Difference between revisions of "PE-GPR/9.0.0/Deployment/quickStart"

From Genesys Documentation
Jump to: navigation, search
(Published)
 
(5 intermediate revisions by 3 users not shown)
Line 5: Line 5:
 
|Context=To use Genesys Predictive Routing (GPR), you'll need to install and configure the products and components listed below.
 
|Context=To use Genesys Predictive Routing (GPR), you'll need to install and configure the products and components listed below.
 
|ComingSoon=No
 
|ComingSoon=No
|Platform=PureEngage
+
|Platform=GenesysEngage-onpremises
|Prereq=
 
 
|Section={{Section
 
|Section={{Section
 
|sectionHeading=Install
 
|sectionHeading=Install
 
|anchor=Install
 
|anchor=Install
 
|alignment=Vertical
 
|alignment=Vertical
|Media=
 
|image=
 
 
|structuredtext=#Install any Genesys components that aren't already part of your environment. You'll need:
 
|structuredtext=#Install any Genesys components that aren't already part of your environment. You'll need:
 
#*[https://docs.genesys.com/Documentation/FR Genesys Framework]
 
#*[https://docs.genesys.com/Documentation/FR Genesys Framework]
Line 23: Line 20:
 
#**Data Loader is deployed in a Docker container and requires that you deploy a supported version of Docker.
 
#**Data Loader is deployed in a Docker container and requires that you deploy a supported version of Docker.
 
#*{{Link-SomewhereInThisManual|topic=cfgSubroutines|anchor=top|display text=URS Strategy Subroutines}}
 
#*{{Link-SomewhereInThisManual|topic=cfgSubroutines|anchor=top|display text=URS Strategy Subroutines}}
|structuredtextwide=
 
|FAQHeading=
 
 
|Status=No
 
|Status=No
 
}}{{Section
 
}}{{Section
Line 30: Line 25:
 
|anchor=Configure
 
|anchor=Configure
 
|alignment=Vertical
 
|alignment=Vertical
|Media=
 
|image=
 
 
|structuredtext=To complete your setup of Predictive Routing, configure the following components:
 
|structuredtext=To complete your setup of Predictive Routing, configure the following components:
  
#Set the desired values for the configuration options, as described in the [https://docs.genesys.com/Documentation/Options/Current/GPM/Welcome Genesys Predictive Routing Options Reference].
+
#Set the desired values for the configuration options, as described in the configuration instructions for the {{Link-SomewhereInThisVersion|manual=Deployment|topic=cfgSubroutines|display text=URS Strategy Subroutines component}} and {{Link-SomewhereInThisVersion|manual=Deployment|topic=DL-CfgFile|display text=Configure Data Loader to upload data}}. The complete list of all Predictive Routing configuration options is available from the [https://docs.genesys.com/Documentation/Options/Current/GPM/Welcome Genesys Predictive Routing Options Reference].
 
#:You use configuration options to configure a wide range of application behavior, including:
 
#:You use configuration options to configure a wide range of application behavior, including:
 
#*The mode GPR is running in, which might be <tt>off</tt>.
 
#*The mode GPR is running in, which might be <tt>off</tt>.
Line 42: Line 35:
 
#*Scoring thresholds, agent hold-out, and dynamic interaction priority.
 
#*Scoring thresholds, agent hold-out, and dynamic interaction priority.
 
#*Many other important functions.
 
#*Many other important functions.
#To configure how the match between interactions and agents is determined, follow the procedures accessed from the {{Link-SomewhereInThisVersion|manual=Help|topic=ConfigPredictModels|display text=Create and Test Predictors and Models}} page, located in the ''Predictive Routing Help''.
+
#(STAFF users only) To configure how the match between interactions and agents is determined, follow the procedures accessed from the {{Link-AnywhereElse|product=PE-GPR|version=Staff|manual=Help|topic=ConfigPredictModels|display text=Create and Train Predictors and Models}} page, located in the ''Predictive Routing Help''.
 
{{AnchorDiv|import}}
 
{{AnchorDiv|import}}
|structuredtextwide=
 
|FAQHeading=
 
 
|Status=No
 
|Status=No
 
}}{{Section
 
}}{{Section
|sectionHeading=Review your Data
+
|sectionHeading=Review your data
 
|anchor=ImportData
 
|anchor=ImportData
 
|alignment=Vertical
 
|alignment=Vertical
|Media=
+
|structuredtext=Data Loader draws interaction and agent data from the Genesys Info Mart Database.  
|image=
 
|structuredtext=The Data Loader draws interaction and agent data from the Genesys Info Mart Database.  
 
  
Using Data Loader, you can import customer and outcome data compiled in CSV format. The GPR application enables you to view and analyze your uploaded data.   
+
Using Data Loader, you can also import agent, customer, and outcome data compiled in .csv format. The GPR web application enables you to view and analyze your uploaded data.   
  
The resulting data is uploaded to the Platform as Datasets. The agent and customer Datasets are given the particular names of ''Agent Profile'' and ''Customer Profile''.  A ''Dataset'' is a collection of raw event data. The primary purpose of a Dataset is to be the source of Predictor data.
+
The resulting data is uploaded to the GPR Core Platform as datasets. The agent and customer datasets are given the particular names of ''Agent Profile'' and ''Customer Profile''.  A ''dataset'' is a collection of raw event data. The primary purpose of a dataset is to be the source of predictor data.
  
*You configure the Dataset schemas and upload parameters using configuration options set on the Data Loader Application object.
+
*You configure the dataset schemas and upload parameters using configuration options set on the Data Loader <nowiki><tt>Application</tt></nowiki> object.
*After the Dataset has been imported, you can append additional data as long as it is consistent with the schema that has already been established.
+
*After Data Loader imports a dataset, you can append additional data as long as it is consistent with the schema that has already been established.
|structuredtextwide=
 
|FAQHeading=
 
 
|Status=No
 
|Status=No
 
}}{{Section
 
}}{{Section
|sectionHeading=Create Predictors and Models
+
|sectionHeading=Create predictors and models
 
|anchor=CreatePredictorsandModels
 
|anchor=CreatePredictorsandModels
 
|alignment=Vertical
 
|alignment=Vertical
|Media=
+
|structuredtext='''Note:''' Only STAFF users can create predictors and models.
|image=
 
|structuredtext=Predictors are based on the Dataset information that you have imported.
 
  
*A Predictor defines a view on that underlying Dataset. It can select from some or all of the data in the Dataset; you can use a Predictor with multiple Datasets.
+
Predictors are based on the dataset information that you have imported.
*A Model is based on a Predictor, and uses the same target metric or KVP as that Predictor. You can configure multiple Models for each Predictor. These Models can use different selections of the features available in the underlying Dataset. Models are the objects actually used to perform agent scoring and interaction matchups.
 
  
As you configure a Predictor, you can choose which metric you want to work with, what kinds of situations you want to evaluate, and other parameters, constructing a way to determine the Next Best Action in the specified situation, based on the possible actions available at that time. As you gather more data, you can add that new data to your Dataset, and have the Predictor test against the actual results coming in, enabling you to refine how successful your Predictor is.
+
*A ''predictor'' defines a view on that underlying dataset. It can select from some or all of the data in the dataset; you can use a predictor with multiple datasets.
|structuredtextwide=
+
*A model is based on a predictor, and uses the same target metric or KVP as that predictor. You can configure multiple models for each predictor. These models can use different selections of the features available in the underlying dataset. Models are the objects actually used to perform agent scoring and interaction matchups.
|FAQHeading=
+
 
 +
As you configure a predictor, you can choose which metric you want to work with, what kinds of situations you want to evaluate, and other parameters, constructing a way to determine the Next Best Action in the specified situation, based on the possible actions available at that time. As you gather more data, you can add that new data to your dataset, and have the predictor test against the actual results coming in, enabling you to refine how successful your predictor is.
 
|Status=No
 
|Status=No
 
}}{{Section
 
}}{{Section
|sectionHeading=Reporting and Analysis
+
|sectionHeading=Reporting and analysis
 
|anchor=ReportingandAnalysis
 
|anchor=ReportingandAnalysis
 
|alignment=Vertical
 
|alignment=Vertical
|Media=
 
|image=
 
 
|structuredtext=You can report on various parameters, such as:
 
|structuredtext=You can report on various parameters, such as:
  
Line 94: Line 77:
  
 
*The Predictive Routing interface. See {{Link-SomewhereInThisVersion|manual=Help|topic=Reports|display text=Monitor trends and performance}} in the ''Predictive Routing Help'' for specific information.
 
*The Predictive Routing interface. See {{Link-SomewhereInThisVersion|manual=Help|topic=Reports|display text=Monitor trends and performance}} in the ''Predictive Routing Help'' for specific information.
*Genesys Interactive Insights/GCXI, as part of the Genesys historical reporting offering. The following reports are available in the ''Historical Reporting with Genesys CX Insights'' documentation:  
+
*Genesys CX Insights (GCXI), as part of the Genesys historical reporting offering. The following reports are available in the ''Historical Reporting with Genesys CX Insights'' documentation:  
 
**[https://all.docs.genesys.com/PEC-REP/Current/RPRT/HRCXIGPRAHTQ Predictive Routing - AHT & Queue Dashboard]
 
**[https://all.docs.genesys.com/PEC-REP/Current/RPRT/HRCXIGPRAHTQ Predictive Routing - AHT & Queue Dashboard]
 
**[https://all.docs.genesys.com/PEC-REP/Current/RPRT/HRCXIGPRMdlEfcncy Predictive Routing - Model Efficiency Dashboard]
 
**[https://all.docs.genesys.com/PEC-REP/Current/RPRT/HRCXIGPRMdlEfcncy Predictive Routing - Model Efficiency Dashboard]
Line 102: Line 85:
 
**[https://all.docs.genesys.com/PEC-REP/Current/RPRT/HRCXIPredictiveOprtnl Predictive Routing Operational Report]
 
**[https://all.docs.genesys.com/PEC-REP/Current/RPRT/HRCXIPredictiveOprtnl Predictive Routing Operational Report]
 
**[https://all.docs.genesys.com/PEC-REP/Current/RPRT/HRCXIPredictiveQStat Predictive Routing Queue Statistics Report]
 
**[https://all.docs.genesys.com/PEC-REP/Current/RPRT/HRCXIPredictiveQStat Predictive Routing Queue Statistics Report]
*Pulse, where the Agent Group KPIs by Predictive Model and Queue KPIs by Predictive Model templates for real-time reporting are available from the {{#Widget:ExtLink|link=http://community.demo.genesys.com/pulse/templates|displaytext=Genesys Dashboard Community Center}}.
+
*Pulse, which provides the Agent Group KPIs by Predictive Model and Queue KPIs by Predictive Model templates for real-time reporting.
|structuredtextwide=
 
|FAQHeading=
 
 
|Status=No
 
|Status=No
 
}}
 
}}
 
}}
 
}}

Latest revision as of 12:45, March 31, 2022

To use Genesys Predictive Routing (GPR), you'll need to install and configure the products and components listed below.

Related documentation:

Install

  1. Install any Genesys components that aren't already part of your environment. You'll need:
  2. Interaction Concentrator
  3. Genesys Info Mart
  4. Install Genesys Predictive Routing, which consists of the following components:
    • Data Loader—Imports agent and interaction data automatically from the Genesys Info Mart Database. Uploads customer and outcome data from user-prepared CSV files.
      • Data Loader is deployed in a Docker container and requires that you deploy a supported version of Docker.
    • URS Strategy Subroutines

Configure

To complete your setup of Predictive Routing, configure the following components:

  1. Set the desired values for the configuration options, as described in the configuration instructions for the URS Strategy Subroutines component and Configure Data Loader to upload data. The complete list of all Predictive Routing configuration options is available from the Genesys Predictive Routing Options Reference.
    You use configuration options to configure a wide range of application behavior, including:
    • The mode GPR is running in, which might be off.
    • Schemas and other configuration for Data Loader and data uploads.
    • Login parameters and access URLs.
    • KPI criteria to decide what makes for a better match.
    • Scoring thresholds, agent hold-out, and dynamic interaction priority.
    • Many other important functions.
  2. (STAFF users only) To configure how the match between interactions and agents is determined, follow the procedures accessed from the Create and Train Predictors and Models page, located in the Predictive Routing Help.

Review your data

Data Loader draws interaction and agent data from the Genesys Info Mart Database.

Using Data Loader, you can also import agent, customer, and outcome data compiled in .csv format. The GPR web application enables you to view and analyze your uploaded data.

The resulting data is uploaded to the GPR Core Platform as datasets. The agent and customer datasets are given the particular names of Agent Profile and Customer Profile. A dataset is a collection of raw event data. The primary purpose of a dataset is to be the source of predictor data.

  • You configure the dataset schemas and upload parameters using configuration options set on the Data Loader <tt>Application</tt> object.
  • After Data Loader imports a dataset, you can append additional data as long as it is consistent with the schema that has already been established.

Create predictors and models

Note: Only STAFF users can create predictors and models.

Predictors are based on the dataset information that you have imported.

  • A predictor defines a view on that underlying dataset. It can select from some or all of the data in the dataset; you can use a predictor with multiple datasets.
  • A model is based on a predictor, and uses the same target metric or KVP as that predictor. You can configure multiple models for each predictor. These models can use different selections of the features available in the underlying dataset. Models are the objects actually used to perform agent scoring and interaction matchups.

As you configure a predictor, you can choose which metric you want to work with, what kinds of situations you want to evaluate, and other parameters, constructing a way to determine the Next Best Action in the specified situation, based on the possible actions available at that time. As you gather more data, you can add that new data to your dataset, and have the predictor test against the actual results coming in, enabling you to refine how successful your predictor is.

Reporting and analysis

You can report on various parameters, such as:

  • The success of your predictors.
  • The results of A/B testing.
  • The factors affecting a KPI you are trying to influence.

Reports are available through the following reporting applications:

Comments or questions about this documentation? Contact us for support!