Difference between revisions of "UseCases/Current/GenesysEngage-onpremises/SL06"

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{{SMART UseCase
 
{{SMART UseCase
−
|ID=
 
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|Title=
 
−
|Offering=
 
 
|SMART_Benefits={{SMART Benefits
 
|SMART_Benefits={{SMART Benefits
 
|UCBenefitID=Increased Revenue
 
|UCBenefitID=Increased Revenue
 
|UCBenefit=Machine learning-based matching of sales reps to prospects based on sales value directly increases revenue.
 
|UCBenefit=Machine learning-based matching of sales reps to prospects based on sales value directly increases revenue.
 
}}{{SMART Benefits
 
}}{{SMART Benefits
−
|UCBenefitID=Improved Net Promoter Score
+
|UCBenefitID=Improved Customer Experience
 
|UCBenefit=Routing prospects to the sales reps best able to handle their sales request improves the customer experience.
 
|UCBenefit=Routing prospects to the sales reps best able to handle their sales request improves the customer experience.
 
}}{{SMART Benefits
 
}}{{SMART Benefits
 
|UCBenefitID=Improved Employee Satisfaction
 
|UCBenefitID=Improved Employee Satisfaction
 
|UCBenefit=Increased sales success leads directly to improved satisfaction for sales reps.
 
|UCBenefit=Increased sales success leads directly to improved satisfaction for sales reps.
 +
}}{{SMART Benefits
 +
|UCBenefitID=Reduced Customer Churn
 +
|UCBenefit=Predictive Routing identifies the best agent for each customer interaction, reducing the likelihood of customer churn to protect revenues.
 
}}
 
}}
−
|UCIntro=This use case is based on[https://repository.docs.genesys.com/Draft:UseCases/Current/Genesys Engage/BO06 Genesys Predictive Routing for Customer Service (BO06) for Genesys Engage]
+
|UCIntro=This use case is based on {{Link-AnywhereElse|product=UseCases|version=Current|manual=GenesysEngage-onpremises|topic=BO06|display text=Genesys Predictive Routing for Customer Service (BO06) for Genesys Engage on-premises}}.
 +
 
 +
The capabilities described in this use case are under shipping control. Contact your Genesys representative for additional details.
 
|UCOverview=Business leaders want to improve their business Key Performance Indicators (KPI), leverage the innovation in Artificial Intelligence and drive business decisions with the abundance of data and context available in their business. Predictive Routing uses machine learning to support optimization of Sales KPIs.
 
|UCOverview=Business leaders want to improve their business Key Performance Indicators (KPI), leverage the innovation in Artificial Intelligence and drive business decisions with the abundance of data and context available in their business. Predictive Routing uses machine learning to support optimization of Sales KPIs.
  
−
A Sales KPI is a metric measuring the sales outcome of an interaction, in contrast to Service KPIs, which measure a Customer Experience or efficiency outcome. Sales KPIs can be a sales conversion rate, a sales revenue amount, a retention rate, a collection promise to pay. This use case focuses on improving revenue for inbound voice calls, but can also be extended to other sales-related KPIs, and other channel types with custom PS effort. The impacts of choosing another KPI or another channel type are documented in this use case wherever applicable.
+
A Sales KPI is a metric measuring the sales outcome of an interaction, in contrast to Service KPIs, which measure a Customer Experience or efficiency outcome. Sales KPIs can be a sales conversion rate, a sales revenue amount, a retention rate, a collection promise to pay. This use case focuses on improving revenue for inbound voice calls, but can also be extended to other sales-related KPIs. The impacts of choosing another KPI or another channel type are documented in this use case wherever applicable.
−
 
 
−
Predictive Routing also applies to optimize Services KPIs. See
 
  
−
[https://repository.docs.genesys.com/Draft:UseCases/Current/Genesys Engage/BO06 Genesys Predictive Routing for Customer Service (BO06) for Genesys Engage].
+
Predictive Routing also applies to optimize Services KPIs. See {{Link-AnywhereElse|product=UseCases|version=Current|manual=GenesysEngage-onpremises|topic=BO06|display text=Genesys Predictive Routing for Customer Service (BO06) for Genesys Engage on-premises}}.
  
−
Traditional routing is designed to match customers to agents through skills-based or group-based logic rather than improving KPI. Unlike traditional routing, Predictive Routing uses machine learning to find signals in historical data to build a predictive model. This model improves KPIs by ranking agents before making the match with customers. This model also addresses the operational challenges that occur in understaffing and overstaffing scenarios while balancing the service level with improving KPI.
+
Traditional routing is designed to match customers to agents through skills-based or group-based logic rather than improving KPI. Unlike traditional routing, Predictive Routing uses machine learning to detect patterns in historical data to build a predictive model. This model improves KPIs by ranking agents before making the match with customers. This model also addresses the operational challenges that occur in understaffing and overstaffing scenarios while balancing the service level with improving KPI.
  
−
Predictive Routing has built-in A/B Testing to demonstrate the uplift of the KPI provided through use of machine learning.  Predictive Routing leverages a variety of Genesys or third party data sources in order to build high quality predictors. In particular, Predictive Routing supports a native integration with Performance DNA in order to obtain a granular representation of the agent's profile.
+
Predictive Routing has built-in A/B Testing to demonstrate the uplift of the KPI provided through use of machine learning.  Predictive Routing leverages a variety of Genesys or third party data sources in order to build high quality predictors.
−
|UCSummary=Consider a retail bank that wants to upsell credit cards to its existing customers. Depending on the customer attributes (such as age or income), the bank wants to maximize both the conversion rate and the credit limit that the customer will accept, resulting in a higher overall revenue. This use case is based on a measure of sales revenue driven from a Sales reporting application (such as CRM).
+
|UCSummary=Consider a retail bank that wants to upsell credit cards to its existing customers. Depending on the customer attributes (such as income), the bank wants to maximize both the conversion rate and the credit limit that the customer accepts, resulting in a higher overall revenue. This use case is based on a measure of sales revenue driven from a Sales reporting application (such as CRM).
  
−
In this use case, we assume that a customer interaction is associated to a credit card offer, either from the explicit customer intention (from IVR, web, or mobile) or from a business rule (such as next best action, which is out of scope of this use case).
+
The underlying premise of this use case is that a customer interaction is associated to a credit card offer, either from the explicit customer intention from IVR, web, or mobile or from a business rule such as next best action.  Next best action is out of scope of this use case.
  
−
The Contact Center Manager or Business owner wants to increase overall revenue generated per agent. The Predictive Routing solution can assist in achieving this objective.
+
The Contact Center Manager or Business owner wants to increase overall revenue generated per agent. The Predictive Routing solution can help with achieving this objective.
  
 
Predictive Routing:
 
Predictive Routing:
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*Uses machine learning, a subset of Artificial Intelligence, to compare feedback of the actual outcome with the predicted outcome, helping to improve future agent-to-customer matches.
 
*Uses machine learning, a subset of Artificial Intelligence, to compare feedback of the actual outcome with the predicted outcome, helping to improve future agent-to-customer matches.
 
*Ranks agents predicted to maximize the expected revenue per interaction.
 
*Ranks agents predicted to maximize the expected revenue per interaction.
−
*Optionally customers can attach PDNA strand data to Agent profiles to improve their match with customers and intents based on their performance in trainings.
+
*Provides the finest grain match of customer contact with agent to help maximize revenue per agent. Provides an uplift on revenue using continuous learning to rank the expected revenue for agents servicing customers.
−
*Provides the finest grain match of customer contact with agent to help maximize revenue per agent.Provides an uplift on revenue using continuous learning to rank the expected revenue for agents servicing customers.
 
  
 
The direct result is that the average revenue per interaction increases. Predictive Routing usually also influences adjacent service KPIs like first contact sale, CSAT or NPS, handle time, and transfers. It is a common best practice to monitor all Sales KPIs and adjacent Service Levels to evaluate all impacts (out of scope of this use case).
 
The direct result is that the average revenue per interaction increases. Predictive Routing usually also influences adjacent service KPIs like first contact sale, CSAT or NPS, handle time, and transfers. It is a common best practice to monitor all Sales KPIs and adjacent Service Levels to evaluate all impacts (out of scope of this use case).
−
|Description=
 
 
|PainPoints=* Low revenue per agent or customer. Variability in business outcomes.​
 
|PainPoints=* Low revenue per agent or customer. Variability in business outcomes.​
 
* Frustrated customers or repeat interactions – cannot get access to an agent providing the proper proficiency on the offer​
 
* Frustrated customers or repeat interactions – cannot get access to an agent providing the proper proficiency on the offer​
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* A/B testing methodologies can be applied to evaluate lift​
 
* A/B testing methodologies can be applied to evaluate lift​
 
* Deep integration with Genesys routing and orchestration
 
* Deep integration with Genesys routing and orchestration
−
|BuyerPersonas=
 
−
|QualifyingQuestions=
 
 
|MaturityLevel=Differentiated
 
|MaturityLevel=Differentiated
 
|SellableItems=See Prerequisits
 
|SellableItems=See Prerequisits
−
|CloudAssumptions=
 
−
|CloudAssumptionsAdditional_Sales=
 
−
|PremiseAssumptions=
 
−
|PremiseAssumptionsAdditional_Sales=
 
 
|BusinessImageFlow={{SMART BusinessImageFlow
 
|BusinessImageFlow={{SMART BusinessImageFlow
−
|BusinessFlow=====Model Creation====
+
|BusinessFlow='''Predictive Routing for Sales'''
−
The following flow shows how a model is created. The main actor of this flow is typically a Business Analyst / Data Analyst in charge of the model creation. The Analyst is a trained professional from Genesys, a partner, or a customer organization.
+
 
−
|BusinessImage=https://www.lucidchart.com/documents/edit/a4257aa9-6e73-4dc8-95f7-f592c45d78ec/0
 
−
|BusinessFlowDescription=# The team Lead / Supervisor and the Analyst agree on the outcome metric to be used. This use case uses revenue optimization as the reference metric.
 
−
# The Analyst gathers customer profile, interaction profile, and agent profile data from Info Mart, and revenue data from sales records (such as CRM) or Info Mart, if captured in the data model.
 
−
# The Analyst analyses the data to determine correlating factors/predictors and verify if the data is suitable for a predictive model.
 
−
# The Analyst creates a predictive model based on the available data set.
 
−
# The Analyst reviews the quality of the predictive model and potential for uplift. If the quality is satisfactory, the model can be provisioned.
 
−
The modeling process described above may be extended to incorporate the following changes:
 
−
* Integration of additional 3rd-party data sources for customer profile (such as CRM), agent profile (such as WFO), content analysis data (speech or text), or outcome data (such as CRM or case management)
 
−
* Selection of other KPI(s) to optimize based on Info Mart data (such as AHT) or 3rd-party data (such as NPS)
 
−
The selection, analysis, and integration of this data into the predictive model requires a project-based implementation that is supported by the Predictive Routing product, but not described in this use case. Contact Genesys Professional Services for more details.
 
−
}}{{SMART BusinessImageFlow
 
−
|BusinessFlow=====Predictive Routing for Sales====
 
 
This business flow shows the use case from the perspective of the customer and agent.
 
This business flow shows the use case from the perspective of the customer and agent.
−
|BusinessImage=https://www.lucidchart.com/documents/edit/bd1c6f7f-98e5-441d-b014-929efe84e045/0
+
|BusinessImage=7deb8693-2294-4b64-8b50-9b3e115b9a89
−
|BusinessFlowDescription=# The customer contacts the company using one of the available inbound channels (such as voice, e-mail, chat, mobile, work item, or Apple Business Chat).* This inbound interaction may be the result of a proactive rule on a web or mobile application.
+
|BusinessFlowDescription=#The customer contacts the company using the inbound voice channel. This inbound interaction can be the result of a proactive rule on a web or mobile application.
−
# One of the Inbound use cases for the corresponding media type handles the interaction. The context data is captured depending on the interaction and engagement type.
+
#One of the Inbound use cases for the corresponding media type handles the interaction and captures interaction context data. The exact data captured depends on the interaction and engagement type.
−
# Genesys queues the interaction until at least one agent with the required skill(s) is available.
+
#Based on the interaction context, Genesys selects an initial group of agents with the required skill(s) as possible routing targets to handle the interaction.
−
# Predictive Routing ranks the agents against expected revenue for that specific interaction and returns a ranked list with values.
+
#Predictive Routing calculates the scores of the agents in the target group using a machine learning model that takes into account the agents' historic performance on similar interactions.
−
# Genesys checks if the rank for at least one agent is above the threshold.
+
#When there are multiple agents available, Genesys attempts to route the interaction to the available agent with a highest score.
−
# If no agents are available within the configured timeout, the routing expands the potential target pool of agents, such as by reducing the required skill level.
+
#If there is an interaction surplus and an agent becomes ready, Genesys selects an interaction from the queue taking into account the priority of each waiting interaction, the score the agent has for each interaction, and the time the interactions were queued.
−
# If yes, Genesys distributes to the best available agent based on the predictive model (the agent with the highest rank) and the routing rules.
+
#If no agents are available within the configured timeout, the routing strategy expands the potential target pool of agents by reducing the skill requirements and then repeats the target agent selection using Predictive Routing.
−
# The agent disconnects the interaction.
+
#After dealing with the customer call, the agent disconnects the interaction.
−
# The outcome is mapped to an InfoMart attribute (for example, a disposition code or custom key value pair)
+
#The outcome is mapped to Genesys Info Mart attribute (for example, a disposition code or custom key-value pair).
−
# Optional: The customer is offered a survey. The answer to the survey is stored in a third-party system.
+
#Optional: The customer is offered a survey. The answer to the survey is stored in a third-party system.
−
# Optional: Outcome data, such as CRM sales transaction completion and value, is produced and stored by a third-party application.
+
#Optional: Outcome data, such as case management closure, is produced and stored by a third-party application.
 
}}
 
}}
−
|BusinessLogic=====Parameters and Business Rules - Create Predictive Model====
+
|BusinessLogic='''Parameters and Business Rules – Predictive Routing Revenue'''
−
'''Model Step 1'''
 
−
* The business representative of the company decides on the KPI to be improved by Predictive Routing. This use case selects revenue as determined by the sales record system.
 
−
* The company decides which metrics to use, in this case the values for revenue.
 
−
* Other KPI types can be considered through a project-based implementation.
 
  
−
'''Model Step 2'''  
+
'''Routing Step 1''' The system creates an inbound interaction when a customer voice call begins. This use case supports inbound voice involving Genesys routing. See Use Case Interdependencies for details.
−
* The user gathers the sales revenue, agent profile, interaction profile and customer profile data from Genesys Info Mart and the Sales records system.
 
−
* Predictive Routing then ingests the data into the system as the basis for the predictive model.
 
−
* Other Genesys or 3rd-party data sources can be considered through a project-based implementation.
 
  
−
'''Model Step 3'''
+
*Precondition: This use case requires one or more use cases handling inbound interactions.
−
* The user assesses the data for predictability of revenue:
 
−
** The data is analyzed for variance in revenue by intention and agent. The results indicate whether a predictive model can generate significant improvement from the data.
 
−
** If Performance DNA is installed in customer environment, Agent profile is enriched with DNA strand data towards model training.
 
−
** Each data point is analyzed for its correlating factor to revenue.
 
−
** The user prepares to create the model.
 
  
−
'''Model Step 4'''
+
'''Routing Step 2'''  
−
* The system creates the predictive model to rank the revenue for customer-to-agent matching.
 
−
* The model can be made available for further analysis before provisioning into production.
 
  
 +
*The inbound interaction use case identifies the primary intention of the customer (Service Type) and the initial target skill expression is set.
 +
*Any required additional customer or agent profile data available to the interaction in run time can be integrated through a project-based implementation.
  
−
'''Model Step 5'''  
+
'''Routing Step 3'''  
−
* The potential uplift of revenue using the predictive model is analyzed for effectiveness and the predictive model is then provisioned for use within customer-to-agent matching
 
−
** The analyst evaluates the model to determine the potential uplift in revenue for this predictive model.
 
−
** The system shows the expected mathematical accuracy of the model through taking a percentage (such as 80%) of the data to train the model and then using the remaining percentage (such as 20%) of the data to predict the outcome with the model. Then the system compares the predicted results with the actual results and provides model accuracy information.
 
  
−
====Parameters and Business Rules – Predictive Routing Revenue====
+
*This step queues the interaction and is designed to cover both agent surplus and customer surplus scenarios. When either one or multiple agents are available (agent surplus scenario), the flow immediately proceeds. Otherwise, Genesys queues the interaction until an agent is available (customer surplus scenario). The system starts to balance the service level with the business KPI through maintaining priority.
−
'''Routing Step 1''' Through Inbound, Digital, Workload Management, and other use cases, the customer creates an interaction on any media type that is stored within Info Mart for the defined FCR metric. This use case supports inbound voice and email channels involving Genesys routing. See Use Case Interdependencies for details.
 
−
* Precondition: This use case requires one or more use cases handling inbound interactions.
 
  
−
'''Routing Step 2'''  
+
'''Routing Step 4'''  
−
* The inbound interaction use case identifies the primary intention of the customer (Service Type) and the initial target skill expression is set. The necessary predictors identified during model creation are available to the interaction and can be passed to Predictive Routing.
 
−
* Any required additional customer or agent profile data available to the interaction in run time can be integrated through a project-based implementation.
 
  
−
'''Routing Step 3'''
+
*Once one or more agents are available, the necessary Customer Profile, Interaction Profile, Agent Profile, and predictor information is passed to Predictive Routing as a scoring request. The request is processed by the relevant machine learning model, resulting in a score for each available agent for that interaction. This process caters to both customer surplus and agent surplus scenarios.
−
* This step queues the interaction and is designed to cover both agent surplus and customer surplus scenarios. When either one or multiple agents are available (agent surplus scenario), the flow immediately proceeds. Otherwise, Genesys queues the interaction until an agent is available (customer surplus scenario). The system starts to balance the service level with the business KPI through maintaining priority.
 
−
 
 
−
'''Routing Step 4'''
 
−
* Once one or more agents are available, the necessary Customer Profile, Interaction Profile, Agent Profile, and predictor information is passed toPredictive Routing to match the customer interactions against the agent for optimal outcome.This process caters to both customer surplus and agent surplus scenarios.
 
  
 
'''Routing Step 5'''  
 
'''Routing Step 5'''  
−
* The rank for each of the interactions against each of the agents is returned to routing to bias the customer-to-agent matching towards the agent(s) that can deliver the highest FCR.
 
−
* The system works in both customer and agent surplus modes. In a customer surplus scenario, the system delivers the highest priority interaction(s) and when multiple interactions are targeting the available agent, the highest priority and then the highest expected revenue score applies.
 
  
 +
*The rank for each of the interactions against each of the agents is returned to routing to weight the customer-to-agent matching towards the agent(s) that can deliver the highest revenue.
 +
*In an agent surplus scenario, the score of the highest ranked agent will be compared to the configured minimum score threshold.  If the agent score exceeds that threshold, the system routes the interaction.  If not, then the interaction is held, pending either a higher ranked agent becoming available, or the threshold reducing.
 +
*In a customer surplus scenario, where multiple interactions are waiting when an agent becomes available, the agent’s scores for each waiting interaction are compared to the minimum score threshold.  If the agent score exceeds the threshold for at least one interaction, the system routes the highest scoring interaction for that agent.  If not, then the agent remains unassigned, pending either a lower scored interaction becoming available, or the threshold reducing.
  
−
'''Routing Step 6'''  
+
'''Routing Step 6'''
−
* If at least one of the revenue values is above the threshold, the interaction is routed to the agent with the highest revenue.
 
−
* The system delivers the interaction normally, handling any ring on no answer and exception situations (applicable to voice, chat or email) as defined in the underlying use case.
 
−
* The customer and the agent are connected.
 
  
 +
*The minimum score threshold is reduced over time according to the pre-configured fallback strategy.
 +
*The checks in Routing Step 5 are repeated regularly until an agent or interaction is identified.
 +
*Normal target expansion, such as relaxing skill level as configured within the underlying distribution strategy, occurs.
 +
*The continual re-prioritization of the interaction also occurs as do any treatments and the queued customer experience.
  
 
'''Routing Step 7'''
 
'''Routing Step 7'''
−
* Optionally: If no appropriately ranked agent become available or the available agent does not have a sufficiently high expected revenue value, the value threshold is reduced after the configured amount of time.
+
 
−
* Normal target expansion, such as relaxing skill level as configured within the underlying distribution strategy, occurs.
+
*If at least one of the revenue values is above the threshold, the interaction is routed to the agent with the highest revenue.
−
* The continual re-prioritization of the interaction also occurs as do any treatments and the queued customer experience.
+
*The system delivers the interaction normally, handling any ring on no answer and exception situations (applicable to voice, chat or email) as defined in the underlying use case.
 +
*The customer and the agent are connected.
  
 
'''Routing Step 9'''
 
'''Routing Step 9'''
−
* The outcome of the interaction is captured through the agent desktop or a server-side process. Genesys APIs are invoked automatically or after an agent action to map the outcome to a Genesys interaction attribute: custom attached data or disposition code.  
+
 
−
* Info Mart captures this attribute with the Info Mart interaction record.
+
*The outcome of the interaction is captured through the agent desktop or a server-side process. Genesys APIs are invoked automatically or after an agent action to map the outcome to a Genesys interaction attribute: custom attached data or disposition code.
 +
*Info Mart captures this attribute with the Info Mart interaction record.
  
 
'''Routing Step 10'''
 
'''Routing Step 10'''
−
* Optionally, the customer receives a survey (the survey results are not connected with Genesys and are intended to evolve with the survey use cases)
+
 
−
* The survey is completed (optionally) and the outcome is collected and stored by a 3rd-party application.
+
*Optionally, the customer receives a survey (the survey results are not connected with Genesys and are intended to evolve with the survey use cases)
 +
*The survey is completed (optionally) and the outcome is collected and stored by a 3rd-party application.
  
 
'''Routing Step 11'''
 
'''Routing Step 11'''
−
* Optionally, the outcome data is produced and stored by third-party application.
 
  
−
====Scenarios with Performance DNA Integration (Optional)====
+
*Optionally, the outcome data is produced and stored by third-party application.<span id="Scenario_1:_Genesys_Predictive_Routing_and_Agent_profile_enrichment_with_PDNA_data" class="mw-headline"></span><span id="Scenario_1:_Genesys_Predictive_Routing_and_Agent_profile_enrichment_with_PDNA_data" class="mw-headline"></span>
−
=====<span id="Scenario_1:_Genesys_Predictive_Routing_and_Agent_profile_enrichment_with_PDNA_data" class="mw-headline"></span><span id="Scenario_1:_Genesys_Predictive_Routing_and_Agent_profile_enrichment_with_PDNA_data" class="mw-headline"></span><u>Scenario 1: Genesys Predictive Routing and Agent profile enrichment with PDNA data</u>=====
+
|DistributionLogic=The details of the distribution of an interaction to an agent are defined in the underlying inbound use cases. Refer to the preceding flow  to understand how Predictive Routing influences the distribution logic.
  
−
{{{!}} class="wrapped confluenceTable tablesorter tablesorter-default stickyTableHeaders wikitable"
+
Predictive Routing provides a routing lever that can be used to control how customer-to-agent matching behaves in customer surplus mode to distribute the interactions based on agent occupancy.
−
{{!}}- class="tablesorter-headerRow"
+
|CustomerInterfaceRequirements=There is no content applicable to this section.
−
! class="confluenceTh tablesorter-header sortableHeader tablesorter-headerUnSorted" style="width: 81.8125px;" scope="col" data-column="0"{{!}}Section Name
+
|AgentDeskRequirements=This use case does not include specific agent desktop requirements. During the routing phase, data is attached to the interaction that the agent can see.
−
! class="confluenceTh tablesorter-header sortableHeader tablesorter-headerUnSorted" style="width: 900.812px;" scope="col" data-column="1"{{!}}Description
+
|RealTimeReporting=Predictive Routing does not include real-time reports. Operational reports are available in the Predictive Routing UI.
−
{{!}}-
 
−
{{!}} class="confluenceTd" style="width: 81.8125px;"{{!}}Business Flow
 
−
{{!}} class="confluenceTd" style="width: 900.812px;"{{!}}
 
−
# The Team Lead / Supervisor and the Analyst agree on the outcome metric to be used. This use case uses Revenue as the reference metric.
 
−
# The Analyst gathers customer profile, interaction profile, agent profile, and Revenue data from Info Mart, and optionally from 3rd-party data sources.
 
−
# The Analyst evaluates PDNA data and<span class="inline-comment-marker valid" data-ref="fd39fe19-e136-4745-8d99-ee4099a05423">attaches it to Agent Profile.</span>Analyst is able to flexibly choose and decide which data he wants to pull from PDNA. PDNA could contain multiple agent performance metrics. Agent skill proficiency through Assessments and Learning Items is core meter to PDNA Performance evaluations.
 
−
# PDNA strands may also include data from QM systems grading agents on their performance in past interactions.
 
−
# The Analyst analyzes the data to determine correlating factors/predictors and verify if the data is suitable for a predictive model. PDNA data is key data source impacting Revenue prediction
 
−
# The Analyst creates a predictive model based on the available Info Mart data set, along with PDNA enriched Agent profile data
 
−
## The Analyst reviews the quality of the predictive model and potential for uplift. If the quality is satisfactory, the model is provisioned.
 
−
{{!}}-
 
−
{{!}} class="confluenceTd" style="width: 81.8125px;" colspan="1"{{!}}Operational Flow
 
−
{{!}} class="confluenceTd" style="width: 900.812px;" colspan="1"{{!}}
 
−
# First time model training<span class="inline-comment-marker valid" data-ref="eebbe85c-66ee-4530-97d7-dc0013231dba">assumes that PDNA data is available for historical interactions</span><br />
 
−
# Agents will have the PDNA data updated on a nightly basis.
 
−
# PDNA scores are recalculated based on new trainings that an agent undergoes
 
−
# For any agent going through new training their disjoint (local) model is de-activated and they get scored using a global model<span class="inline-comment-marker valid" data-ref="50d072a1-9fcc-42f3-a7ac-d1b770172212">until they build critical mass</span>of new interactions to retrain a local model
 
−
# Nightly PDNA data updates and model re-trainings happen automatically to keep Predictive Routing lift and accuracy up.
 
−
{{!}}-
 
−
{{!}} class="confluenceTd" style="width: 81.8125px;" colspan="1"{{!}}Reporting
 
−
{{!}} class="confluenceTd" style="width: 900.812px;" colspan="1"{{!}}New reports need to be defined to measure PDNA impact towards Predictive model performance and Lift
 
−
{{!}}}
 
  
 +
Operational reports include:
  
 +
*KPI Outcome
 +
*Feature Coverage
 +
*Model Accuracy
  
−
=====<u>Scenario 2:Daily Agent Survey using PDNA for improved scoring</u>=====
+
<br />
 +
|HistoricalReporting=The historical reports available through GCXI include the following:
  
−
{{{!}} class="relative-table wrapped confluenceTable tablesorter tablesorter-default stickyTableHeaders wikitable"
+
*Predictive Routing Operational Report - tracks Predictive Routing operational statistics.
−
{{!}}- class="tablesorter-headerRow"
+
*Predictive Routing A/B Testing Report - tracks A/B testing results for Predictive Routing models and predictors.<sup>*</sup>
−
! class="confluenceTh tablesorter-header sortableHeader tablesorter-headerUnSorted" scope="col" data-column="0"{{!}}Section Name
+
*Predictive Routing Agent Occupancy Report - tracks Agent Occupancy while Predictive Routing is being used to optimize routing.
−
! class="confluenceTh tablesorter-header sortableHeader tablesorter-headerUnSorted" scope="col" data-column="1"{{!}}Description
+
*Predictive Routing Daily Queue Statistics Report - tracks KPIs for each Queue while Predictive Routing is being used to optimize routing.
−
{{!}}-
+
*Predictive Routing Detail Report - provides interaction level detail data about Predictive Routing use and its impact on KPIs.
−
{{!}} class="confluenceTd" colspan="1"{{!}}Business Flow
 
−
{{!}} class="confluenceTd" colspan="1"{{!}}
 
−
# A customer data analyst has deployed a Revenue optimization model which uses PDNA data as input.
 
−
# PDNA application is configured by IT to survey Agents in the beginning of their shift
 
−
# PDNA calculates scores based on survey response of agents
 
−
# PDNA pushes latest strand scores to Predictive Routing agent profiles to be used for scoring
 
−
# Through out the day PDNA score are used as input to Predictive routing models for Revenue optimization
 
−
{{!}}-
 
−
{{!}} class="confluenceTd" colspan="1"{{!}}Operational Flow
 
−
{{!}} class="confluenceTd" colspan="1"{{!}}
 
−
# PDNA is configured to push data to Predictive Routing
 
−
# Daily sync of PDNA strands occurs right after the beginning of the agent shift
 
−
#* ''Note that we may need enhancement to push on new data as opposed to by specific time''
 
−
# Model re-training is NOT required
 
−
# Model refresh/re-training cadence continues based on the business criteria of KPI lifecycle, Agent Churn and Customer Churn
 
−
{{!}}-
 
−
{{!}} class="confluenceTd" colspan="1"{{!}}Reporting
 
−
{{!}} class="confluenceTd" colspan="1"{{!}}New reports needs to be defined to view the PDNA sync information shared with GPR
 
−
{{!}}}
 
−
|DistributionImageFlow=
 
−
|DistributionLogic=The details of the distribution of an interaction to an agent are defined in the underlying inbound use cases. Refer to the flow above to understand how Predictive Routing influences the distribution logic.
 
  
−
Predictive Routing provides a routing lever that can be used to control how customer-to-agent matching behaves in customer surplus mode to distribute the interactions based on agent occupancy.
+
<sup>*</sup>A/B reports can be developed from any standard or custom Info Mart data. If the outcomes data is NOT integrated with Info Mart, the creation of A/B reports must be evaluated as a separate effort.
−
|CustomerInterfaceRequirements=N/A
+
|DocVersion=V 1.1.4
−
|AgentDeskRequirements=This use case does not include specific agent desktop requirements.Specific data can be attached during the routing phase and may be visualized by the agent.
+
|GeneralAssumptions=*Requires Product Management approval.
−
|RealTimeReporting=Predictive Routing real-time reports include:
+
*Predictive Routing solution is offered to on-premises customers in a hybrid architecture that incorporates core functionality served from components deployed in your own environment.
−
====Queue KPIs By Predictive Model====
+
*Predictive Routing is offered as a managed service by Genesys Professional Services, who deal with all aspects of machine-learning model creation and maintenance. A Professional Services package is mandatory for implementation and support of Predictive Routing.
−
Monitor your Queue performance with the prediction scores provided by Genesys Predictive Routing to optimize your business KPI.
+
*The standard deployment materials address Inbound voice interactions based on Genesys Info Mart data only.
 +
*Integration of additional data sources, whether Genesys or 3rd-party, requires a dedicated assessment and implementation by Genesys Professional Services.
 +
*Customer must have implemented a use case for one or more channels and have deployed Genesys Info Mart reporting. These use cases populate the data used to build predictors and models, which direct how interactions are routed. Note that the capture and analysis of FCR KPIs is not part of Genesys Info Mart out-of-box statistics and is developed during model creation.
 +
*This use case is for revenue optimization but can be extended to other Sales KPIs.
 +
*Prerequisites: An implemented use case for one or more channels and Genesys Info Mart reporting. These use cases populate the predictors used to direct routing and the data necessary to build the models. This solution cannot use data that is not present.
 +
*The standard deployment materials address Inbound voice interactions only, and Genesys Info Mart data only.
 +
*The capture and analysis of Sales KPIs is not part of Genesys Info Mart out-of-box statistics and is developed during model creation.
 +
*The revenue definition chosen in this use case is illustrative and needs to be adapted for each project.
  
−
[[File:PRM_Queue_Group_by_Model_View_3.png]]
+
Note the exceptions where Predictive Routing cannot be integrated listed in the interdependencies section:
−
====Agent Group KPIs by Predictive Model====
 
−
Monitor the Agent Group Performance with the volume of answered interactions by range of predicted scores provided by Genesys Predictive Routing to optimize your business KPI.
 
  
−
[[File:PRM_Agent_Group_By_Model_View_1_3.png]]
+
*Self-Service use cases
−
|HistoricalReporting=The historical reports available within the Predictive Routing Data Store include:
+
*Outbound preview and agent reservation used for Predictive and Progressive outbound
−
* Predictive Routing Operational Report tracks Predictive Routing operational statistics.
+
|CustomerAssumptions=*Customer has already optimized traditional routing strategies and processes and wants to achieve further improvements.
−
* Predictive Routing A/B Testing Report tracks A/B testing results for Predictive Routing models and predictors.<sup>*</sup>
+
*Customer has all compatible versions of URS, IRD, Genesys Info Mart, GCXI, and Pulse; or upgrades have been scoped in to the project plan.
−
* Predictive Routing Agent Occupancy Report tracks Agent Occupancy while Predictive Routing is being used to optimize routing.
+
*Customer has the necessary systems and processes in place to track results and measure impact over the life of the model.
−
* Predictive Routing Daily Queue Statistics Report tracks KPIs for each Queue while Predictive Routing is being used to optimize routing.
+
*Customer identification is available and stored in Genesys Info Mart.
−
* Predictive Routing Detail Report provides interaction level detail data about Predictive Routing use and its impact on KPIs.
 
−
 
 
−
<sup>*</sup>A/B reports can be developed from any standard or custom Info Mart data. If the outcomes data is NOT integrated with Info Mart, the creation of A/B reports must be evaluated as a separate effort.
 
−
|GeneralAssumptions=The following use cases are exceptions where Predictive Routing cannot be integrated:
 
−
* [https://repository.docs.genesys.com/Draft:UseCases/Current/Genesys Engage/CE38 Genesys Email Auto Response (CE38) for Genesys Engage:] : Routing to a Genesys Digital Auto-response.
 
−
* [https://repository.docs.genesys.com/Draft:UseCases/Current/Genesys Engage/EE04 Genesys Schedule-based Routing (EE04) for Genesys Engage]:  Enable Schedule-based routing.
 
−
* Self-Service Use Cases
 
−
* Outbound preview + agent reservation used for Predictive and progressive outbound
 
−
* Callback, web monitor, proactive assist, co-browse, and Knowledge Management.
 
−
|CustomerAssumptions=* Customer has all compatible versions of URS, IRD, Composer, GIM, GCXI, GII and Pulse or upgrades have been scoped in to the project plan.
 
−
* Customer has necessary systems and processes in place to track results and measure impact over the life of the model
 
−
* Customer identification must be available and stored in Infomart.
 
−
* Significant percentage of interactions require to have a customer id linked. If less than 10 percent interactions have a customer id then building models may not be feasible
 
−
|RequiresAll=
 
 
|RequiresOr=CE01, CE02
 
|RequiresOr=CE01, CE02
 
|Optional=CE16, CE18, CE19, EE14
 
|Optional=CE16, CE18, CE19, EE14
−
|Exceptions=CE38, CE12, EE04
+
|Exceptions=CE12
−
|SMART_PremiseAssumptions={{SMART PremiseAssumptions
+
|PremiseAssumptionsAdditional=<br />
−
|Premise_Assumption=This use case is for revenue optimization but can be extended to other Sales KPIs.
+
|RelatedDocs={{TSSection
−
}}{{SMART PremiseAssumptions
+
|sectionheading=Data Loader
−
|Premise_Assumption=The standard deployment materials address Inbound voice interactions only, and Informart data only.
+
|description=Enables you to upload data, including dataset configuration and upload scheduling.
−
}}{{SMART PremiseAssumptions
+
|relatedarticles=*[https://all.docs.genesys.com/PE-GPR/9.0.0/Deployment/cfgAsc Deploy Data Loader]
−
|Premise_Assumption=Omnichannel and outbound integrations require Genesys Product Management approval and Genesys Professional Services support.
+
*[https://all.docs.genesys.com/PE-GPR/9.0.0/Deployment/DL-CfgFile Configure Data Loader to upload data]
−
}}{{SMART PremiseAssumptions
+
*[https://all.docs.genesys.com/PE-GPR/9.0.0/Deployment/DL-CFEP Configure Data Loader for Feature Engineering]
−
|Premise_Assumption=Integration of additional data sources, Genesys or 3rd-party, requires a dedicated assessment and implementation by Genesys Professional Services.
+
*[https://all.docs.genesys.com/PE-GPR/9.0.0/Deployment/dataReqs Set up data for import]
−
}}{{SMART PremiseAssumptions
+
}}{{TSSection
−
|Premise_Assumption=Digital, Workload Management and Outbound integrations require Genesys PS, Product, and R&amp;D support.
+
|sectionheading=Routing and Reporting integrations
−
}}{{SMART PremiseAssumptions
+
|description=The URS Strategy Subroutines component integrates with your existing Genesys Routing environment. Genesys Reporting produces reports based on KVPs that capture Predictive Routing interaction handling and outcomes.
−
|Premise_Assumption=Customer should have one or more Genesys channels.
+
|relatedarticles=*[https://all.docs.genesys.com/PE-GPR/9.0.0/Deployment/cfgSubroutines Deploy the URS Strategy Subroutines]
−
}}{{SMART PremiseAssumptions
+
*[https://all.docs.genesys.com/PE-GPR/9.0.0/Deployment/GIMintegration Integrate with Genesys Reporting]
−
|Premise_Assumption=Prerequisites: An implemented use case for one or more channels and Info Mart reporting. These use cases populate the predictors in the routing and the data necessary to build the models. This solution cannot use data that is not present.
+
}}{{TSSection
−
}}{{SMART PremiseAssumptions
+
|sectionheading=Model performance
−
|Premise_Assumption=The capture and analysis of Sales KPIs is not part of Info Mart out-of-box statistics and is developed during model creation.
+
|description=The GPR web application is the user interface that provides reports on feature coverage and model accuracy.
−
}}{{SMART PremiseAssumptions
+
|relatedarticles=*[https://all.docs.genesys.com/PE-GPR/9.0.0/Help/Reports Monitor trends and performance]
−
|Premise_Assumption=Routing to agents is based on skill expressions or group-based routing.
 
−
}}{{SMART PremiseAssumptions
 
−
|Premise_Assumption=The revenue definition chosen in this use case is illustrative and needs to be adapted for each project.
 
−
}}
 
−
|PremiseAssumptionsAdditional=* '''<span class="s1">Predictive Routing solution is offered to On-Premise customers from the Genesys Cloud in a Cloud-only supporting Hybrid Architecture.</span>'''
 
−
* '''<span class="s1">Requires PM approval.</span>'''
 
−
 
 
−
Please reference [https://repository.docs.genesys.com/Draft:UseCases/Current/Genesys Engage/BO06 Genesys Predictive Routing for Customer Service (BO06) for Genesys Engage] for a complete list of assumptions.
 
−
|SMART_CloudAssumptions={{SMART CloudAssumptions
 
−
|Cloud_Assumption=This use case is currently not supported in Genesys Engage cloud, Genesys Cloud, or PureConnect Premise or Cloud.
 
 
}}
 
}}
−
|CloudAssumptionsAdditional=
 
−
|SMART_HybridAssumptions=
 
−
|Requires=
 
−
|Conditions=
 
−
|DocVersion=version 1.1.2
 
 
}}
 
}}

Latest revision as of 16:28, November 8, 2022

This topic is part of the manual Genesys Engage On-Premises Use Cases for version Current of Genesys Use Cases.
Important
This use case is based on Genesys Predictive Routing for Customer Service (BO06) for Genesys Engage on-premises. The capabilities described in this use case are under shipping control. Contact your Genesys representative for additional details.
Place revenue generation at the center of your routing decisions by using AI to match each customer opportunity with the best agent

What's the challenge?

Your existing routing strategy doesn’t use machine learning to adapt to the changing patterns of interactions and optimize for sales conversions. You want customers to speak with a rep who can fulfill their need quickly and is predicted best to increase revenue, based on customer journey. Don't let your CX scores suffer!

What's the solution?

Create a differentiated experience by connecting customers with your best-fit sales reps. Genesys Predictive Routing provides the finest grain matching between sales reps and customers and appropriately routes the interaction on the customer’s preferred channel.

Use Case Overview

Story and Business Context

Business leaders want to improve their business Key Performance Indicators (KPI), leverage the innovation in Artificial Intelligence and drive business decisions with the abundance of data and context available in their business. Predictive Routing uses machine learning to support optimization of Sales KPIs.

A Sales KPI is a metric measuring the sales outcome of an interaction, in contrast to Service KPIs, which measure a Customer Experience or efficiency outcome. Sales KPIs can be a sales conversion rate, a sales revenue amount, a retention rate, a collection promise to pay. This use case focuses on improving revenue for inbound voice calls, but can also be extended to other sales-related KPIs. The impacts of choosing another KPI or another channel type are documented in this use case wherever applicable.

Predictive Routing also applies to optimize Services KPIs. See Genesys Predictive Routing for Customer Service (BO06) for Genesys Engage on-premises.

Traditional routing is designed to match customers to agents through skills-based or group-based logic rather than improving KPI. Unlike traditional routing, Predictive Routing uses machine learning to detect patterns in historical data to build a predictive model. This model improves KPIs by ranking agents before making the match with customers. This model also addresses the operational challenges that occur in understaffing and overstaffing scenarios while balancing the service level with improving KPI.

Predictive Routing has built-in A/B Testing to demonstrate the uplift of the KPI provided through use of machine learning. Predictive Routing leverages a variety of Genesys or third party data sources in order to build high quality predictors.

Use Case Benefits*

The following benefits are based on benchmark information captured from Genesys customers and may vary based on industry, lines of business or Genesys product line:

Use Case Benefits Explanation
Improved Customer Experience Routing prospects to the sales reps best able to handle their sales request improves the customer experience.
Improved Employee Satisfaction Increased sales success leads directly to improved satisfaction for sales reps.
Increased Revenue Machine learning-based matching of sales reps to prospects based on sales value directly increases revenue.
Reduced Customer Churn Predictive Routing identifies the best agent for each customer interaction, reducing the likelihood of customer churn to protect revenues.
*You can sort all use cases according to their stated benefits here: Sort by benefits

Summary

Consider a retail bank that wants to upsell credit cards to its existing customers. Depending on the customer attributes (such as income), the bank wants to maximize both the conversion rate and the credit limit that the customer accepts, resulting in a higher overall revenue. This use case is based on a measure of sales revenue driven from a Sales reporting application (such as CRM).

The underlying premise of this use case is that a customer interaction is associated to a credit card offer, either from the explicit customer intention from IVR, web, or mobile or from a business rule such as next best action. Next best action is out of scope of this use case.

The Contact Center Manager or Business owner wants to increase overall revenue generated per agent. The Predictive Routing solution can help with achieving this objective.

Predictive Routing:

  • Uses machine learning, a subset of Artificial Intelligence, to compare feedback of the actual outcome with the predicted outcome, helping to improve future agent-to-customer matches.
  • Ranks agents predicted to maximize the expected revenue per interaction.
  • Provides the finest grain match of customer contact with agent to help maximize revenue per agent. Provides an uplift on revenue using continuous learning to rank the expected revenue for agents servicing customers.

The direct result is that the average revenue per interaction increases. Predictive Routing usually also influences adjacent service KPIs like first contact sale, CSAT or NPS, handle time, and transfers. It is a common best practice to monitor all Sales KPIs and adjacent Service Levels to evaluate all impacts (out of scope of this use case).


Use Case Definition

Business Flow

Predictive Routing for Sales

This business flow shows the use case from the perspective of the customer and agent.

Business Flow Description

  1. The customer contacts the company using the inbound voice channel. This inbound interaction can be the result of a proactive rule on a web or mobile application.
  2. One of the Inbound use cases for the corresponding media type handles the interaction and captures interaction context data. The exact data captured depends on the interaction and engagement type.
  3. Based on the interaction context, Genesys selects an initial group of agents with the required skill(s) as possible routing targets to handle the interaction.
  4. Predictive Routing calculates the scores of the agents in the target group using a machine learning model that takes into account the agents' historic performance on similar interactions.
  5. When there are multiple agents available, Genesys attempts to route the interaction to the available agent with a highest score.
  6. If there is an interaction surplus and an agent becomes ready, Genesys selects an interaction from the queue taking into account the priority of each waiting interaction, the score the agent has for each interaction, and the time the interactions were queued.
  7. If no agents are available within the configured timeout, the routing strategy expands the potential target pool of agents by reducing the skill requirements and then repeats the target agent selection using Predictive Routing.
  8. After dealing with the customer call, the agent disconnects the interaction.
  9. The outcome is mapped to Genesys Info Mart attribute (for example, a disposition code or custom key-value pair).
  10. Optional: The customer is offered a survey. The answer to the survey is stored in a third-party system.
  11. Optional: Outcome data, such as case management closure, is produced and stored by a third-party application.

Business and Distribution Logic

Business Logic

Parameters and Business Rules – Predictive Routing Revenue

Routing Step 1 The system creates an inbound interaction when a customer voice call begins. This use case supports inbound voice involving Genesys routing. See Use Case Interdependencies for details.

  • Precondition: This use case requires one or more use cases handling inbound interactions.

Routing Step 2

  • The inbound interaction use case identifies the primary intention of the customer (Service Type) and the initial target skill expression is set.
  • Any required additional customer or agent profile data available to the interaction in run time can be integrated through a project-based implementation.

Routing Step 3

  • This step queues the interaction and is designed to cover both agent surplus and customer surplus scenarios. When either one or multiple agents are available (agent surplus scenario), the flow immediately proceeds. Otherwise, Genesys queues the interaction until an agent is available (customer surplus scenario). The system starts to balance the service level with the business KPI through maintaining priority.

Routing Step 4

  • Once one or more agents are available, the necessary Customer Profile, Interaction Profile, Agent Profile, and predictor information is passed to Predictive Routing as a scoring request. The request is processed by the relevant machine learning model, resulting in a score for each available agent for that interaction. This process caters to both customer surplus and agent surplus scenarios.

Routing Step 5

  • The rank for each of the interactions against each of the agents is returned to routing to weight the customer-to-agent matching towards the agent(s) that can deliver the highest revenue.
  • In an agent surplus scenario, the score of the highest ranked agent will be compared to the configured minimum score threshold.  If the agent score exceeds that threshold, the system routes the interaction.  If not, then the interaction is held, pending either a higher ranked agent becoming available, or the threshold reducing.
  • In a customer surplus scenario, where multiple interactions are waiting when an agent becomes available, the agent’s scores for each waiting interaction are compared to the minimum score threshold.  If the agent score exceeds the threshold for at least one interaction, the system routes the highest scoring interaction for that agent.  If not, then the agent remains unassigned, pending either a lower scored interaction becoming available, or the threshold reducing.

Routing Step 6

  • The minimum score threshold is reduced over time according to the pre-configured fallback strategy.
  • The checks in Routing Step 5 are repeated regularly until an agent or interaction is identified.
  • Normal target expansion, such as relaxing skill level as configured within the underlying distribution strategy, occurs.
  • The continual re-prioritization of the interaction also occurs as do any treatments and the queued customer experience.

Routing Step 7

  • If at least one of the revenue values is above the threshold, the interaction is routed to the agent with the highest revenue.
  • The system delivers the interaction normally, handling any ring on no answer and exception situations (applicable to voice, chat or email) as defined in the underlying use case.
  • The customer and the agent are connected.

Routing Step 9

  • The outcome of the interaction is captured through the agent desktop or a server-side process. Genesys APIs are invoked automatically or after an agent action to map the outcome to a Genesys interaction attribute: custom attached data or disposition code.
  • Info Mart captures this attribute with the Info Mart interaction record.

Routing Step 10

  • Optionally, the customer receives a survey (the survey results are not connected with Genesys and are intended to evolve with the survey use cases)
  • The survey is completed (optionally) and the outcome is collected and stored by a 3rd-party application.

Routing Step 11

  • Optionally, the outcome data is produced and stored by third-party application.

Distribution Logic

The details of the distribution of an interaction to an agent are defined in the underlying inbound use cases. Refer to the preceding flow to understand how Predictive Routing influences the distribution logic.

Predictive Routing provides a routing lever that can be used to control how customer-to-agent matching behaves in customer surplus mode to distribute the interactions based on agent occupancy.

User Interface & Reporting


Agent UI

This use case does not include specific agent desktop requirements. During the routing phase, data is attached to the interaction that the agent can see.

Reporting

Real-time Reporting

Predictive Routing does not include real-time reports. Operational reports are available in the Predictive Routing UI.

Operational reports include:

  • KPI Outcome
  • Feature Coverage
  • Model Accuracy


Historical Reporting

The historical reports available through GCXI include the following:

  • Predictive Routing Operational Report - tracks Predictive Routing operational statistics.
  • Predictive Routing A/B Testing Report - tracks A/B testing results for Predictive Routing models and predictors.*
  • Predictive Routing Agent Occupancy Report - tracks Agent Occupancy while Predictive Routing is being used to optimize routing.
  • Predictive Routing Daily Queue Statistics Report - tracks KPIs for each Queue while Predictive Routing is being used to optimize routing.
  • Predictive Routing Detail Report - provides interaction level detail data about Predictive Routing use and its impact on KPIs.

*A/B reports can be developed from any standard or custom Info Mart data. If the outcomes data is NOT integrated with Info Mart, the creation of A/B reports must be evaluated as a separate effort.

Customer-facing Considerations

Interdependencies

All required, alternate, and optional use cases are listed here, as well as any exceptions.

All of the following required: At least one of the following required: Optional Exceptions
None

Inbound

Digital

Workforce Engagement

Outbound


General Assumptions

  • Requires Product Management approval.
  • Predictive Routing solution is offered to on-premises customers in a hybrid architecture that incorporates core functionality served from components deployed in your own environment.
  • Predictive Routing is offered as a managed service by Genesys Professional Services, who deal with all aspects of machine-learning model creation and maintenance. A Professional Services package is mandatory for implementation and support of Predictive Routing.
  • The standard deployment materials address Inbound voice interactions based on Genesys Info Mart data only.
  • Integration of additional data sources, whether Genesys or 3rd-party, requires a dedicated assessment and implementation by Genesys Professional Services.
  • Customer must have implemented a use case for one or more channels and have deployed Genesys Info Mart reporting. These use cases populate the data used to build predictors and models, which direct how interactions are routed. Note that the capture and analysis of FCR KPIs is not part of Genesys Info Mart out-of-box statistics and is developed during model creation.
  • This use case is for revenue optimization but can be extended to other Sales KPIs.
  • Prerequisites: An implemented use case for one or more channels and Genesys Info Mart reporting. These use cases populate the predictors used to direct routing and the data necessary to build the models. This solution cannot use data that is not present.
  • The standard deployment materials address Inbound voice interactions only, and Genesys Info Mart data only.
  • The capture and analysis of Sales KPIs is not part of Genesys Info Mart out-of-box statistics and is developed during model creation.
  • The revenue definition chosen in this use case is illustrative and needs to be adapted for each project.

Note the exceptions where Predictive Routing cannot be integrated listed in the interdependencies section:

  • Self-Service use cases
  • Outbound preview and agent reservation used for Predictive and Progressive outbound

Customer Responsibilities

  • Customer has already optimized traditional routing strategies and processes and wants to achieve further improvements.
  • Customer has all compatible versions of URS, IRD, Genesys Info Mart, GCXI, and Pulse; or upgrades have been scoped in to the project plan.
  • Customer has the necessary systems and processes in place to track results and measure impact over the life of the model.
  • Customer identification is available and stored in Genesys Info Mart.


Related Documentation

Data Loader

Enables you to upload data, including dataset configuration and upload scheduling.

Routing and Reporting integrations

The URS Strategy Subroutines component integrates with your existing Genesys Routing environment. Genesys Reporting produces reports based on KVPs that capture Predictive Routing interaction handling and outcomes.

Model performance

The GPR web application is the user interface that provides reports on feature coverage and model accuracy.

Document Version

  • Version V 1.1.4 last updated November 8, 2022

Retrieved from "https://all.docs.genesys.com/UseCases/Current/GenesysEngage-onpremises/SL06 (2025-06-19 13:25:19)"
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