Average Speed Of Answer
Also known as ASA. The average amount of time that it takes from the initial arrival of an interaction into the interaction management system to the delivery of that interaction to an endpoint—for example, the average amount of time that a caller will wait in queue for an agent to become available. The unit of measurement is influenced by the average length of each call, the number of agents who are available, and the total number of calls that are arriving.
In Premier Edition Cloud, this is calculated using the formula below.
Average Handle Time
Also known as AHT. The average amount of time an agent takes to respond to incoming interactions.
Known as Average Handling Time in Premier Edition Cloud, this is the average amount of time an agent takes to handle a call for this day. This statistic is counted for the day. The following is a list of valid agent activities for this state:
- Consult Call
- Internal Call
- Outbound Call
- Inbound Call
- After Call Work
- Inbound Call On Hold
- Outbound Call On Hold
Automatic Call Distributor
Also known as an ACD. A system that performs four basic functions: answering incoming calls, obtaining information and instructions from a database, determining the best way to handle the call, and sending the call to the proper agent as soon as one is available. In its software version, also known as Automatic Call Distribution.
Use the Workforce Management (WFM) Forecasting tool to predict contact-center workload and staffing requirements, based on historical data or user-defined templates. WFM provides multiple methods of forecasting the workload and staffing requirements for work activities in the following sections:
- Using Historical Data
- Using Forecasting Events
- Setting Service Objectives
- Flexible Forecasting
- Deferred-Work Forecasting
- Multi-Skill Support
Start by creating one or multiple forecast scenarios. Creating multiple scenarios enables you to see the effects of changes to forecasting parameters, such as service objectives and predicted interaction volume. When you have determined the most satisfactory forecast, publish it, making it the Master Forecast on which schedule scenarios, and eventually the Master Schedule, are based.
If you choose to, you can derive workload forecasts from historical information that is either collected automatically by WFM from the Genesys system or imported from .csv files using the WFM Web. You can also create workload and staffing forecasts as reusable templates. Once you have generated a workload prediction, WFM determines the staffing requirements needed to service the workload, taking into account any applicable service objectives.
For more information about importing historical data, see Forecasts > Import.
Using Historical Data
WFM automatically collects historical data from Stat Server for all work activities handled by the Genesys platform encompassing all media, contact segments, and service types. Using Genesys Stat Server, rather than automatic call distribution (ACD) reports, provides you with far greater flexibility in defining and gathering statistics that provide an appropriate measure of contact center performance over time.
WFM analyzes interaction volumes and average handling time (AHT) in order to predict future trends for each work activity. This data enables WFM to build accurate forecasts for the anticipated workload, and to calculate the staffing required to meet that workload.
Using the WFM API, you can also develop a custom application that will enable Interaction Volume and AHT data from a third-party system to be imported directly into the WFM database. This is useful if you want to use WFM to forecast and schedule a type of work that is not being routed by Genesys.
Using Forecasting Events
WFM can track events that might affect interaction volume. A sales promotion or marketing campaign, for example, might cause a predictable peak in interaction volume. These types of events are entered in WFM Web for Supervisors and used by the advanced WFM algorithms. If an event recurs, the forecasting algorithms learn the impact of that event and account for its impact in future forecasts.
Setting Service Objectives
With WFM forecasting, you can set specific service objectives. You can also adjust these objectives and then rebuild the forecast, which provides a detailed "what-if" analysis of the potential impact of staffing or service-objective changes. WFM forecasting uses parameters to determine effects of different service objective settings, such as:
- Interaction volumes
- Average handle time (AHT)
- Average speed of answer (ASA)
- Desired percentage of interactions handled within a target time (service level)
- Maximum percentage of abandoned interactions
WFM supports an unlimited number of forecasting scenarios, enabling you to create multiple forecasts and evaluate how changes in the parameters or the forecasting method that you use, affect expected service objectives. Resource planners can then easily create reliable forecasts, fine-tuning the results in tabular and graphical data views. You can also save forecast workforce data as templates for use in subsequent forecast building.
WFM offers several different forecasting methods of varying complexity:
- Template-Based—Good for work activities with little historical information or for activities with very predictable interaction traffic.
- Expert Average Engine—Good for work activities that have a reasonable amount of historical data or those that fluctuate more dramatically because of unknown factors.
- Universal Modeling Engine—Good for work activities with more than one year of historical data and accurate forecasting event information.
- Copy Historical Data—Good for work activities when you have some historical data, but not enough to use the Expert Average Engine or the Universal Modeling Engine. You can combine the historical data with overlap templates, which fill in gaps in the historical data.
- Use Value—Good for work activities if your site activity load is very regular. Applies a specific interaction volume or AHT to each time interval in the scenario.
WFM is designed to consider work activities that can be deferred, such as e-mail, as inherently different from immediate work, such as a phone call. WFM uses a proprietary algorithm designed to distribute the backlog of interactions that can be deferred across the day in order to satisfy your service goal, which is expressed in minutes, hours, or days.
Spreading out the deferred work enables you to avoid spikes in workload forecasts when a contact center opens for the day, or during brief periods of high volume.
A multi-skilled contact center presents an opportunity for increased productivity.
An agent might be idle in a single-skill environment, because she cannot answer calls that are queuing for a particular activity/skill that she possesses—because the schedule prevents her from using that skill.
In a multi-skilled environment, the agent can use her additional skills to answer calls. A multi-skilled agent is qualified to work on multiple activities, and therefore can perform different types of work during a shift.
In a multi-skill environment, an agent can be available for multiple activities during any timestep. The agent can be scheduled to work on an activity for only part of a timestep, and only the fraction of the time period during which she works is counted.
Because of this, the value for staffing can be expressed as a fraction. For details, see Multi Forecasting Primer.