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Are You Overstaffing Your Call Center?

by Ric Kosiba, Ph.D., Vice President, Interactive Intelligence's Decisions Group - March 30, 2016

Are You Overstaffing Your Call Center?
                                                               Ric Kosiba      
Vice President, Interactive Intelligence
 
 
Over the last fifteen years we have talked to numerous companies about simulation modeling and ways to make sure each call center’s staffing was "just right".  We covered just-in-time hiring, math models for finding the best balance between hiring, overtime, and controllable shrink, and how to manage the blend of activities associated with multi-channel agents.  We also discussed forecasting through seasonal peaks and valleys, and calculating the weekly staffing requirement.  And we are always asked…."But how do you know?" 
 
All contact centers are completely different.  There are multi-channel centers with agents handling phone, email, and chat in different combinations across different times of the day.  Many networks are multi-skilled with different agents handling different mixes of types of customer interactions.  Workflows are likely different from one company to another and vary daily at times. Each contact center network handles different sorts of customers, even in the same industry. 
 
The purposes of the contact are different and handle times reflect the distribution of contact arrivals, handle times, customer patience and their willingness to abandon a call.  Also the value of the customer interaction can be very different.
 
How do we know that our staffing requirement is correct?
Before we all turn to our capacity planning spreadsheets and attempt to answer this, let's think about what goes into explaining this seemingly simple question.
 
Knowing that we are staffed correctly requires that we have the appropriate service standards for each of our many contact types.  Should we run at 80 percent of all calls handled in 20 seconds for our customer service call center?  Is that "appropriate"?  Should it be longer or shorter? Should it be different by season?
 
There is so much that goes into this decision; the corporate mission and brand identity as a premium brand or low cost provider, customer expectations, the availability of alternative channels, the availability of competitive alternatives, the cost of servicing, and the revenues or perceived value of each contact. But determining the appropriate service goals for your contact centers is critical to being staffed correctly.  This usually single metric, the service goal, drives costs and revenues for sales centers.
 
Once we have a goal that we have analyzed and proven, the next step is to develop a process that performs four important steps:
 
·         First, gather appropriate and clean ACD, payroll, and workforce management historical time-series performance data and populate a contact center history database.  This data will be used to produce forecasts and validate the accuracy of our analytic processes.
·         Second, use this database to develop forecasts of all important contact center metrics; such as volumes, handle times, agent sick time and absence, agent attrition and customer experience scores such as Net Promoter Score, agent quality, or first call resolution.  It is important that we forecast at the appropriate level of detail, by center and staff group because each contact center will likely have a different seasonality associated with items like shrink and attrition.  Other trends and seasonality metrics may require completely different forecasting methods.
·         Third, apply a mathematical model to transform your volumes, handle times, and shrinkage forecasts into an interval by interval, and week over week staffing requirement that exactly hits the ideal service goals.  Many planners utilize an Erlang C equation or an assumed occupancy calculation to determine this staffing requirement. As more companies worry about the accuracy of those two methods, they are turning to discrete-event simulation to determine their staffing needs.
·         Finally, we must look at the staffing requirement and compare it to the expected agent staff availability week over week minus agent attrition and uncontrollable agent shrink.  We then determine when we are over or under staffed, and determine a hiring, overtime, under-time, and controllable shrinkage plan. Many capacity planners perform this step by hand, looking at an over/under-staffed chart and "guessing and testing" when to hire. This process is slow, inaccurate, and error prone.  There are, however terrific technologies, like integer programming, which will develop very efficient "just-in-time" plans.
 
The Right Plan
So, how do we determine with confidence that each step we are taking will yield the right plan?  Let's look at each of the four steps.
 
First, it is very important to validate and prove that our database has clean and consistent source data.  While this sounds pretty simple, this step requires that definitions of data, most often from different data sources are understood and consistent and that there is a method for validating the accuracy of contact center data.
 
Here is an "easy" check.  Since our database will include staffing, shrinkage, and ACD performance data, we can check to make sure that the topline staffing, minus agent shrinkage, totals to the bottom line staffed hours that our ACD records.  We know how many hours our agents were on a call or in a ready state, we keep track of shrink, and we know how many agents we have on our payrolls so we should be able to reconcile these. Any difference, we call "lost time" and it represents unaccounted agent time.  If lost time for your contact centers is relatively low, then your data is relatively clean.  In other words, you know your data is consistent.
 
The second step is to develop forecasts of all important plan drivers.  There are standard forecast "goodness" processes and metrics that can check the expected validity of a forecasting technique.  Typically the analyst holds out data from their time series history and applies a forecasting methodology (like Holt-Winters) to the historical time series data to develop a forecast. This forecast is compared to the "hold out" actual performance data.  There are a series of metrics such as root-squared mean error that can judge the accuracy of the forecast on this hold out data.
 
The third step involves converting volumes, handle times, and service goals into a week over week staffing requirement.  How do we know that this step is right?  It is actually a very simple process-- simply take several week's work of ACD data, plug the actual calls offered and handle times into the staffing methodology and plug the historical  service level achieved as the method's goal.  If the staff requirement predicted by the model is the same as the staff on hand for that historical time period, then the model is "validated".
 
But here is an observation: the Erlang equation always overstaffs; its requirement is always too high, when compared to actual performance.  This is why many contact center planners have switched to discrete-event simulation as their requirements generator of choice.  Simulation does not usually have an overstaffing bias.
 
The final step is determining a hiring and overtime plan.  This step is difficult to validate, without developing a perfect plan to compare.  The good news is that there are systems that will develop perfect just-in-time hiring plans for you using mathematical technologies such as integer programming.  But if you are using an integer program to determine how good your planning process is, then why not use the perfect plan in your operation? 
 
By checking and validating your planning process you will know that your forecasts are right and your staffing and hiring models are valid and optimal.  When your senior manager asks "How do you know?" you'll be able to show them.
 
Ric Kosiba, Ph.D., is vice president of Interactive Intelligence’s Decisions Group. He can be reached at Ric.Kosiba@InIn.com   or (410) 224-9883.

  

 
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