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Contact Center Metrics, Contact Center Planning, and How our Metrics Can Lead Us Down the Wrong Path
Contact Center Metrics, Contact Center Planning, and How our Metrics Can Lead Us Down the Wrong Path
Contact Center Metrics, Contact Center Planning, and How our Metrics Can Lead Us Down the Wrong Path
Contact Center Metrics, Contact Center Planning, and How our Metrics Can Lead Us Down the Wrong Path
Contact Center Metrics, Contact Center Planning, and How our Metrics Can Lead Us Down the Wrong Path
Contact Center Metrics, Contact Center Planning, and How our Metrics Can Lead Us Down the Wrong Path
Contact Center Metrics, Contact Center Planning, and How our Metrics Can Lead Us Down the Wrong Path
Contact Center Metrics, Contact Center Planning, and How our Metrics Can Lead Us Down the Wrong Path
Contact Center Metrics, Contact Center Planning, and How our Metrics Can Lead Us Down the Wrong Path
Contact Center Metrics, Contact Center Planning, and How our Metrics Can Lead Us Down the Wrong Path
Contact Center Metrics, Contact Center Planning, and How our Metrics Can Lead Us Down the Wrong Path
Contact Center Metrics, Contact Center Planning, and How our Metrics Can Lead Us Down the Wrong Path
Contact Center Metrics, Contact Center Planning, and How our Metrics Can Lead Us Down the Wrong Path
Contact Center Metrics, Contact Center Planning, and How our Metrics Can Lead Us Down the Wrong Path
Contact Center Metrics, Contact Center Planning, and How our Metrics Can Lead Us Down the Wrong Path
Contact Center Metrics, Contact Center Planning, and How our Metrics Can Lead Us Down the Wrong Path
Contact Center Metrics, Contact Center Planning, and How our Metrics Can Lead Us Down the Wrong Path
Contact Center Metrics, Contact Center Planning, and How our Metrics Can Lead Us Down the Wrong Path
Contact Center Metrics, Contact Center Planning, and How our Metrics Can Lead Us Down the Wrong Path
Contact Center Metrics, Contact Center Planning, and How our Metrics Can Lead Us Down the Wrong Path
Contact Center Metrics, Contact Center Planning, and How our Metrics Can Lead Us Down the Wrong Path
Contact Center Metrics, Contact Center Planning, and How our Metrics Can Lead Us Down the Wrong Path
Contact Center Metrics, Contact Center Planning, and How our Metrics Can Lead Us Down the Wrong Path
Contact Center Metrics, Contact Center Planning, and How our Metrics Can Lead Us Down the Wrong Path
Contact Center Metrics, Contact Center Planning, and How our Metrics Can Lead Us Down the Wrong Path
Contact Center Metrics, Contact Center Planning, and How our Metrics Can Lead Us Down the Wrong Path
Contact Center Metrics, Contact Center Planning, and How our Metrics Can Lead Us Down the Wrong Path
Contact Center Metrics, Contact Center Planning, and How our Metrics Can Lead Us Down the Wrong Path
Contact Center Metrics, Contact Center Planning, and How our Metrics Can Lead Us Down the Wrong Path
Contact Center Metrics, Contact Center Planning, and How our Metrics Can Lead Us Down the Wrong Path
Contact Center Metrics, Contact Center Planning, and How our Metrics Can Lead Us Down the Wrong Path
Contact Center Metrics, Contact Center Planning, and How our Metrics Can Lead Us Down the Wrong Path
Contact Center Metrics, Contact Center Planning, and How our Metrics Can Lead Us Down the Wrong Path
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Contact Center Metrics, Contact Center Planning, and How our Metrics Can Lead Us Down the Wrong Path

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If you would like to rile up a contact center meeting, naively ask the group “Which is better, Service Level or Average Speed of Answer?” Now, step back and watch the fireworks. …

If you would like to rile up a contact center meeting, naively ask the group “Which is better, Service Level or Average Speed of Answer?” Now, step back and watch the fireworks.

We use many metrics when we develop plans: some standard metrics include forecast error, occupancy and schedule efficiency, abandon rate, capture rate, as well as average speed of answer, and service levels. We do so because we are trying to describe very complex operational performance in simple terms.

But simple metrics can create unusual management behavior.

In this session, we will describe some of the standard contact center metrics and show how they have botched some very big operations. We will conclude the session with ways in which you can improve contact center planning and reporting.

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  • 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.
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    • 1. CONTACT CENTER METRICS, CONTACTCENTER PLANNING(and How Our Choice of Metrics Make Us Do Silly Things)Ric KosibaPresidentBay 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 2
    • 3. OverviewThis 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 © 2012 Bay Bridge Decision Technologies, Inc. All Rights Reserved. Proprietary and Confidential 3
    • 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” © 2012 Bay Bridge Decision Technologies, Inc. All Rights Reserved. Proprietary and Confidential 4
    • 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 © 2012 Bay Bridge Decision Technologies, Inc. All Rights Reserved. Proprietary and Confidential 5
    • 6. What does this cost the company in overtime? 22 FTEAbout 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 6
    • 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 7
    • 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 © 2012 Bay Bridge Decision Technologies, Inc. All Rights Reserved. Proprietary and Confidential 8
    • 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) © 2012 Bay Bridge Decision Technologies, Inc. All Rights Reserved. Proprietary and Confidential 9
    • 10. It is also difficult and costly to maintain a 10 Second ASA 18 Sec 13 Sec 10 Sec 8 Sec 3 Sec © 2012 Bay Bridge Decision Technologies, Inc. All Rights Reserved. Proprietary and Confidential 10
    • 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 11
    • 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 12
    • 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 13
    • 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 © 2012 Bay Bridge Decision Technologies, Inc. All Rights Reserved. Proprietary and Confidential 14
    • 15. Occupancy measures economies of scale! Higher volume, Occupancy at 70% SL = 72% Lower volume, Occupancy at 70% SL = 62% © 2012 Bay Bridge Decision Technologies, Inc. All Rights Reserved. Proprietary and Confidential 15
    • 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 16
    • 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 17
    • 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 18
    • 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 theSteve 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 19
    • 20. Erlang over-staffs all the time. Sometimes a lot. Sometimes not. Erlang vs Actual Staffing Requirements 200 180Effective 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 © 2012 Bay Bridge Decision Technologies, Inc. All Rights Reserved. Proprietary and Confidential 20
    • 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 21
    • 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 6Volume 1000 1000 1000 1000 1000 1000Staff Required 200 200 200 200 200 200Overflow Volume 0 200 400 600 800 1000Volume (including overflows) 1000 1200 1400 1600 1800 2000 Volumes grow and grow! © 2012 Bay Bridge Decision Technologies, Inc. All Rights Reserved. Proprietary and Confidential 22
    • 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 23
    • 24. Forecast ErrorForecast 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 24
    • 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 25
    • 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 26
    • 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 ConfidenceCall 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 0This 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 28
    • 29. An over/under analysis 600 Number of Agents 500 Expected Requirements Versus StaffedOver under is only 400half of the picture– 300the cost of hitting our 200 Staffedgoal. Is that the only Agents 100decision we canmake? (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 WeekThe other half of thepicture is operationalperformance The number of agents The number of agentsexpected. 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 29
    • 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 30
    • 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 31
    • 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 32
    • 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 33

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