons. As figure 3.2 (see below) shows us, many financial institutions
that pursue improved teller productivity also are simultaneously working on
increasing revenue and upholding excellent service levels. A few detailed
examples of this include:
figure 3.2
Client Rank –
FMSI Benchmarking
Data Ranking Report
Assets
Before
After
FMSI
FMSI
Transactions per
Hour Average
Before After
Productivity
Percent
Improvement
# of Branches
Before
After
#2
$1.3 bil
$2.4 bil
20.3
23.7
16.7%
15
24
#4
$470 mil
$613 mil
18.4
22.3
21.2%
5
No Change
#10
$2.4 bil
$4.4 bil
24.0
25.8
7.5%
16
23
Analysis
Peer benchmarking information empowers management with the knowledge of
what is possible. Time and time again institutions all over North America are
able to move the needle on service and sales while at the same time lowering
their average labor cost per teller transaction. The common theme for these
institutions is a top down management approach which supports these types of
initiatives. The people driving a productivity initiative need to have ownership of
the bottom-line performance of the entire institution, not just sales and service.
For example, a CEO, CFO, or COO can be involved in managing toward the
desired results, which helps establish the right level of accountability and
responsibility. It is all too common for workforce optimization initiatives to be
stopped at the retail management level in the name of sales and service. When
the FMSI industry benchmarking data reports clearly show many banks and
credit unions succeeding on the sales and service front—why are all institutions
not fully optimizing their staff through demand based scheduling?
Management Tip
Management can refrain from the practice of “just replacing” a teller
every time one leaves or transfers. This is a costly and outdated
routine that can easily be replaced with referencing detailed workforce
optimization reporting that justifies the new hire.
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Case Study – Community First Credit Union of Florida (CFCU),$1.2 billion in
assets, has 16 branch locations in Jacksonville, FL.
After rolling out FMSI’s Teller Management System™ in May 2011, CFCU saw
a 31% increase in teller line productivity (see figure 4.2) and a 23% decrease
in their teller labor cost per transaction (figure 4.3), both of which helped
catapult them into the FMSI Top 10 Benchmarking Monthly Client Ranking
Report. The following chart (figure 4.1) highlights CFCU’s steady climb in FMSI
benchmarking industry rankings.
figure 4.1
Labor Cost Per Transaction/Rank
Salary & Benefit Rate/Rank
January 2012
July 2012
15.9 (#55)
18.0 (#27)
20.9 (#9)
$1.07 (#76)
$0.91 (#42)
$0.82 (#28)
$17.05 (#81)
$16.39 (#76)
$17.17 (#94)
Community First Credit Union of Florida
Transactions Per Teller-Hour Trend
31.4% Increase in Productivity from July 2011
Community First Credit Union of Florida
Labor Cost Per Transaction Trend
23.2% Decrease in Labor Cost Per Transaction from July 2011
figure 4.2
120
25
Average Transactions
Per Teller Hour Trend
Case Study
20.9
18.0
15
15.9
5
0
Jul-11
Jan-11
Jul-12
Labor Cost Per Transaction (Cents)
Transactions per Hour/Rank
July 2011
figure 4.3
$1.07
$0.91
$0.82
80
40
0
Jul-11
Jan-11
Jul-12
See more detail about CFCU’s TMS experience
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CFCU Actual Staffing Effectiveness Before and After Chart Comparison
FMSI’s Excess Waiting for Work (EWFW) histogram (see figure 4.4 and 4.5
below) provides a graphic measure of unproductive performance, which reflects
the percentage of time spent in lower productivity (transactions per hour) ranges
due to improper scheduling. Figures 4.4 and 4.5 show CFCU’s EWFW impact in
red. As a result of CFCU’s scheduling improvements, figure 4.5 shows much
less EWFW.
figure 4.4
July 2011
30
15.9 Average Transactions per Hour (TPH)
$1.07 Labor Cost per Transaction
Percent of Total Hours
25
Weighted Goal TPH: 23
EWFW = 37.8%
At Goal FTE Net: Over 20.69
20
15
10
5
>50
>45 - 50
>40 - 45
>35 - 40
>30 - 35
>25 - 30
>20 - 25
>15 - 20
>10 - 15
>5 - 10
>0 - 5
0
figure 4.5
July 2012
20.9 Average Transactions per Hour (TPH)
$0.82 Labor Cost per Transaction
25
Weighted Goal TPH: 24
EWFW = 12.9%
At Goal FTE Net: Over 6.1
20
10
5
>50
>45 - 50
>40 - 45
>35 - 40
>30 - 35
>25 - 30
>20 - 25
>15 - 20
>10 - 15
>5 - 10
0
>0 - 5
Percent of Total Hours
Case Study
15
“There is ample opportunity to better manage our staffing levels and FMSI
offers the best solution set to help us accomplish this
critical task.” - Mike Tomko, SVP of Operations at CFCU
(8)
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Conclusion
Most financial institutions are not currently reviewing monthly benchmarking
industry data relating to workforce optimization. As a result, these organizations
are missing the opportunity to harness a very powerful source for business
intelligence that commonly leads to significant labor cost savings. Seeing the
teller productivity performance lined up next to similar institutions can be
eye opening.
Based on FMSI’s free one month trial, where institution’s transaction data is
analyzed and transformed into actionable workforce optimization business
intelligence, many organizations are often shocked by their initial teller
productivity performance when compared to their peers. While a select few
number of institutions validate the effectiveness of their internal workforce
optimization processes, most find they have over $2,000 per branch per month
available to save.
Branch staff benchmarking industry performance data can enhance the
performance of financial institutions in multiple ways, including:
•
•
•
Enhancing the effectiveness of SMART Goal Setting
Validating the potential of improving sales and service while focusing on workforce optimization in your branches
Help improve teller line productivity as presented in the detailed Community First Credit Union of Florida case study, which highlighted the use of industry benchmarking data to achieve a 23% decrease in teller labor cost per transaction and 31% improvement in teller productivity.
Conclusion
“Knowing your numbers and seeing where one is ranked on the FMSI monthly
benchmarking data report is what causes senior management to affect
change.” –W. Michael Scott, FMSI President/CEO
(9)
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About Financial Management Solutions, Inc. (FMSI)
Located in Atlanta, GA and established in 1990, FMSI provides easy-to-use, yet
sophisticated, business intelligence systems —The Teller Management System™
(TMS), The ContactCenter Management System™ (CMS), and the Lobby Tracking
System™ (LTS)— that allow financial institutions to manage and staff to meet
their service and sales needs.
Put simply, while using FMSI’s flagship product, The
TELLER
Teller Management System™, financial institutions of
MANAGEMENT
TM
SYSTEM
all sizes have been able to significantly reduce their
operating expenses while improving service levels. The
system’s dynamic online dashboard and monthly reports
include extensive performance management business intelligence including
a ranking report that consists of peer productivity metrics—for benchmarking
purposes. In addition, the system provides a more automated scheduling process
through a web-based scheduling engine that utilizes teller transaction data to
forecast and align the right number of tellers at the right times—resulting in a
reduction of excess labor costs.
By providing 20 plus unique lobby service and sales
LOBBY TRACKING reports including: individual sales and productivity
SYSTEM
reports, account holder platform activity volume
Conclusion
TM
reports, and cross-sell reports, FMSI’s Lobby Tracking
System™ (LTS) effectively supports senior management in making critical lobby
management decisions. Coupled with a queue management system (with iPad &
Kiosk integration), upon implementation, LTS also dramatically professionalizes
the account holder service experience through an advanced eClipboard online
portal— offering user service alerts and complete real-time service performance
dashboard reporting throughout the institution’s management structure.
See other FMSI White Papers
For more information, contact Gordon A. Williams, EVP Business Development,
@ 877.887.3022 or [email protected]. — www.fmsi.com
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