Skill-based routing (SBR) has been around almost since the dawn of call centers. Since then, call centers have become multi-channel contact centers and the move towards omnichannel communications means that SBR is more relevant than ever.
What is skills-based routing?
SBR enables calls and other contacts to be queued simultaneously to more than one group of agents, optionally in a preferred order according to rules defined by the administrator. SBR routes each call to the first available agent with the requisite skill(s), regardless of whether that agent has one, two, or more skills, with priority given to specific contact types if desired.
Why skills-based routing is ‘A Good Thing’
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- Customers are more likely to have their issue resolved on the first call.
- Handling time is reduced because calls are routed to the agents best equipped to handle them.
- Agent occupancy and productivity are boosted since there is less idle time between calls compared to a single-skill scenario with a dedicated team of agents for each call type.
- SBR eliminates the need to fire-fight by moving agents from one call type to another to follow differing peaks in workload. The system automatically finds the best resource for the workload mix, in real-time.
Having teams of agents dedicated to each contact type offers no pooling gains. Consequently, in some centers, the goal is to train every employee to be a ‘universal agent’ who can handle any type of call. This will deliver the maximum possible pooling effect and the greatest schedule efficiency. Unfortunately, it is often unrealistic, because:
- It typically requires weeks or months of training before an agent can handle all contact types proficiently.
- Some calls require a special personality, for example, sales and collections.
- The handling of some contact types requires certification that takes months or years to obtain.
- Staff turnover is a fact of life in every call center, so in many cases, the nirvana of a fully multi-skilled universal agent workforce can never be reached.
SBR helps centers to utilize staff in a way that may not be fully universal but is certainly more efficient than individual dedicated teams. New hires can be trained on the different call types one by one, progressing once they have demonstrated mastery and gained confidence by handling the calls that are routed to them.
No free lunch
There are however some downsides to SBR from the planning point of view. For example:
- Shift swaps need to be handled with more care since there will be an impact on customer service unless agents swap with colleagues who have exactly the same skill combination.
- The same challenge applies to time off and overtime.
- Last but not least, the complexity of the workforce management (WFM) process soars.
A fundamental challenge that SBR poses to WFM is that of determining how many people are needed with each skill or combination of skills. In a single-skill or universal agent scenario, the Erlang C formula works well. When agents have a combination of skills, however, Erlang C is completely lacking, since it takes no account of agent skills; it assumes that all agents are identical. With SBR, the specific combination makes a big difference since there is a web of interdependencies. For example, hiring staff with skill X may not seem to address a shortage of resourcing in skill Y. But if there are agents with skills X and Y already on the payroll, the new agents will free up those agents with skill Y to take more calls.
Another challenge is around sickness. In a single-skill environment, planners and team leaders know just what to do to backfill for an agent calling in sick. With SBR, the set of skills that person has really matters. If the missing agent has three skills, must we find another person with those same three skills to replace them, or could we make it work if the substitute has only a subset of those skills? It’s easy to imagine a situation where we have calls arriving but there is no suitably skilled agent logged in.
WFM software applications approach these challenges in a variety of ways. Some require the user to make simplifying assumptions and handle multi-skilled agents in the same way as single-skilled agents. Others use sophisticated simulation models that mimic the exact mix of calls in each interval and experiment with different shift combinations for the multi-skilled agents on a trial-and-error basis until a solution is found. Simulation is certainly powerful but does have some downsides:
- The simulations can take many hours to run, even in a small- to medium-sized center. That’s not only inconvenient, it makes experimentation less practical. More importantly, it limits the planning team’s ability to quickly respond when actual contact volumes differ from the forecast or when several agents call in sick.
- Simulation relies on accurate forecasting of volume and AHT in each interval, since it models the arrival of the different contact types in each interval. How accurate is your forecast? Many centers pride themselves on achieving ±5% measured over a day or week but may have variance at interval level that is nearer 20%. The smaller the workload for a specific call type, the more likely it is that the variances will be high at the 15-minute interval level.
- What about the assumptions about which staff will actually be logged in for any given period? The simulation typically assumes that no agents will be absent on the day, which is obviously over-optimistic.
- The simulation will inevitably use a simplified version of the truth. If the schedules are built weeks in advance, when the day arrives, some agents may have left the business, new ones will have started and others trained on new skills. Even a schedule for the upcoming week may experience changes as agents request time off, team meetings are scheduled, and the myriad of day-to-day challenges are managed. The patterns of agent availability are not easily predicted in many centers.
- What if you change the routing logic in the ACD or contact routing platform? Many centers make frequent changes to routing rules or ‘vectors’ in response to changing business needs. These changes make the simulation results invalid and mean that the simulation must be reprogrammed - a labor-intensive and time-consuming process.
Another approach to creating schedules in an SBR environment is the use of sophisticated optimization algorithms, instead of simulation. Optimization offers a number of important benefits:
- Optimization builds schedules that take account of individual agent skills to an acceptable level of precision much more quickly than simulation. This speed enables timely, effective responses to changes in volume, call arrival patterns, or agent availability.
- Intraday adjustments can be calculated quickly and staff more precisely matched to demand when the actual workload and staff skill mix are known.
- The initial setup and ongoing maintenance effort are dramatically reduced. There is no need to update a complex configuration every time the routing logic is changed on the ACD.
While this approach may seem on the surface to be less precise than simulation, in reality the optimization is completed quickly and typically results in schedule efficiency equivalent or even superior to that offered by simulation. Schedule efficiency is a key WFM metric and it makes sense for scheduling to be as quick and easy for the user as possible, even with SBR. The advantages of optimization are clear.
Generating efficient schedules in an SBR environment is a difficult task. The challenges don’t stop with schedule building. The planning team will be faced with crosswinds such as changing volumes, staff sickness, and business changes that affect average handling time (AHT). Any of these effects may require the schedules to be revisited. Remember, the main goal of WFM is to consistently put the right number of people with the right skills in their seats at the right time. SBR adds a level of complexity to this, but savvy planners can overcome such challenges by choosing the right methods and tools.
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Originally published on May 19, 2021, updated on Jul 13, 2021.
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