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Measuring and Reacting to Absenteeism as We Head into 2014

As industry leaders in the workforce planning profession, measuring data is our job description. Whether you are a consultant, planning and managing for your customer, or part of a workforce management team, the old saying, “you cannot improve what you cannot measure”, rings more true than ever. In today’s multichannel contact center, we are pushed to measure every detail of our customer’s behavior and our staff’s performance. Workforce management is a giant puzzle and the struggle to achieve a perfect schedule is built from many little pieces. I think I can speak for us all when I say that the one piece we are constantly driven mad by is absenteeism. It is the one piece of our puzzle we cannot measure and predict….or can we?

Absenteeism starts with coaching. It starts with us. As leaders in our contact centers we need to articulate the effects of absenteeism to our agents. We have probably all heard of “the ball game”. The Society of Workforce Planning Professionals (SWPP) and ICMI both teach this technique as a practical way to show phone agents the effects of not being in their seat when they were scheduled to be. It’s a simple game; put everyone in a circle and start passing balls in the circle. Have the CSR’s continue to pass the balls amongst each other. Slowly start removing people from the group to simulate agents going on break, etc. Soon balls will start dropping, just like calls will, and agents will not be able to keep up. This is a fun way of communicating the effect of not being where you should be at the right time. Supplement this exercise with a little math refresher. If a CSR can handle 80 calls a day, and 10 CSR’s call-out sick; that leaves 800 calls that need to be distributed amongst the rest of the team. If each call takes 5 minutes to handle, on this day, absenteeism created an additional 67 hours of calls needing to be answered. If we had 25 agents on hand, and the calls are distributed equally amongst them, each agent would need to handle an additional 32 calls that day. Those 32 calls represent an additional 2 hours and 40 minutes of work the rest of the team needs to handle. This is the message we need to drive home. Our employees will respond to the thought of burdening their teammates with extra work.

This is where the numbers come into play. We need to know what to look for and focus our attention on the opportunities we expose. See the chart below.

This graph represents hours of unplanned absence by month. The vertical axis shows total number of hours of absence in a particular labor unit. A data dive as simple as this can be used in a meeting amongst stakeholders to discuss why unplanned absences are so high in July. Once you do the math related to how many calls “could have” been handled if July was more in tune with the average of your months, you can give a rough estimate of the potential service level boost. This data can be powerful to stakeholders. Let’s look at another example.

In the graphs above, Friday is highlighted to show the obvious. As a workforce planner, we cannot have Friday be our highest “unplanned” absence day as well as our highest “planned” outage day. This is a clear example of a problem. When this is charted against service level, absenteeism will prove to be a major player in the unit’s performance. At the end of the year, information like this will help you plan your “allowances” for PTO. Knowing that Friday is your highest call-out day, you surely will have the data to lower the number of allowed FTE personal days for Fridays in the new year. Let’s look at a third example of the power of absentee data.

Again, this graph easily exploits a problem. Unfortunately, we expect to see an increase in partial absences as it gets later in the week, with Friday being highest on the list. Here, Wednesday contains the highest day-of-week partial absence count. Armed with this data, you need to take this to your contact center supervisors and team leads, seeking answers to unruly information. Looking for more answers to the shrinkage dilemma, brings us to another data set.


If you are using a workforce management tool or even a spreadsheet, sum all of your employee absences that are >8 hours. Using a pivot table, breakout your partial absences and graph, as seen above, to show a count of partial absences by teammate. This easily shows your floor leaders where they need to focus their coaching. It will stir the conversation pot during a scorecard session and hopefully help you unravel some partial absence mysteries. In the last graph, we get even more granular.


This exhibit shows the number of partial absences, broken out by length (in hours) of each partial absence. In this graph, the most common partial absence was 4-4.75 hours. This means most agents work at least half their shift before leaving sick. Looking at data like this, you might expose procedural loopholes. For example, if employees are not marked with an occurrence if they leave during the first hour of their shift, you will see a high count in the “>1” grouping. This can help drive a business case to lead a change with HR.

As a WFM professional, pivot tables are your best friend. They are a powerful, easy way to group and organize your information. The graphs above are meant to help get you pointed in the right direction when analyzing your shrinkage data.  They are intended to get you thinking about reasons for your absentee problems. It helps spur discussions on policy or behavior. You should not stop with high level analysis either. Some of the most interesting data is found at the most granular level. Look for patterns in CSR behavior. Breakout your shrinkage occurrences by day and see which employee has the most sickness by day-of-week. Look to see which CSR has the most paired Thursday-Friday, absences. Grab your employee handbook and look to see if three call-outs in a row equals one occurrence and see if you observe this type of behavior real-time. Be ready to show the impact to the customer, of policies such as this. Equipped with this information, you can drive change at your contact center. Shrinkage data helps give insight into the wildcard component of the perfect schedule. It teaches us that employees have habits and their behaviors are predictable. Looking at the big picture of your shrinkage numbers helps give you answers to the part of our business that is most difficult to predict…or was it, after all?