Call Center Management Featured Article
May 08, 2009
WFM + Cross-Trained Agents = Improved Call Center Management
One of the most challenging tasks call center managers face is balancing the number of agents needed against the number of calls or contacts flowing in during any given shift.
Fortunately, most of today’s workforce management solutions have analytics capabilities that allow managers to forecast, with surprising accuracy, how many contacts will be coming in on any given day, thus enabling them to schedule the proper number of agents. This capability -- a key advantage over spreadsheet systems -- is critical for achieving effective call center management, as labor is the single biggest costs facing any call center and agent time must be used efficiently. Put too many agents on a shift with not enough contacts coming in and you’ll have a bunch of agents sitting idly at their desks while on the clock. Fail to have enough agents on a shift and you’ll jeopardize service levels.
Fortunately, most of today’s workforce management solutions have analytics capabilities that allow managers to forecast, with surprising accuracy, how many contacts will be coming in on any given day, thus enabling them to schedule the proper number of agents. This capability -- a key advantage over spreadsheet systems -- is critical for achieving effective call center management, as labor is the single biggest costs facing any call center and agent time must be used efficiently. Put too many agents on a shift with not enough contacts coming in and you’ll have a bunch of agents sitting idly at their desks while on the clock. Fail to have enough agents on a shift and you’ll jeopardize service levels.
Beyond the ability to forecast the proper number of agents needed, today’s WFM systems also improve call center management by enabling managers to schedule agents based on their unique skill sets. For example, certain groups of agents might be trained only to handle contacts from customers who have purchased specific products or who are using specific services. Or a particular group of agents might be trained for handling “premium” customers while another handles only “new” customers.
Similarly, agent groups might be broken down based on the technologies or applications they’re trained use. For example, some agents might be trained to handle Web chats while other are trained to handle emails. Some might be experts at using legacy applications, while other might be more adept at using newer applications.
The beauty of today’s WFM solutions is that they enable managers to schedule agents based on skill set. For example, using the system’s forecasting capabilities, a manager of a multi-channel contact center can determine not only how many phone calls will be coming in, but also how many emails or Web chats, based on historical data gleaned from the ACD or integrated IP contact center platform. The system can then forecast how many agents from each group are needed to handle the forecasted number of emails and Web chats.
This allows for extremely granular scheduling of agents – in essence, it enables the manager to schedule the appropriate number of agents for each mode of contact, and build a team that is more precisely tailored for each shift. As such, labor efficiencies are realized, yet service levels remain unaffected.
To bring even further flexibility to agent scheduling, Monet Software, a leading provider of Web-based WFM solutions, recommends that all contact center cross-train their agents to handle different types of contacts and different types of customers. If you have agents trained to handle multiple skills and use skill-based routing, you can reduce the number of agents needed to handle your call volume. The productivity gain from giving each agent two skills can easily be 10 to 15 percent, the company claims.
The importance of multi-skilled agents is that they form overlapping groups. For example, having one group that can handle calls type A and B (News - Alert) while another group takes calls type C and D, can be substantially improved by adding a group that is able to handle calls type B and C (or one of the other three combinations). This model provides greater flexibility and is especially useful in times of fewer resources and changing call volumes and patterns.
The same principle also applies to channel type: Centers which cross-train their agents to handle different types of contacts (phone, Web chat, email), sometimes referred to as “universal agents,” have a distinct flexibility advantage over centers which train certain agents to only handle specific channels. By having more agents with multiple skills sets, a call center manager can actually get more out of the WFM system, because there will be even greater flexibility in terms of tailoring the agent groups needed for any given shift.
As such, cross training agents can be one of the more effective ways to cut costs in the call center while at the same time keeping service levels intact. Especially when those cross trained agents are being scheduled via a full featured, on-demand WFM system!
Similarly, agent groups might be broken down based on the technologies or applications they’re trained use. For example, some agents might be trained to handle Web chats while other are trained to handle emails. Some might be experts at using legacy applications, while other might be more adept at using newer applications.
The beauty of today’s WFM solutions is that they enable managers to schedule agents based on skill set. For example, using the system’s forecasting capabilities, a manager of a multi-channel contact center can determine not only how many phone calls will be coming in, but also how many emails or Web chats, based on historical data gleaned from the ACD or integrated IP contact center platform. The system can then forecast how many agents from each group are needed to handle the forecasted number of emails and Web chats.
This allows for extremely granular scheduling of agents – in essence, it enables the manager to schedule the appropriate number of agents for each mode of contact, and build a team that is more precisely tailored for each shift. As such, labor efficiencies are realized, yet service levels remain unaffected.
To bring even further flexibility to agent scheduling, Monet Software, a leading provider of Web-based WFM solutions, recommends that all contact center cross-train their agents to handle different types of contacts and different types of customers. If you have agents trained to handle multiple skills and use skill-based routing, you can reduce the number of agents needed to handle your call volume. The productivity gain from giving each agent two skills can easily be 10 to 15 percent, the company claims.
The importance of multi-skilled agents is that they form overlapping groups. For example, having one group that can handle calls type A and B (News - Alert) while another group takes calls type C and D, can be substantially improved by adding a group that is able to handle calls type B and C (or one of the other three combinations). This model provides greater flexibility and is especially useful in times of fewer resources and changing call volumes and patterns.
The same principle also applies to channel type: Centers which cross-train their agents to handle different types of contacts (phone, Web chat, email), sometimes referred to as “universal agents,” have a distinct flexibility advantage over centers which train certain agents to only handle specific channels. By having more agents with multiple skills sets, a call center manager can actually get more out of the WFM system, because there will be even greater flexibility in terms of tailoring the agent groups needed for any given shift.
As such, cross training agents can be one of the more effective ways to cut costs in the call center while at the same time keeping service levels intact. Especially when those cross trained agents are being scheduled via a full featured, on-demand WFM system!
Patrick Barnard is a contributing writer for TMCnet. To read more of Patrick’s articles, please visit his columnist page.
Edited by Patrick Barnard