PEC-WFM/Current/Administrator/Forecasting - Revision history

WikiSysop at 18:45, May 24, 2022

2022-05-24T18:45:50Z

Ed.jamer@genesys.com: Text replacement - "Genesys Cloud" to "Genesys Cloud CX"

2021-11-09T21:27:56Z

Text replacement - "Genesys Cloud" to "Genesys Cloud CX"

Mary.hachey@genesys.com: Published

2021-03-23T19:42:27Z

Published

Mary.hachey@genesys.com: Published

2021-03-23T19:40:39Z

Published

WikiSysop: Text replacement - "\|Platform=([^\|])GenesysEngage-onpremises([\|])" to "|Platform=$1GenesysEngage-cloud$2"

2020-07-25T02:34:09Z

Text replacement - "\|Platform=([^\|]*)GenesysEngage-onpremises([\|]*)" to "|Platform=$1GenesysEngage-cloud$2"

WikiSysop: Text replacement - "\|Platforms?=([^\|])PureEngage([\|])" to "|Platform=$1GenesysEngage-onpremises$2"

2020-07-15T11:45:29Z

Text replacement - "\|Platforms?=([^\|]*)PureEngage([\|]*)" to "|Platform=$1GenesysEngage-onpremises$2"

Mhachey: Published

2020-06-10T19:48:16Z

Published

Bgrenon: Published

2020-06-08T15:23:03Z

Published

New page

{{Article
|Standalone=No
|DisplayName=Forecasting
|TocName=Forecasting
|Context=Use the Forecasting tools to predict contact-center workload and staffing requirements, based on historical data or user-defined templates.
|Dimension=
|UseCase=
|ComingSoon=No
|Platform=PureEngage
|Role=Administrator
|Application=Workforce Administration
|Prereq=
|Section={{Section
|sectionHeading=
|anchor=
|alignment=Vertical
|Media=
|image=
|structuredtext=Use the Workforce Management (WFM) Forecasting tools to predict contact-center workload and staffing requirements, based on historical data or user-defined templates. WFM offers flexibility, by providing {{Link-SomewhereInThisVersion|manual=Administrator|topic=Forecasting|anchor=FlxFore|display text=multiple methods to forecast}} the workload and staffing requirements for work activities, including AI-powered forecasting.

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-formatted 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.
|structuredtextwide=
|FAQHeading=
|Status=No
}}{{Section
|sectionHeading=Using historical data
|anchor=HstData
|alignment=Vertical
|Media=
|image=
|structuredtext=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 Distributor (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.
|structuredtextwide=
|FAQHeading=
|Status=No
}}{{Section
|sectionHeading=Forecasting events
|anchor=ForeEvnts
|alignment=Vertical
|Media=
|image=
|structuredtext=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.
|structuredtextwide=
|FAQHeading=
|Status=No
}}{{Section
|sectionHeading=Setting service objectives
|anchor=SrvObj
|alignment=Vertical
|Media=
|image=
|structuredtext=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 the 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)
*Occupancy
*Maximum percentage of abandoned interactions
|structuredtextwide=
|FAQHeading=
|Status=No
}}{{Section
|sectionHeading=Flexible forecasting
|anchor=FlxFore
|alignment=Vertical
|Media=
|image=
|structuredtext=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:

*'''Automated'''—Use one of two WFM automated algorithms:
**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.
*{{Link-Standalone|topic=PEC-WFM/PECAIPwrd|display text=AI-Powered Forecasting}}—Good for large volumes of work activities, enabling access to more than 40 machine-learning trained models and automated, best-selection-method workflows to ensure accurate forecasts and improved productivity.
*'''Template-Based'''—Good for work activities with little historical information or for activities with very predictable interaction traffic.
*'''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.
|structuredtextwide=
|FAQHeading=
|Status=No
}}{{Section
|sectionHeading=Deferred-work forecasting
|anchor=DfrFore
|alignment=Vertical
|Media=
|image=
|structuredtext=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.
|structuredtextwide=
|FAQHeading=
|Status=No
}}{{Section
|sectionHeading=Multi-skill support
|anchor=MltSkl
|alignment=Vertical
|Media=
|image=
|structuredtext=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 {{Link-SomewhereInThisManual|topic=MltiForecst|anchor=top|display text=Multi Forecasting Primer}}.
|structuredtextwide=
|FAQHeading=
|Status=No
}}{{Section
|sectionHeading=AI-powered forecasting
|anchor=AIPwdFrcstg
|alignment=Vertical
|Media=
|image=
|structuredtext=Workforce Management features AI-powered forecasting, which is a sophisticated build method that leverages best practices in data science and the industry. Use this automated method to build volumes and AHT forecasts using the best-of-best forecasting algorithms provided by Genesys Cloud.

When AI-powered forecasting is enabled, WFM reads the historical data and sends it to Genesys Cloud where it calculates the forecast using the AI-powered method and its algorithms. Once completed, Genesys Cloud sends the forecast back to WFM, where the historical data is retained in the WFM database—it is not saved in Genesys Cloud.

===Enabling the feature===
AI forecasting is readily available in the latest WFM Supervisors Forecast UI and as a service in Genesys Cloud. To access this capability, contact your Genesys Professional Services representative.
|structuredtextwide=
|FAQHeading=
|Status=No
}}
}}

@@ Line 20: / Line 20: @@
 |anchor=AIPwdFrcstg
 |alignment=Vertical
-|structuredtext=Workforce Management features AI-powered forecasting, which is a sophisticated build method that leverages best practices in data science and the industry. Use this automated method to build volumes and AHT forecasts using the best-of-best forecasting algorithms provided by the Genesys Cloud platform.
+|structuredtext=Workforce Management features AI-powered forecasting, which is a sophisticated build method that leverages best practices in data science and the industry. Use this automated method to build volumes and AHT forecasts using the best-of-best forecasting algorithms provided by the Genesys Cloud CX platform.
-When AI-powered forecasting is enabled, WFM reads the historical data and sends it to the Genesys Cloud platform where it calculates the forecast using the AI-powered method and its algorithms. Once completed, the forecast is sent back to WFM, where the historical data is retained in the WFM database—it is not saved in the Genesys Cloud platform.
+When AI-powered forecasting is enabled, WFM reads the historical data and sends it to the Genesys Cloud CX platform where it calculates the forecast using the AI-powered method and its algorithms. Once completed, the forecast is sent back to WFM, where the historical data is retained in the WFM database—it is not saved in the Genesys Cloud CX platform.
 ===Enabling the feature===
-AI forecasting is readily available in the latest WFM Supervisors Forecast UI and as a service in the Genesys Cloud platform. To access this capability, contact your Genesys Professional Services representative.
+AI forecasting is readily available in the latest WFM Supervisors Forecast UI and as a service in the Genesys Cloud CX platform. To access this capability, contact your Genesys Professional Services representative.
 |Status=No
 }}{{Section

@@ Line 10: / Line 10: @@
 |Section={{Section
 |alignment=Vertical
-|structuredtext=Use the Workforce Management (WFM) Forecasting tools to predict contact-center workload and staffing requirements, based on historical data or user-defined templates. WFM offers flexibility, by providing {{Link-SomewhereInThisVersion|manual=Administrator|topic=Forecasting|anchor=FlxFore|display text=multiple methods to forecast}} the workload and staffing requirements for work activities, including AI-powered forecasting.
+|structuredtext=Use the Workforce Management (WFM) Forecasting tools to predict contact-center workload and staffing requirements, based on historical data or user-defined templates. WFM offers flexibility, by providing {{Link-SomewhereInThisVersion|manual=Administrator|topic=Forecasting|anchor=FlxFore|display text=multiple methods to forecast}} the workload and staffing requirements for work activities.
 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-formatted 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.
 |Status=No
 }}{{Section
@@ Line 38: / Line 27: @@
 **Expert Average Engine&mdash;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&mdash;Good for work activities with more than one year of historical data and accurate forecasting event information.
 *'''Template-Based'''&mdash;Good for work activities with little historical information or for activities with very predictable interaction traffic.
 *'''Copy Historical Data'''&mdash;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.

@@ Line 5: / Line 5: @@
 |Context=Use the Forecasting tools to predict contact-center workload and staffing requirements, based on historical data or user-defined templates.
 |ComingSoon=No
-|Platform=GenesysEngage-onpremises
+|Platform=GenesysEngage-cloud
 |Role=Administrator
 |Application=Workforce Administration

@@ Line 4: / Line 4: @@
 |TocName=Forecasting
 |Context=Use the Forecasting tools to predict contact-center workload and staffing requirements, based on historical data or user-defined templates.
 |ComingSoon=No
 |Platform=PureEngage
@@ Line 22: / Line 20: @@
 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-formatted 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.
 |structuredtextwide=
 |FAQHeading=
@@ Line 64: / Line 97: @@
 *Occupancy
 *Maximum percentage of abandoned interactions
 |structuredtextwide=
 |FAQHeading=
@@ Line 117: / Line 130: @@
 Because of this, the value for staffing can be expressed as a fraction. For details, see {{Link-SomewhereInThisManual|topic=MltiForecst|anchor=top|display text=Multi Forecasting Primer}}.
 |structuredtextwide=
 |FAQHeading=

PEC-WFM/Current/Administrator/Forecasting - Revision history

WikiSysop at 18:45, May 24, 2022

Ed.jamer@genesys.com: Text replacement - "Genesys Cloud" to "Genesys Cloud CX"

Mary.hachey@genesys.com: Published

Mary.hachey@genesys.com: Published

WikiSysop: Text replacement - "\|Platform=([^\|]*)GenesysEngage-onpremises([\|]*)" to "|Platform=$1GenesysEngage-cloud$2"

WikiSysop: Text replacement - "\|Platforms?=([^\|]*)PureEngage([\|]*)" to "|Platform=$1GenesysEngage-onpremises$2"

Mhachey: Published

Bgrenon: Published

WikiSysop: Text replacement - "\|Platform=([^\|])GenesysEngage-onpremises([\|])" to "|Platform=$1GenesysEngage-cloud$2"

WikiSysop: Text replacement - "\|Platforms?=([^\|])PureEngage([\|])" to "|Platform=$1GenesysEngage-onpremises$2"