Mais conteúdo relacionado Contact Center Metrics, Contact Center Planning, and How our Metrics Can Lead Us Down the Wrong Path1. CONTACT CENTER METRICS, CONTACT
CENTER PLANNING
(and How Our Choice of Metrics Make Us Do Silly Things)
Ric Kosiba
President
Bay Bridge Decision Technologies
2. Your Seminar Leader
Ric Kosiba serves as President of Bay Bridge Decision Technologies. In early 2000, he
cofounded the innovative software company and now leads the development of the
company’s optimization technologies used in call center management. He is expert in the
field of call center management and modeling, call center strategy development, and the
optimization of large-scale operational processes. Kosiba received a Ph.D. in Operations
Research and Engineering from Purdue University and an M.S.C.E. and B.S.C.E. from
Purdue’s School of Civil Engineering.
Kosiba has obtained a patent on the application of optimal collection strategies to
delinquent portfolios in addition to two patents on the application of simulation and
analytics to contact center planning.
At the start of his career, Kosiba served notable roles for two major airlines including
Manager of Customer Service Analytics for USAir’s Operations Research Division as well
as Operations Management Senior Analyst with Northwest Airlines. His specialties
included airport and call center staffing as well as productivity improvement projects.
Following this role, Kosiba moved into Customer Support at First USA, where he served
as Vice President of Operations Research. Expertise here included all facets of contact
center process improvement, ranging from overall collections strategy modeling to
detailed staff plan development and call center budgeting.
Prior to Bay Bridge, Kosiba held a position as the Director of Management Science at
Partners First, where his primary duties included detailed modeling of portfolio risks, as
well as predictive and prescriptive marketing and operations engineering.
© 2012 Bay Bridge Decision Technologies, Inc. All Rights Reserved. Proprietary and Confidential
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3. Overview
This webinar will bounce around a lot! We’re going to chat
about metrics and planning, and things that I’ve seen we do
that don’t always make a lot of sense
Service failures and “catching up”
Occupancy as efficiency (and what is better)
Service level and back office
Forecast error
Staffing over/under
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4. An old and common story…
The call center gets hammered on the first day of the month…
Scenario:
• Service Level Goal: 85/20
• On first two days, only
achieved 45%
• From then on, overtime
by 5% service level in
order to get average
service level back up to
85% (run 90% every
day)!
… and spends the rest of the month trying to “catch up”
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5. So, how does this work out?
Service Level by Day
100
Achieve service level goal by day 18
90
ServiceLevel
80
70 Daily Service Level
Avg SL for Month
60
50
40
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
Days
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6. What does this cost the company in overtime?
22 FTE
About 22 FTE’s worth of overtime, for 16 days, at $20/hr, equals ~$57,000
© 2012 Bay Bridge Decision Technologies, Inc. All Rights Reserved. Proprietary and Confidential
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7. But what would happen if our goal was, instead, an ASA goal?
Using CenterBridge’s sensitivity analysis, we can find
“equivalent average speed of answers”
From CB Sensitivity
Analysis:
• 45% SL = 80 Sec ASA
• 85% SL = 20 Sec ASA
• 90% SL = 10 Sec ASA
© 2012 Bay Bridge Decision Technologies, Inc. All Rights Reserved. Proprietary and Confidential
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8. So, let’s do the same exact analyses, but in ASA, not SL
Average Speed of Answer by Day
90
Average Speed of Answer
Hit ASA goal by day 14 (12
80
days of playing catch up)
70
Daily ASA
60
50 Avg ASA for Month
40
30
20
10
0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
Days
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9. There are strong diminishing service level returns
3%
5%
9%
10%
Buying service level is expensive at
13%
the upper ends of the curve
(it is hard and pricey to achieve a 90% SL)
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10. It is also difficult and costly to maintain a 10 Second ASA
18 Sec
13 Sec
10 Sec
8 Sec
3 Sec
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11. First. This is not a discussion of ASA versus SL
Each metric has it’s own properties Service
– SL has a ceiling (100%)- and it is very ASA!!
Level!!
difficult to get near to that ceiling!
– ASA’s floor is also impossible to achieve
• But it is easier to average to a
number “20” when your floor is “0”
and your performance is “10” than it
is to average to an “85” when your
cap is “100” and your performance is
“90”
• But none of this has anything to do
with “service”
© 2012 Bay Bridge Decision Technologies, Inc. All Rights Reserved. Proprietary and Confidential
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12. The punch line
• You would save ~$12,000 or 22% of the overtime dollars, if you
managed to an ASA goal instead of a service level goal, and you
wanted to “catch up” to your service goal by month’s end
• But “catching up” is really pretty counterproductive
– Nobody who called during the service blow up got better
service during the “catch up” days
– Those who did call during those catch up days noticed nothing
– It cost an awful lot of money to catch up – for no benefit
(except punitive)
• I realize that if you are an outsourcer or a utility, there are serious
penalties for not hitting goals
• That does not mean it is a good idea service-wise
© 2012 Bay Bridge Decision Technologies, Inc. All Rights Reserved. Proprietary and Confidential
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13. So what would be a better metric?
• ASA is slightly (22%) better in this scenario
• There is a WFM trend to manage service by “% intervals met”
– The incentive then is to save costs by missing peaks and averaging poor
service peak intervals with high service valley intervals
– CenterBridge weighs service by “minute” or “volume weighted”
• Heavy volume intervals have more impact
• No “games” (and you are right staffed anyway)
The problem with service contracts
– If you use outsourcers, does it make any sense to hold them to it? (don’t we
want our partners to succeed??)
– Why would we do it to ourselves (Burn overtime hours and spend $57K with
little real benefit?)?
– If an outsourcer, can we discuss the folly with our partners? Work on a better
contract?
© 2012 Bay Bridge Decision Technologies, Inc. All Rights Reserved. Proprietary and Confidential
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14. Occupancy as Efficiency
Occupancy is
at 88% this What? They are sitting around doing
month nothing for 12% of their time??
They are waiting
for a call to arrive We can cut your
budget by 12%
Finance
WFM
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15. Occupancy measures economies of scale!
Higher volume,
Occupancy at 70% SL = 72%
Lower volume,
Occupancy at 70% SL = 62%
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16. Occupancy does not measure true efficiency
Service Level
is too high and
Look how inefficient your
occupancy low!
operation is!
Let’s have a team
meeting! Wow! You are
efficient again!
Finance
WFM
© 2012 Bay Bridge Decision Technologies, Inc. All Rights Reserved. Proprietary and Confidential
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17. So what are better measures of efficiency?
Via Michele Borboa and Duke Witte (and, hence, in CenterBridge!):
– Occupied to Staffed Time: Occupancy
– Staffed to Worked Time: Of the time in the building, how much time agents
are available for contacts? (measures on premise “other stuff”)
– Worked to Paid Time: Of the time being paid, how much time is being
spent in the building? (measures on premise to off premise efficiency)
– Occupied to Worked Time: The ratio of time in the building to time on the
phone (measures on premise other stuff and economies of scale)
– Occupied to Paid Time: Of the total paid hours, how much time is spent on
the phone?
– Staffed to Paid Time: Of the total paid hours, how much time are agents are
available for contacts? (my bet: this is the best efficiency metric)
© 2012 Bay Bridge Decision Technologies, Inc. All Rights Reserved. Proprietary and Confidential
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18. How do we as an industry determine how many agents we need?
Most: Erlang C
Some: Assumed Occupancy Workload Calculation
Fewer, but growing: Discrete-event simulation
© 2012 Bay Bridge Decision Technologies, Inc. All Rights Reserved. Proprietary and Confidential
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19. What’s wrong with assuming occupancy first?
(words of wisdom from Steve Martin)
If you know the right number of
people, you know the
occupancy. If you know
occupancy, you know the right
staff.
Guessing the occupancy is the
Steve Martin, Call Center Planning Savant same thing as guessing the right
number of staff!
(Occupancy is a result of hiring, overtime, undertime, and controllable shrinkage
decisions)
© 2012 Bay Bridge Decision Technologies, Inc. All Rights Reserved. Proprietary and Confidential
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20. Erlang over-staffs all the time. Sometimes a lot. Sometimes not.
Erlang vs Actual Staffing Requirements
200
180
Effective Staff Required
160
actual
140
erlang
120
100
80
60
9 11 13 15 17 19 21
Hour of Day
Erlang vs Actual Staffing Requirements
450
400
Effective Staff Required
350
300
Depending on workload 250
calculations or Erlang will 200
150
make your FTE 100 actual
requirements a guess 50 erlang
0
9 11 13 15 17 19 21
Hour of Day
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21. A properly validated model (this is discrete-event simulation)
Tip: Validation of your
analytic process breeds
confidence in both your
analyses, and you! Make
validation a regular part of
your planning meetings–
even if everyone is tired of
reading how smart you are!
© 2012 Bay Bridge Decision Technologies, Inc. All Rights Reserved. Proprietary and Confidential
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22. Service level to staff for back office
• Long service times (e.g. 80% responded within 24 hours)
• Do our normal ways of calculating service levels make sense?
– Forecast each time period
– Determine staffing independently
Example (80% / 24hrs):
Day 1 Day 2 Day 3 Day 4 Day 5 Day 6
Volume 1000 1000 1000 1000 1000 1000
Staff Required 200 200 200 200 200 200
Overflow Volume 0 200 400 600 800 1000
Volume (including
overflows) 1000 1200 1400 1600 1800 2000
Volumes grow and grow!
© 2012 Bay Bridge Decision Technologies, Inc. All Rights Reserved. Proprietary and Confidential
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23. What about changing our normal way of staffing?
• Forecast each time period
• Determine staffing knowing the overflow
Day 1 Day 2 Day 3 Day 4 Day 5 Day 6
Volume 1000 1000 1000 1000 1000 1000
Staff Required 200 240 245 245 245 245
Overflow Volume 0 200 240 248 249.6 249.9
Volume (including
overflows) 1000 1200 1240 1248 1249.6 1249.9
Volumes reach close to a steady state!
We’ve spent a fair amount of time studying this: a better
method is to staff to complete the work in a 24 hour period
© 2012 Bay Bridge Decision Technologies, Inc. All Rights Reserved. Proprietary and Confidential
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24. Forecast Error
Forecast everything (attrition, wage rate, each shrinkage category,…)!
An error rate of 5% of call volume
is equal to an error rate of 3% of
shrinkage!
There is a relative value
associated with each
forecast’s error.
The value of each
forecast’s accuracy is
represented by the amount
of service level error that
the performance driver
forecast produces (you can
determine this using
sensitivity analyses graphs)
© 2012 Bay Bridge Decision Technologies, Inc. All Rights Reserved. Proprietary and Confidential
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25. Forecasting is one Piece of the Planning Life Cycle
“The end result is not a forecast, but a plan” -- Duke Witte, Wyndham Hotel Group
THIS is the result of your forecast!
Error rates associated with the forecast is not nearly
as important as the errors associated with your plan!
© 2012 Bay 25
Bridge Decision Technologies, Inc. All Rights Reserved. Proprietary and Confidential
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26. Lets Discuss Forecasting Error Ask yourself- which of
these forecasting
models will lead me to
Its good to measure forecast error, but DO a more reasonable
NOT GET HUNG UP ON IT business Decision?
Rule of thumb: always be suspicious when
someone touts a forecast method based
upon fancy error formulas. Statisticians are
notorious for measuring the wrong things
The real measure of forecast error is risk to
the organization- either in service or cost
Example: One method may have great
goodness of fit, but be off more during peak
periods- this error will overstaff
significantly, when determining your hiring
plan
In order to measure forecast risk, you need
an accurate staff/capacity planning method.
© 2012 Bay Bridge Decision Technologies, Inc. All Rights Reserved. Proprietary and Confidential
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27. A quick example of forecast error: Which forecast is best?
Forecast #1 Versus Actual Volume Forecast #3 Versus Actual Volume
120,000 120,000
Actual Volume
Actual Volume
Forecast #1
100,000 100,000 Forecast #3
80,000 80,000
Call Volume
Call Volume
60,000 60,000
40,000 40,000
20,000 20,000
0 0
J F M A M J J A S O N D
M
M
M
M
O
O
J
J
J
J
J
J
J
J
F
F
N
N
D
D
A
A
A
A
S
Tim e S Tim e
Forecast Mean Error Mean RMSE Comments
Forecast #2 Versus Actual Volume Absolute
Error
120,000
Actual Volume Number 1 No Bias High Very High By and Far
100,000 Forecast #2 Variability The Worst
and Low Finish
80,000
Confidence
Call Volu me
Number 2 Small Under- Low Low The Winner!
60,000
forecast Bias Variability
40,000
and High
Confidence
20,000
Number 3 Small Over- Low Low A Close
forecast Bias Variability Second
0 and High Place Finish!
J F M A M J J
Tim e
A S O N D Confidence
What about Business Risk?? 27
© 2012 Bay Bridge Decision Technologies, Inc. All Rights Reserved. Proprietary and Confidential
28. A quick example of forecast error: Which forecast is best?
Forecast #1 Versus Actual Volume Forecast #3 Versus Actual Volume
120,000 120,000
Actual Volume
Actual Volume
Forecast #1
100,000 100,000 Forecast #3
80,000 80,000
Call Volume
Call Volume
60,000 60,000
40,000 40,000
20,000 20,000
0 0
This method staffs perfectly, just a week late This method understaffs at peak
J F M A M J J A S O N D
M
M
M
M
O
O
J
J
J
J
J
J
J
J
F
F
N
N
D
D
A
A
A
A
S
Tim e S Tim e
Forecast #2 Versus Actual Volume
120,000
Actual Volume
Assessing business risk:
100,000 Forecast #2
80,000
Which forecasting
Call Volu me
technique would cause
60,000
40,000
more harm to the
20,000
company?
0
J This method overstaffs at peak
F M A M J J
Tim e
A S O N D
© 2012 Bay Bridge Decision Technologies, Inc. All Rights Reserved. Proprietary and Confidential
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29. An over/under analysis
600
Number of Agents
500 Expected Requirements Versus Staffed
Over under is only 400
half of the picture– 300
the cost of hitting our
200
Staffed
goal. Is that the only Agents
100
decision we can
make? (A: Nope.) 0
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51
Week
The other half of the
picture is operational
performance The number of agents The number of agents
expected. required by week staffed, using
hiring, overtime, undertime, t
raining, etc…
And the difference? Our over/under picture!
© 2012 Bay Bridge Decision Technologies, Inc. All Rights Reserved. Proprietary and Confidential
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30. Evaluating risk requires a (validated) simulation
With simulation, you can change
anything and see resulting
service (and vice versa).
Accurately.
Service: ASA, SL,
Abandon, Occupancy
© 2012 Bay Bridge Decision Technologies, Inc. All Rights Reserved. Proprietary and Confidential
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31. Final Thoughts
• Just because you have the power to do something, it doesn’t mean you should:
Penalties for missing service should be used sparingly- why would you want your
outsources to “catch up” and take a meaningless cost hit?
• Similarly, construct smart contracts: Our metrics and our contracts may create
some counterproductive behaviors
• Challenge conventional wisdom: Metrics, such as occupancy and service level are
easy to get, but may not measure what we think (i.e. efficiency)
• “How we’ve always done it” should be challenged when it comes to new contact
types: Just because our methods worked for call centers does not mean that they
will for contact centers
• Make sure you focus on the decision: Interim metrics like forecast error should not
take priority over analyzing the best staffing decision given we know there will be
variability
© 2012 Bay Bridge Decision Technologies, Inc. All Rights Reserved. Proprietary and Confidential
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32. Supporting Tools: CenterBridge
• CenterBridge is a contact center forecasting, strategic planning, and what-if
analysis system. It helps you, for example:
– Forecast all center planning metrics
– Quickly develop budget plans that are accurate and generate savings. Automatically
produce variance analysis
– Perform risk and sensitivity analysis of your contact center
– Set optimal service levels
– Evaluate center investments, consolidation, and growth opportunities.
• CenterBridge compliments tactical workforce management tools by focusing on
strategic decision making
• Uses a patent-pending, customized discrete-event simulation model of your
contact center (not Erlang equations) to drive analysis
© 2012 Bay Bridge Decision Technologies, Inc. All Rights Reserved. Proprietary and Confidential
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33. Contact Us!
Ric Kosiba
Ric@BayBridgeTech.com
410-224-9883
… if you would like a copy of the slides or to see a
quick CenterBridge demonstration
Also! We have a white paper, Contact Center Planning: Agility
is Key, available for download at:
www.BayBridgeTech.com
© 2012 Bay Bridge Decision Technologies, Inc. All Rights Reserved. Proprietary and Confidential
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Notas do Editor Steve martin joke about getting a million dollarsYou.. can be a millionaire.. and never pay taxes! You can be a millionaire.. and never pay taxes! You say.. "Steve.. how can I be a millionaire.. and never pay taxes?" First.. get a million dollars. Now.. you say, "Steve.. what do I say to the tax man when he comes to my door and says, 'You.. have never paid taxes'?" Two simple words. Two simple words in the English language: "I forgot!“Tell USAir story of figuring out everything! Assumed occupancy.