For outsourcers or contact centres with highly skilled agents or relatively low volume queues in a single campaign, Erlang can prove particularly problematic when it comes to FTE stacking errors looking at combined view requirements.
Put simply, forecasting and scheduling lets you down, costing substantially more than it needs to and erodes what is an already tight margin in low volume or outsourced scenarios.
The issue arises when the Erlang requirements for each individual queue are calculated and then added together, resulting in an overestimation of how many agents are needed to service all the queues because each queue’s requirements are rounded up to the nearest whole agent. So on 10 queues you could be up to 9 heads over. On a high volume queue needing 100+ agents an extra body doesn’t impact, but on a low volume queue it can significantly hurt the margin. It gets worse as we know. Typically in low volume queues, they can be identified as mission critical, needing higher skilled agents (higher per agent cost = higher margin erosion) and are less elastic on service levels - making this factor difficult to mitigate by playing the service level game.
Further, whilst Erlang does a good job of modelling queuing voice or chat media for a single queue in isolation, the reality is that single-skill and single channel contact centres are a distant memory. In multi-skill campaigns utilising multi-channels the modelling errors this introduces can quickly compound (or stack) to make FTE forecasting this way ineffective, the campaign inefficient and thus the business uneconomic. This issue is especially apparent when viewing the combined view of many low volume queues, as often seen with outsourcers. Where for instance a single campaign for a handful of outsourced customers, each with a small number of low volume queues, ends up being a substantial volume of calls among one or two dozen queues. Experience says this campaign may only require 50 FTEs at most, but the stacking errors can result in an FTE requirement prediction of several hundred. It has been seen and of course is obviously wrong and treated as such. What if though the requirement prediction was 75? It’s enough over to kill the campaign margin and business justification stone dead but low enough to drag you into a poor decision. That’s even before we get in to how good the spend on forecasting and scheduling was.
The problem is of course how to account for partial agent utilisation between different queues. U-WFM recognise this problem and attacks it on two clear fronts:
In the forecasting phase where higher level modelling is used, U-WFM can tell you what your maximum and minimum FTE requirements will be for each queue, as well as any combination. So as an outsourcer you can quickly find the range of agent requirements for a set of queues in only a few clicks. The minimum is found quite easily using the assumption that all agents have all skills. The maximum is not quite the opposite. Our algorithm uses a few rules to come up with a combined value depending on agent skills and queue settings, based on U-WFM’s feedback, experience and skills in the field. This is obviously of limited usability but enables you to see the bare range requirements and whilst there still is some stacking error, it’s not as dramatic as other WFM applications.
In the scheduling phase our proprietary algorithm directly addresses the issues and models agents between different queues and distributes allocated time based on skillset and queue demand in the right way. Our smart solution utilises a representation of fractional agents. This allows the necessary granularity and clarity such that the combination of agent FTE will always add up to the actual number of agents needed, with negligible stacking error. Essential for you as a business and the benefits do not stop there. Our solution can therefore also be used with confidence to divide up costs between outsourced customers in the same campaign, or provide a better understanding of under or over-utilisation of skills and/or skillsets and thus where to focus agent training. A real win-win.
In summary, U-WFM provides the smart solution by keeping margins relevant and enabling you to control your business better in the omnichannel world across all volumes.
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