subject: Call Center Productivity -- Service Level Force Management Yields Increased Profitability [print this page] In highly productive call center environments, in which large numbers of agents and service representatives answer hundreds of inquiries daily, it's possible to predict exactly how many calls a company's call center will receive minute-by-minute, day-by-day. In turn, the workforce can then be scheduled for availability during periods when call volumes are at their highest or for assignment to other duties or for time off during predictably slow times. Sophisticated forecasting and scheduling tools have long been used in these large, high-volume call centers where enormous returns have justified an investment in complex technology.
Until recently, however, the cost and complexity of automated workforce management tools put them out of reach of many customer contact centers. Only an estimated 10% of call centers have elected to use them.
Even small call centers need effective scheduling
As the trend accelerates toward using the customer interaction center as the focal point for building and solidifying customer relationships many small to mid-size organizations are beginning to rethink their positions on automating their workforce management activities.
Today's customer seeks convenience and will pick up the phone, or click on your web page at any time of the day or night and from anywhere in the world. When a customer makes the effort to connect to a warm-blooded person, todays contact center manager has to make certain that a skilled agent is there to answer the question, take the order, and create a positive experience.
In a business that is customer driven, scheduling the right number of agents with the proper skills to handle all calls promptly has been proven to enhance customer satisfaction, promote repeat business and thus increase profits. The business benefits of accurate scheduling are not trivial to any company with a customer focus.
Obviously, understaffing causes poor customer service. Not so obvious is the way understaffing can have a snowball effect that drags service down ever lower. Agents who are overwhelmed by long queues of waiting customers and pressed to complete calls quickly tend to rush through calls in a way that leaves transactions incomplete and generates additional calls. If the overload continues, burnout sets in and call handling times actually increase. Longer call times then create longer queues and more stress for the agents in a vicious circle of overload and delay.
Forecasting call volume accurately is the first step in avoiding the snowball effect. Another step is creating a schedule flexible enough to match work hours, lunches, breaks, meetings, with the peaks and valleys of customer demand.
Having enough agents on average to handle the average number of customer calls is not enough; the challenge is to match agent schedules to the demands of the business on a day-to-day, hour-to-hour, even minute-to-minute basis. Even a small error in staffing can dramatically affect service levels.
It is very costly to not answer the door when customers come knocking. Conversely, it is very costly to overstaff when 70% of call center expense is payroll. Helping you control these costs and enhance customer satisfaction with its attendant profitability is what the new generation of workforce management software can do for you.
Good workforce management tools not only match the agents to the demand; they also make it easier to recruit the agents. The nature of the workforce is changing as people adapt their lifestyles to the nonstop economy. Employees are seeking more flexible hours than the traditional nine-to-five workday allowed. In a tight labor market, companies staffing contact centers can gain a competitive edge by initiating scheduling that goes beyond nine-to-five. This can be accomplished using workforce management software capable of setting up work assignments based on customer demand.
The new generation of force management tools - not just for big centers
A new generation of force management tools reduces the cost of force management technology and lowers the learning curve barrier to adoption. The new generation of workforce management is not be the preserve of a few specialists tending proprietary systems. The success of the Internet provides a model for the new generation of workforce management software. As millions of Internet users have discovered, a little basic typing and a few mouse-clicks puts a world-wide instant communications network at the command of even the most reluctant of technophobes.
While some corporations want to install and run the software at their computer centers, the option of Web-hosting allows a company to outsource maintenance and assure that the latest version of the software is available without any need for updating local servers. End users have the most current software available the minute they access the scheduling program via their browsers. The initial cost of purchasing and installing software is eliminated, greatly reducing the barriers to getting started.
Anyone familiar with a standard Web browser, such as Internet Explorer, is able to use the workforce management software. It is designed so the complexity of the technology is transparent to the users. A user does not need to know technical details of forecasting and scheduling in order to work with it successfully.
In the case of the new generation of scheduling software, easier is not less accurate. New approaches to programming, made possible by advances in computer science and increases in computing power allow us to schedule at a finer level of detail than ever before. These new programming techniques juggle all the complexities of work rules and employee availabilities to achieve the best possible match between the available workforce and the minute to minute flow of customer demand.
Special payoff for small teams
[The results presented in the examples we discuss below were achieved using the Irene forecasting and scheduling system.]
As experienced contact centers know, team size has an impact on team efficiency.
Small-team agents handle fewer calls per hour in order to get the same speed of answer performance as large teams. Here, we are assuming that the average call handling time is 3 minutes and that we want our calls answered with a 30-second average speed of answer.
Yet sometimes small teams are necessary. Often, it doesnt make sense to train the entire contact center staff to handle low volume specialized call types, and the better approach is to create a small team of specialists. However, if a dedicated small team is trained to handle only those special calls, call managers have discovered that they must operate this team very inefficiently to get reasonably good grades of service.
The Solution a multi-skilled team
You can combine the advantages of a small team of specialists with the efficiency of larger teams by organizing a multi-skilled team. With this approach some agents handle more than one type of call. This approach requires an ACD with the ability to direct calls to agents on the basis of the agents skill set (skills based routing)
The savings can be significant. Consider the following example:
A contact center handles two types of calls. - Type 1 and Type 2 volume. The Average Handling Time for both types of calls is 100 seconds. But Type 1 calls are low in volume. The load is 36 calls in 15 minutes, whereas the Type 2 load is 360 calls in 15 minutes. If we were to handle each queue with its own team, in order to get a 15 second average answer, we would need 6 agents to handle Type 1 calls and 43 agents to handle Type 2 calls, for a total of 49 agents.
Now suppose we set up a multi-skilled team where some are agents cross-trained to handle both types of calls. The theoretically optimal mixture of agents is 3 Type 1 agents, 34 Type 2 agents and 10 multi-skilled agents, for a total of 47 agents. This is a saving of 2 agents over deploying separate teams.
Our studies reveal that for small teams you can depart from the theoretical optimal solution and still gain much of the practical advantage. For example, with the in the case described above, the specialized Type 1 agentscould be replaced by multi-skilled agents with no loss of efficiency.
As in the previous example, we would require 6 agents to handle Type 1 calls if there were handled separately and 43 agents to handle the Type 2 calls separately. On the other hand, if we use configuration 2, we would need 37 Type 2 agents and 10 multi-skilled agents, for a total of 47 agents. This still results in a saving of 2 agents.
Rules of Thumb
What types of efficiency gains can be expected from the use of skill-based routing? First, we have to make some assumptions.
Our studies indicate that there are some configurations that are better than others when using a multi-skilled team. The following are some general guidelines/rules of thumb that may be useful in considering whether this form of skill-based routing is beneficial.
Optimal benefits are obtained when the size of the multi-skilled team is more than 20% of the total team required.
Optimal benefits are obtained when the sizes of the specialized teams are proportional to their respective loads. In the example above, the Type 2 load is 10 times that of the Type 1 load, so the Type 2 team should be about 10 times the size of the Type 1 team.
In some settings, switching back and forth between call types slows agents down so that it takes longer to handle either type of call. This can reduce the savings from using multi-skilled teams. If the multi-skilled team takes more than 5% longer to handle both types of calls, skill-based routing may not reduce the overall number of agents required.
Not all services are the same
In contact centers, one type of service is frequently more important than others. For example, a center may charge a premium for some services such as support desks. Perhaps priority is given to new customers rather than requests for information. There may be clients whose services must be handled first. One way to insure better quality service is to set up a special team for the premium service and staff it more liberally. However, this can cause inefficiencies if the premium service has a small call volume.
Alternatively, we can combine all services and serve them with a single team, thus gaining large team efficiencies, but setting the target service objective as the objective of the premium service (e.g. the smallest speed of answer objective). Again, this is done at the expense of agent efficiency. Sometimes however, the ACD permits call centers to give preferential treatment for some queues over others.
Priority Queues
Normally, a team of agents handles multiple queues (each of which may be dedicated to a specific service) by selecting the oldest call among the queues for servicing. Some ACDs allow users to artificially multiply the age of the queue entries by a specified input value. This is sometimes called and ageing factor.
By giving priorities to the premium services, it is possible to gain agent efficiencies while still meeting target answer objectives. It requires special analysis to determine agent requirements when priorities are in place. As our examples show, it is possible to save on agent requirements in a number of cases by using priority queueing.
Example 1 different answer objectives, medium sized team
Suppose we have two services, service 1 and service 2, and service 1 is a premium service requiring priority service. For purposes of an example, lets say that we want that the target average speed of answer for service 1 should be 10 seconds and that for service 2 should be 25 seconds. We need to set the ageing factor, or priority factor for service 1 five times higher than that for service 2, to get the kind of answer performance that we want. So, if we set the ageing factor for service 1 to be 10, we should set the ageing factor for service 2 to be 10 times less, or 1. (We can also select the ageing factors to be 20 and 2 respectively and this will also work.)
To complete the example, we will assume that the Average Work Times for the two services are the same, three minutes, and that the calling rate for the premium service is 10% of the total calling rate. We will assume that for a specific 15-minute period we expect 500 calls to occur. If we were to split the two service into separate teams, the premium team would require 14 agents and the team having regular attempts would require 94 agents, for a total of 108 agents.
Suppose we elect to handle both services with only one team of agents, but without any priority queueing. In this case, we have to provide a 10 second average speed of answer for both services, since we cannot differentiate them. We can determine that the required number of agents necessary to provide a 10 second average speed of answer for both services is 107 agents. These agents will be busy about 93.5% of the time. Notice that we save an agent over having two separate teams service two separate queues. In this case, the impact of pooling traffic and serving them with one team of agents outweighs the fact that we are providing better than required answer performance for our regular customers.
If we incorporate priority queueing and ageing factors as described above, you only need 104 agents to meet the target service objectives for both services. What this means is that service 1 will see, on average, a 10 second average speed of answer while service 2 will see a 25 second average speed of answer objective. And, compared with a single team with no priority, we save 3 agents. This is a nearly 3% saving (the average time agents are busy is now 95.8%).
Example 2 different answer objectives, small team
If the traffic load is smaller, the results of priority queueing tend to be somewhat better. Consider the same scenario as example 1, but suppose that instead of expecting 508 calls to occur in a 15-minute period, we expect about 50 calls to occur. In this case, if there were separate teams, it would require 3 agents to serve the premium service and 11 agents to serve the regular service, for a total of 14 agents.
If we combine the two services and serve them with only one pool of agents, in order to provide a 10 second average speed of answer for both services, 14 agents are needed. These agents will be busy about 74% of the time. In this particular case, there is no apparent gain from pooling.
If we incorporate priority queueing and ageing factors, only 13 agents are needed to meet the target service objectives for both services. This results in a saving of 1 agent, which is nearly an 8% saving over handling both services with one non-priority pool of agents.
Priority Queue Summary
Priority queueing for premium customers has several advantages. In all instances, it allows for better agent efficiencies (and hence cost savings) while still meeting grade of service objectives. By handling multiple services with one combined team, forecasting tends to be more accurate and performance more predictable.
Priority queueing provides agent efficiency gains in all scenarios, but as indicated, the amount of gain depends on several factors. Some general observations can be made about the amount of savings one can expect from using priority queueing:
Greatest benefits occur when overall team size is small.
Greatest benefits occur when the premium target answer is much smaller than that of the other services.
Greatest benefits occur when the average waiting time for premium service is smaller than of other services.
Greatest benefits occur when the calling rate of premium service is a small fraction of the total calling rate.
Small teams often benefit more than large teams
As the examples we have discussed show, the benefit of force management tools for small teams is often greater as a percentage of staff costs than the benefit large call centers realize. As it becomes easier and less expensive to use these tools, we expect more and more small contact centers to take advantage of these tools.
