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|SMART_Benefits={{SMART Benefits
 
|SMART_Benefits={{SMART Benefits
 
|UCBenefitID=Improved Customer Experience
 
|UCBenefitID=Improved Customer Experience
−
|UCBenefit=Target the best agents to resolve the customer call type by reducing misroutes or repeated transactions to improve customer satisfaction measures (such as Net Promoter Score and Customer Effort Score
+
|UCBenefit=Reduce misroutes or repeated transactions to improve customer satisfaction by targeting the best agents to resolve different types of calls.
 
}}{{SMART Benefits
 
}}{{SMART Benefits
−
|UCBenefitID=Improved Employee Attrition Rate
+
|UCBenefitID=Reduced Employee Attrition
−
|UCBenefit=Improve Employee Experience by routing to the Agent the work that they are more efficient or “good” at more often.
+
|UCBenefit=Improve Employee Experience by routing work to the Agent that they are more efficient or “good” at more often.
 
}}{{SMART Benefits
 
}}{{SMART Benefits
 
|UCBenefitID=Improved First Contact Resolution
 
|UCBenefitID=Improved First Contact Resolution
−
|UCBenefit=Improve Employee Experience by routing to the Agent the work that they are more efficient or “good” at more often.
+
|UCBenefit=Improve First Contact Resolution by routing the interaction to the most proficient Agent available to handle it.
 
}}{{SMART Benefits
 
}}{{SMART Benefits
 
|UCBenefitID=Reduced Handle Time
 
|UCBenefitID=Reduced Handle Time
−
|UCBenefit=Reduce transfers and conferences by routing interactions to the best qualified agent and reduce handle time as more efficient workers often have a lower Average Handle Time
+
|UCBenefit=Reduce transfers and conferences by routing interactions to the best qualified agent and reduce average handle time by having more efficient workers take each interaction.
 
}}
 
}}
−
|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 Service KPIs.
+
|UCIntro=The capabilities described in this use case are under shipping control. Contact your Genesys representative for additional details.
  
−
Service KPIs gather any types of metrics measuring a Customer Experience or Efficiency outcome of an interaction, as opposed to Sales KPIs that measure the Sales Outcome of an interaction. Service KPIs can be of two types:
+
<br />
−
* Customer Experience or Service outcome such as Net Promotor Score (NPS), Customer Satisfaction (CSAT),  First Contact Resolution (FCR), and Customer Effort Score (CES)
+
|UCOverview=Companies want to improve their business Key Performance Indicators (KPI), capitalize on innovation in Artificial Intelligence and drive business decisions with the abundance of data and context available. Predictive Routing uses machine learning to support optimization of Customer Service KPIs.
−
* Operational efficiency metrics such as Handle Time (AHT), transfers, hold count, hold time, case open/closed, back office tasks opened, and field technical sent
 
−
This use case focuses on improving First Contact Resolution (FCR), captured from Genesys Info Mart or from 3rd-party surveys (for inbound voice interactions, for example) but can also be extended to other service-related KPIs, and other inbound, outbound, digital, or workload management channel types. Wherever applicable, this use case documents the impacts of choosing another channel type or another KPI, captured from Genesys data or from 3rd-party sources.
 
  
−
Predictive Routing also applies to Sales & Marketing KPIs. See {{#mintydocs_link:topic=SL06}}.
+
Customer Service KPIs are metrics measuring a customer experience or efficiency outcome of an interaction, as opposed to Sales KPIs that measure the sales outcome of an interaction. Service KPIs can be of two types:
  
−
Traditional routing is designed to match customers to agents through skills-based or group-based logic to maintain a service level rather than improving KPI. Unlike traditional routing, Predictive Routing uses machine learning to find signals in historical data to build a model that predicts the business outcome of an customer's interaction when handled by an employee. 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.
+
*Customer experience outcome such as Net Promotor Score (NPS), Customer Satisfaction (CSAT), First Contact Resolution (FCR), and Customer Effort Score (CES)
 +
*Operational efficiency metrics such as Handle Time (AHT), transfers, hold count, hold time, cases open/closed, and back-office tasks opened
  
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Predictive Routing has built-in A/B Testing to demonstrate the uplift of the KPI provided through blending Artificial Intelligence with humans in what Genesys terms Blended AI.
+
This use case illustrates an improvement in First Contact Resolution (FCR), captured from Genesys Info Mart or from 3rd-party surveys (for inbound voice interactions, for example). The use case also illustrates service-related KPIs, where the data for the KPIs is available in Info Mart or another available data source.
−
|UCSummary=The contact center manager wants to increase the percentage of the interactions achieving First Contact Resolution (FCR). The Predictive Routing solution can assist in achieving this objective. FCR can be obtained either from:
 
−
* Genesys Historical Reporting data, enabling you to define FCR. For example, FCR 7 is defined as the number of customers who didn't make a subsequent contact for the same service type in 7 days.
 
−
* A 3rd-party outcome data source (such as a survey).
 
  
−
Predictive Routing:
+
Predictive Routing also applies to Sales & Marketing KPIs.  See {{Link-AnywhereElse|product=UseCases|version=Current|manual=GenesysEngage-onpremises|topic=SL06|display text=Genesys Predictive Routing for Sales (SL06) for Genesys Engage on-premises}}
−
* 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 resolve the inquiry on first contact.
+
Traditional routing matches customers to agents through skills-based or queue-based logic. The goal is to maintain a service level, rather than to improve a KPI. Predictive Routing differs from traditional routing in that it uses machine learning to detect patterns in historical data from Genesys Info Mart and other third-party data sources. The predictive algorithm then uses these sources to build a model that predicts the business outcome of a customer's interaction when handled by an employee.
−
* Provides the finest-grain match of customer contact with agent to improve FCR.
+
 
−
* Provides an FCR uplift using continuous learning to rank the expected FCR for agents servicing customers.
+
The predictive model works to improve KPIs by ranking agents according to their predicted impact on the business outcome. It then assigns the interaction to the highest ranked available agent. A/B testing measures the real-world impact of Predictive Routing on the target KPI by comparing Predictive Routing performance against the existing routing strategy.
−
* In many customer environments, anchoring the optimization on a customer experience metric like FCR, CSAT, or NPS also drives benefits in adjacent KPIs such as a reduction in handle time, transfers, or multi-agent events (MAE) with manageable impact to service levels.
+
 
−
The result is that repeat contacts are reduced and FCR improves.
+
 
 +
 
 +
<br />
 +
|UCSummary=Organizations seeking to improve the level of customer service offered to their customers realize significant benefits from Predictive Routing. Machine learning models configured to optimize metrics such as First Call Resolution are at the core of the solution.
 +
 
 +
A customer calls the contact center, and Predictive Routing uses the data captured about the customer, their journey, and the current interaction to rank all available agents according to their predicted probability of resolving the call. Configuration options manage and balance the Service Level (speed to answer) with connecting to the most suitable agent. The result is a reduction in repeat contacts and improved FCR.
 +
 
 +
The outcome data feeds back into the machine learning model to inform future predictions. Impacts on KPIs and the performance of the machine learning models are available via real-time reports.
 +
 
 +
<br />
 
|PainPoints=* Frustrated customers or repeat interactions – increased number of transfers to identify the right person to resolve the issue​
 
|PainPoints=* Frustrated customers or repeat interactions – increased number of transfers to identify the right person to resolve the issue​
 
* Resource inefficiency and high costs – unable to identify the right agent to quickly solve the issue​
 
* Resource inefficiency and high costs – unable to identify the right agent to quickly solve the issue​
−
* Reduced employee satisfaction – employees don’t feel empowered and managers are unable to identify the right training opportunities​​​​​​​​
+
* Reduced employee satisfaction – employees don’t feel empowered and managers are unable to identify the right training opportunities​​​​​​​​<br />
−
* Reduced employee satisfaction – employees don’t feel empowered and managers are unable to identify the right training opportunities​
 
−
​​
 
 
|DesiredState=* Leverage Predictive analytics and machine learning to identify the optimal agent-customer match to improve First Contact Resolution​
 
|DesiredState=* Leverage Predictive analytics and machine learning to identify the optimal agent-customer match to improve First Contact Resolution​
 
* Employ continuous learning by feeding back actual outcomes to update predictive models​
 
* Employ continuous learning by feeding back actual outcomes to update predictive models​
Line 47: Line 50:
 
* 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=Head of Customer Experience, Head of Customer Service, Head of Contact Center(s)
 +
|MaturityLevel=Differentiated
 
|SellableItems=At least one Inbound SMART USE Case is required, CIM, CIM HA (Optional), Genesys Infomart, Genesys Infomart - HA (optional)
 
|SellableItems=At least one Inbound SMART USE Case is required, CIM, CIM HA (Optional), Genesys Infomart, Genesys Infomart - HA (optional)
 +
|CloudAssumptions=This use case is currently not available in Genesys Multicloud CX
 
|PremiseAssumptionsAdditional_Sales=* Available on premises (Conditional)
 
|PremiseAssumptionsAdditional_Sales=* Available on premises (Conditional)
 
Pre-requisite:
 
Pre-requisite:
 
* Genesys CIM
 
* Genesys CIM
 
* Info Mart (Used as a component of Predictive Routing, or common)​
 
* Info Mart (Used as a component of Predictive Routing, or common)​
−
* At least one Inbound, Digital or Outbound SMART USE Case (see use-case inter-dependancies section for details)
+
* At least one Inbound or Digital SMART USE Case (see use-case inter-dependancies section for details)
−
|BusinessImageFlow={{SMART_BusinessImageFlow|BusinessFlow=
+
|BusinessImageFlow={{SMART BusinessImageFlow
−
====Model Creation====
+
|BusinessFlow=This business flow shows the use case from the perspective of the customer and agent.
−
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=7deb8693-2294-4b64-8b50-9b3e115b9a89
−
|BusinessImage=https://www.lucidchart.com/documents/edit/a4257aa9-6e73-4dc8-95f7-f592c45d78ec/0
+
|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.
−
|BusinessFlowDescription=
+
#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.
−
# The team Lead / Supervisor and the Analyst agree on the outcome metric to be used. This use case uses FCR as the reference metric.
+
#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.
−
# The Analyst gathers customer profile, interaction profile, agent profile, and FCR data from Info Mart, and optionally from 3rd-party data sources.
+
#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.
−
# The Analyst analyzes the data to determine correlating factors/predictors and verify if the data is suitable for a predictive model.
+
#When there are multiple agents available, Genesys attempts to route the interaction to the available agent with a highest score.
−
# The Analyst creates a predictive model based on the available Info Mart data set.
+
#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.
−
# The Analyst reviews the quality of the predictive model and potential for uplift. If the quality is satisfactory, the model can be provisioned.
+
#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 modeling process described above may be extended to incorporate the following changes:
+
#After dealing with the customer call, the agent disconnects the interaction.
−
* 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 NPS surveys or CRM or case management)
+
#The outcome is mapped to Genesys Info Mart attribute (for example, a disposition code or custom key-value pair).
−
* Selection of other KPI(s) to optimize based on Info Mart data (such as AHT) or 3rd-party data (such as NPS)
+
#Optional: The customer is offered a survey. The answer to the survey is stored in a third-party system.
−
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 details.
+
#Optional: Outcome data, such as case management closure, is produced and stored by a third-party application.
 
}}
 
}}
 +
|BusinessLogic='''Routing Step 1'''
  
−
{{SMART_BusinessImageFlow|BusinessFlow=
+
*Using a supported channel and media type, the customer creates an interaction. As the interaction is handled and traced through your environment, data is captured that enables you to determine the outcome for the metric you want to optimize.
−
====Predictive Routing First Contact Resolution====
+
*Prerequisite: This use case requires inbound call routing.
−
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
+
'''Routing Step 2'''
−
|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.
 
−
# 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.
 
−
# Genesys queues the interaction until at least one agent with the required skill(s) is available.
 
−
# Predictive Routing ranks the agents against the FCR probability for that specific interaction and returns a ranked list with values.
 
−
# Genesys checks if the rank for at least one agent is above the threshold.
 
−
# 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 yes, Genesys distributes to the best available agent based on the predictive model (the agent with the highest rank) and the routing rules.
 
−
# The agent disconnects the interaction.
 
−
# The outcome is mapped to an InfoMart 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: Outcome data, such as case management closure, is produced and stored by a third-party application.
 
  
−
<sup>*</sup><small>The outbound scenario differs from this flow as the agent routing function is triggered by the dialing algorithm to the customer. Nevertheless, In Predictive and Progressive mode, once the customer answers, the agent distribution is based on the same predictive routing principle. As the Preview mode is not distributed through routing, this use case does not apply to Preview mode.</small>}}
+
*The interaction use case identifies the customer's primary intention (Service Type) and sets the initial target skill expression.
−
|BusinessLogic=====Parameters and Business Rules - Create Predictive Model====
 
−
'''Model Step 1'''
 
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* The business representative of the company decides on the KPI to be improved by Predictive Routing. This use case selects First Contact Resolution as determined by repeat callers for the Service Type.
 
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* The company decides which metrics to use, in this case the values for First Contact Resolution X (such as 7 days) and First Contact Resolution Y (such as 35 days), to determine the periods during which the customer has contacted the company more than once.
 
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* At least once a day the FCR metrics are evaluated against the customers that have contacted the company over the last X and Y days for each agent and service type.
 
−
* Other KPI types can be considered through a project-based implementation.
 
  
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'''Model Step 2'''
+
'''Routing Step 3'''
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* The user gathers the FCR, Agent Profile, Interaction profile, and Customer profile data from Genesys Info Mart.
 
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* Predictive Routing then ingests the data into the system as the basis for the predictive model.
 
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* Other Genesys or 3rd-party data sources can be considered through a project-based implementation.
 
  
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'''Model Step 3'''
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*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 user assesses the data for predictability of FCR:
 
−
** The data is analyzed for variance in First Contact Resolution by intention and agent. The results indicate whether a predictive model can generate significant improvement from the data.
 
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** Each data point is analyzed for its correlating factor to FCR.
 
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* The user prepares to create the model.
 
  
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'''Model Step 4'''
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'''Routing Step 4'''
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* The system creates the predictive model to rank the FCR for customer-to-agent matching.
 
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* The model can be made available for further analysis before provisioning into production.
 
  
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'''Model Step 5'''
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*The predictor information along with necessary call information (Customer Profile, Interaction, Agent Profile) passes to Predictive Routing as a scoring request once one or more agents are available. The relevant machine learning model processes the request, resulting in a score for each available agent for that interaction. This process handles both customer surplus and agent surplus scenarios.
−
* The potential uplift of FCR using the predictive model is analyzed for effectiveness and the predictive model is then provisioned for use within customer-to-agent matching.
 
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** The analyst evaluates the model to determine the potential uplift in FCR 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.
 
  
 +
'''Routing Step 5'''
  
−
====Parameters and Business Rules – Predictive Routing FCR====
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*The customer-to-agent matching provides the interaction score for each of the agents to the routing engine to identify the agents that can deliver the highest benefit to the target KPI. In an agent surplus scenario, the model compares the score of the highest ranked agent to the configured minimum score threshold. If the score is below the threshold, then the interaction is held until a higher-ranked agent becomes available or the threshold is reduced.
−
'''Routing Step 1'''
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*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 with the minimum score threshold. If the agent score exceeds the threshold for at least one interaction, the system proceeds to route the highest scoring interaction. If not, then the agent remains unassigned until a lower-scored interaction becomes available or the threshold is reduced.
−
* 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 all inbound and outbound channels involving Genesys routing. See Use Case Interdependencies for details.
 
−
* Prerequisite: This use case requires one or more use cases handling inbound or outbound interactions.
 
  
−
'''Routing Step 2'''
+
'''Routing Step 6'''
−
* The 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.
 
−
* The system integrates 3rd-party data sources for customer profiles during the interactions qualification process. 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'''
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*The minimum score threshold is reduced over time according to the preconfigured fallback strategy.
−
* 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.
+
*The checks in Routing Step 5 repeat regularly until an agent-interaction matchup meets the threshold requirements.
 +
**Normal target expansion, such as relaxing skill level as configured within the underlying distribution strategy, occurs.
 +
**The continual reprioritization of the interaction also occurs, as do any treatments and the standard queued customer experience.
  
−
'''Routing Step 4'''
+
'''Routing Step 7'''
−
* Once one or more agents are available, the necessary Customer Profile, Interaction Profile, Agent Profile, and predictor information is passed to Predictive Routing to rank the customer interactions in the queue against the agent outcome. This process caters to both customer surplus and agent surplus scenarios.
 
  
−
'''Routing Step 5'''
+
*The system delivers the interaction, handling any ring on no answer and exception situations as defined in the underlying use case.
−
* 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 first contact resolution.
+
*The customer and the agent connect.
−
* 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 FCR match applies.
 
  
−
'''Routing Step 6'''
+
'''Routing Step 8'''
−
* If at least one of the FCR values is above the threshold, the interaction is routed to the agent with the highest FCR value.
 
−
* The system delivers the interaction normally, handling any ring on no answer and exception situations (applicable to voice, open media, or digital channels) as defined in the underlying use case.
 
−
* The customer and the agent are connected.
 
  
−
'''Routing Step 7'''
+
*The interaction ends when the customer or agent disconnects the call.
−
* Optionally: If no appropriately ranked agent become available or the available agent does not have a sufficiently high FCR 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.
 
−
* The continual re-prioritization of the interaction also occurs as do any treatments and the queued customer experience.
 
  
 
'''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 agent workspace or a server-side process captures the interaction outcome. Genesys APIs invoke, either automatically or after an agent action, to map the outcome to a Genesys interaction attribute, which can be custom attached data or a disposition code.
 +
*Genesys Info Mart captures this attribute in 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.
 +
*If the customer completes the survey, the system collects and stores the outcome through a 3rd-party application.
  
 
'''Routing Step 11'''
 
'''Routing Step 11'''
−
* Optionally, the outcome data is produced and stored by a third-party application.
 
−
|DistributionLogic=The details of the distribution of an interaction to an agent are defined in the underlying Inbound, Digital, or Outbound 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.
+
*Optionally, a third-party application produces and stores the outcome data.
 +
|DistributionLogic=The inbound use case provides details of the distribution of an interaction to an agent. Refer to the flow above to understand how Predictive Routing influences the distribution logic.
 
|CustomerInterfaceRequirements=N/A
 
|CustomerInterfaceRequirements=N/A
−
|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.
+
|AgentDeskRequirements=Target agents can review Attached Data/Case Data when an interaction routes to their Agent Workspace.
−
|RealTimeReporting=Predictive Matching realtime reports include:
+
|RealTimeReporting=Predictive Routing does not include real-time reports. Operational reports are available in the Predictive Routing UI.
  
−
====Queue KPIs By Predictive Model====
+
Operational reports include:
−
Monitor your Queue performance with the prediction scores provided by Genesys Predictive Matching to optimize your business KPI.
 
  
−
[[File:PRM_Queue_Group_by_Model_View_3.png]]
+
*KPI Outcome
−
====Agent Group KPIs by Predictive Model====
+
*Feature Coverage
−
Monitor the Agent Group Performance with the volume of answered interactions by range of predicted scores provided by Genesys Predictive Matching to optimize your business KPI.
+
*Model Accuracy
 +
|HistoricalReporting=The historical reports available through GCXI are the following:
  
−
[[File:PRM_Agent_Group_By_Model_View_1_3.png]]
+
*Predictive Routing Operational Report - tracks Predictive Routing operational statistics.
−
|HistoricalReporting=The historical reports available within the Predictive Matching Data Store include:
+
*Predictive Routing A/B Testing Report - tracks A/B testing results for Predictive Routing models and predictors.
−
* Predictive Matching Operational Report tracks Predictive Matching operational statistics.
+
*Predictive Routing Agent Occupancy Report - tracks Agent Occupancy while Predictive Routing is being used to optimize routing.
−
* Predictive Matching A/B Testing Report tracks A/B testing results for Predictive Matching models and predictors.
+
*Predictive Routing Daily Queue Statistics Report - tracks KPIs for each Queue while Predictive Routing is being used to optimize routing.
−
* Predictive Matching Agent Occupancy Report tracks Agent Occupancy while Predictive Matching is being used to optimize routing.
+
*Predictive Routing Detail Report - interaction-level detail data about Predictive Routing use and its impact on KPIs.
−
* Predictive Matching Daily Queue Statistics Report tracks KPIs for each Queue while Predictive Matching is being used to optimize routing.
 
−
* Predictive Matching Detail Report provides interaction level detail data about Predictive Matching use and its impact on KPIs.
 
  
 
<sup>*</sup><small>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.</small>
 
<sup>*</sup><small>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.</small>
−
|GeneralAssumptions=The following use cases are exceptions where Predictive Routing cannot be integrated:
+
|DocVersion=v 2.2.1
−
* {{#mintydocs_link:topic=CE17}}: Routing to a Genesys Digital Auto-response.
+
|GeneralAssumptions=*Requires Product Management approval.
−
* {{#mintydocs_link:topic=EE04}}: Enable Schedule-based routing.
+
*Predictive Routing solution is offered to on-premises customers in a hybrid architecture that incorporates core functionality served from the components deployed in your own environment.
−
* Self-Service Use Cases
+
*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.
−
* Outbound preview + agent reservation used for Predictive and progressive outbound
+
*The standard deployment materials address Inbound voice interactions based on Genesys Info Mart data only.
−
* Callback, web monitor, proactive assist, co-browse, and Knowledge Management.
+
*Integration of additional data sources, whether Genesys or 3rd-party, requires a dedicated assessment and implementation by Genesys Professional Services.
−
|RequiresOr=BO01, BO02, BO03, BO04, CE01, CE02, CE11, CE12, CE16, CE18, CE19, CE20, CE24, CE29, CE01-S4B
+
*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.
−
|Exceptions=CE17, EE04
+
 
−
|PremiseAssumptionsAdditional=* FCR 7 is defined as the number of customers who didn't make a subsequent contact for the same service type within 7 days.
+
Note the exceptions where Predictive Routing cannot be integrated listed in the interdependencies section:
−
* The Standard deployment materials address Inbound voice interactions only, Informart data only, including FCR 7.
+
 
−
* Omnichannel and outbound integrations require Genesys Product Management approval and Genesys Professional Services support.
+
*Self-Service use cases
−
* Integration of additional data sources, Genesys or 3rd-party, requires a dedicated assessment and implementation by Genesys Professional Services.
+
*Outbound preview and agent reservation used for Predictive and Progressive outbound
−
* Digital, Workload Management, and Outbound integrations require Genesys PS, Product and R&D support.
+
|CustomerAssumptions=*Customer has already optimized traditional routing strategies and processes and wants to achieve further improvements.
−
* Customer should have one or more Genesys channels.
+
*Customer has all compatible versions of URS, IRD, Genesys Info Mart, GCXI, and Pulse; or upgrades have been scoped in to the project plan.
−
* 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.
+
*Customer has the necessary systems and processes in place to track results and measure impact over the life of the model.
−
* The capture and analysis of FCR KPIs is not part of Info Mart out-of-box statistics and is developed during model creation.
+
*Customer identification is available and stored in Genesys Info Mart.
−
* Routing to agents is based on skill expressions or group-based routing.
+
|RequiresOr=CE01, CE02
−
* There is no industry standard definition for FCR. The definition chosen in this use case is intended to be a guideline metric that can be repeated across our customers who have not defined this metric.
+
|Optional=CE16, CE18, CE19, EE14
−
|SMART_CloudAssumptions={{SMART CloudAssumptions
+
|Exceptions=CE03, CE07, CE08, CE09, CE10, CE11, CE12, CE27, CE28, CE31, CE41
−
|Cloud_Assumption=This use case is currently not available in PureEngage Cloud
+
|PremiseAssumptionsAdditional=<br />
−
}}
+
|Video=312195823
−
|SMART_HybridAssumptions={{SMART HybridAssumptions
+
|RelatedDocs={{TSSection
−
|Hybrid_Assumption=v 2.0.1
+
|sectionheading=Data Loader
 +
|description=Enables you to upload data, including dataset configuration and upload scheduling.
 +
|relatedarticles=*[https://all.docs.genesys.com/PE-GPR/9.0.0/Deployment/cfgAsc Deploy Data Loader]
 +
*[https://all.docs.genesys.com/PE-GPR/9.0.0/Deployment/DL-CfgFile Configure Data Loader to upload data]
 +
*[https://all.docs.genesys.com/PE-GPR/9.0.0/Deployment/DL-CFEP Configure Data Loader for Feature Engineering]
 +
*[https://all.docs.genesys.com/PE-GPR/9.0.0/Deployment/dataReqs Set up data for import]
 +
}}{{TSSection
 +
|sectionheading=Routing and Reporting integrations
 +
|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.
 +
|relatedarticles=*[https://all.docs.genesys.com/PE-GPR/9.0.0/Deployment/cfgSubroutines Deploy the URS Strategy Subroutines]
 +
*[https://all.docs.genesys.com/PE-GPR/9.0.0/Deployment/GIMintegration Integrate with Genesys Reporting]
 +
}}{{TSSection
 +
|sectionheading=Model performance
 +
|description=The GPR web application is the user interface that provides reports on feature coverage and model accuracy.
 +
|relatedarticles=*[https://all.docs.genesys.com/PE-GPR/9.0.0/Help/Reports Monitor trends and performance]
 
}}
 
}}
 
}}
 
}}

Latest revision as of 11:52, November 8, 2022

This topic is part of the manual Genesys Engage On-Premises Use Cases for version Current of Genesys Use Cases.
Important
The capabilities described in this use case are under shipping control. Contact your Genesys representative for additional details.
Place CX and agent efficiency at the center of your routing decisions using AI to match each customer interaction with the best agent

What's the challenge?

Today’s contact centers generate large volumes of data and have outgrown legacy skill and queue-based routing for matching customers and agents. It is almost impossible to optimize for metrics such as First Call Resolution (FCR) or Average Handling Time (AHT) because thousands of if-then rules have to be built and managed.

What's the solution?

Genesys Predictive Routing works in real-time, using AI to analyze 100s of data points to discover patterns to match customers to the best agents. With Genesys Predictive Routing, contact centers can improve customer experiences, grow revenue, improve efficiency, and optimize for important KPIs.

Use Case Overview

Story and Business Context

Companies want to improve their business Key Performance Indicators (KPI), capitalize on innovation in Artificial Intelligence and drive business decisions with the abundance of data and context available. Predictive Routing uses machine learning to support optimization of Customer Service KPIs.

Customer Service KPIs are metrics measuring a customer experience or efficiency outcome of an interaction, as opposed to Sales KPIs that measure the sales outcome of an interaction. Service KPIs can be of two types:

  • Customer experience outcome such as Net Promotor Score (NPS), Customer Satisfaction (CSAT), First Contact Resolution (FCR), and Customer Effort Score (CES)
  • Operational efficiency metrics such as Handle Time (AHT), transfers, hold count, hold time, cases open/closed, and back-office tasks opened

This use case illustrates an improvement in First Contact Resolution (FCR), captured from Genesys Info Mart or from 3rd-party surveys (for inbound voice interactions, for example). The use case also illustrates service-related KPIs, where the data for the KPIs is available in Info Mart or another available data source.

Predictive Routing also applies to Sales & Marketing KPIs. See Genesys Predictive Routing for Sales (SL06) for Genesys Engage on-premises

Traditional routing matches customers to agents through skills-based or queue-based logic. The goal is to maintain a service level, rather than to improve a KPI. Predictive Routing differs from traditional routing in that it uses machine learning to detect patterns in historical data from Genesys Info Mart and other third-party data sources. The predictive algorithm then uses these sources to build a model that predicts the business outcome of a customer's interaction when handled by an employee.

The predictive model works to improve KPIs by ranking agents according to their predicted impact on the business outcome. It then assigns the interaction to the highest ranked available agent. A/B testing measures the real-world impact of Predictive Routing on the target KPI by comparing Predictive Routing performance against the existing routing strategy.



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 Reduce misroutes or repeated transactions to improve customer satisfaction by targeting the best agents to resolve different types of calls.
Improved First Contact Resolution Improve First Contact Resolution by routing the interaction to the most proficient Agent available to handle it.
Reduced Employee Attrition Improve Employee Experience by routing work to the Agent that they are more efficient or “good” at more often.
Reduced Handle Time Reduce transfers and conferences by routing interactions to the best qualified agent and reduce average handle time by having more efficient workers take each interaction.
*You can sort all use cases according to their stated benefits here: Sort by benefits

Summary

Organizations seeking to improve the level of customer service offered to their customers realize significant benefits from Predictive Routing. Machine learning models configured to optimize metrics such as First Call Resolution are at the core of the solution.

A customer calls the contact center, and Predictive Routing uses the data captured about the customer, their journey, and the current interaction to rank all available agents according to their predicted probability of resolving the call. Configuration options manage and balance the Service Level (speed to answer) with connecting to the most suitable agent. The result is a reduction in repeat contacts and improved FCR.

The outcome data feeds back into the machine learning model to inform future predictions. Impacts on KPIs and the performance of the machine learning models are available via real-time reports.



Use Case Definition

Business Flow

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

Routing Step 1

  • Using a supported channel and media type, the customer creates an interaction. As the interaction is handled and traced through your environment, data is captured that enables you to determine the outcome for the metric you want to optimize.
  • Prerequisite: This use case requires inbound call routing.

Routing Step 2

  • The interaction use case identifies the customer's primary intention (Service Type) and sets the initial target skill expression.

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).

Routing Step 4

  • The predictor information along with necessary call information (Customer Profile, Interaction, Agent Profile) passes to Predictive Routing as a scoring request once one or more agents are available. The relevant machine learning model processes the request, resulting in a score for each available agent for that interaction. This process handles both customer surplus and agent surplus scenarios.

Routing Step 5

  • The customer-to-agent matching provides the interaction score for each of the agents to the routing engine to identify the agents that can deliver the highest benefit to the target KPI. In an agent surplus scenario, the model compares the score of the highest ranked agent to the configured minimum score threshold. If the score is below the threshold, then the interaction is held until a higher-ranked agent becomes available or the threshold is reduced.
  • 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 with the minimum score threshold. If the agent score exceeds the threshold for at least one interaction, the system proceeds to route the highest scoring interaction. If not, then the agent remains unassigned until a lower-scored interaction becomes available or the threshold is reduced.

Routing Step 6

  • The minimum score threshold is reduced over time according to the preconfigured fallback strategy.
  • The checks in Routing Step 5 repeat regularly until an agent-interaction matchup meets the threshold requirements.
    • Normal target expansion, such as relaxing skill level as configured within the underlying distribution strategy, occurs.
    • The continual reprioritization of the interaction also occurs, as do any treatments and the standard queued customer experience.

Routing Step 7

  • The system delivers the interaction, handling any ring on no answer and exception situations as defined in the underlying use case.
  • The customer and the agent connect.

Routing Step 8

  • The interaction ends when the customer or agent disconnects the call.

Routing Step 9

  • The agent workspace or a server-side process captures the interaction outcome. Genesys APIs invoke, either automatically or after an agent action, to map the outcome to a Genesys interaction attribute, which can be custom attached data or a disposition code.
  • Genesys Info Mart captures this attribute in the Info Mart interaction record.

Routing Step 10

  • Optionally, the customer receives a survey.
  • If the customer completes the survey, the system collects and stores the outcome through a 3rd-party application.

Routing Step 11

  • Optionally, a third-party application produces and stores the outcome data.

Distribution Logic

The inbound use case provides details of the distribution of an interaction to an agent. Refer to the flow above to understand how Predictive Routing influences the distribution logic.

User Interface & Reporting


Agent UI

Target agents can review Attached Data/Case Data when an interaction routes to their Agent Workspace.

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 are 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 - 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

Digital

Inbound

Outbound

Self-Service and Automation


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 the 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.

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 2.2.1 last updated November 8, 2022

Retrieved from "https://all.docs.genesys.com/UseCases/Current/GenesysEngage-onpremises/BO06 (2025-06-18 20:43:39)"
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