Genesys Predictive Routing for Customer Service (BO06) for Genesys Engage on premises

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This topic is part of the manual Genesys Engage On-premises Use Cases for version Current of Genesys Use Cases.
This use-case is in "Sales-Ready" status, meaning that all capabilities described in the use-case are supported by the Genesys products, but the associated services require a custom quotation. The Predictive Routing product is in Conditional Commercialization status, meaning that some capabilities are still restricted or may require PM approval.
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?

Your existing routing strategy doesn’t use machine learning to adapt to the changing patterns of interactions and optimize for business KPIs. You want customers to speak with someone who can fulfill their need quickly and is predicted as the best agent to optimize KPIs. Don’t let your CX score, productivity and outcomes suffer.

What's the solution?

Create a differentiated experience by connecting customers with your best-fit agents. Genesys Predictive Routing provides the finest grain matching between agent and customer to improve business KPIs and appropriately route the interaction on any channel.

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

The design of traditional routing is to match customers to agents through skills-based or group-based logic.  The goal is to maintain a service level, rather than improving a KPI. Predictive Routing differs from traditional routing in that it uses machine learning to find signals 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 way the predictive model works to improve KPIs is 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 is the method that measures the actual impact of Predictive Routing on the target KPI, which compares the performance of Predictive Routing against the existing routing strategy.

Use Case Benefits*

Use Case Benefits Explanation
Improved Employee Attrition Improve Employee Experience by routing to the Agent the work that they are more efficient or “good” at more often.
Improved First Contact Resolution Improve First Contact Resolution by routing the interaction to the most proficient Agent available to handle it.
Improved Net Promoter Score Reduce misroutes or repeated transactions to improve customer satisfaction by targeting the best agents to resolve different types of calls.
Reduced Handle Time 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
*You can sort all use cases according to their stated benefits here: Sort by benefits


Organizations seeking to improve the level of customer service offered to their customers realize significant benefit from Predictive Routing. Machine learning models configured to optimize 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 may 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. The context data is captured depending on the interaction and engagement type.
  3. Genesys queues the interaction until at least one agent with the required skill(s) is available.
  4. Predictive Routing ranks the agents according to the probability that they will resolve that specific interaction first time, and returns a ranked list with values.
  5. Genesys checks if the score for at least one agent is above the configured score threshold.
  6. If no agents are available within the configured timeout, the routing expands the potential target pool of agents, by reducing the score threshold.
  7. If agents over the score threshold are found, Genesys distributes to the best available agent based on the predictive model (the agent with the highest rank) and the routing rules.
  8. After dealing with the customer call, the agent disconnects the interaction.
  9. The outcome is mapped to an InfoMart 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 that captures the defined metric outcome.
  • Prerequisite: This use case requires inbound call routing.

Routing Step 2

  • The interaction use case identifies the primary intention of the customer (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 caters to 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 promote the agents that can deliver the highest benefit to the target KPI. The model compares the score of the highest ranked agent to the configured minimum score threshold, in an agent surplus scenario. 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 with the minimum score threshold. If the agent score exceeds the threshold for at least one interaction, the system proceeds to routing the highest scoring interaction. 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 reduced over time according to the preconfigured fallback strategy.
  • The checks in Routing Step 5 repeat regularly until the identification of an agent or interaction occurs.
    • 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 queued customer experience.

Routing Step 7

  • The system delivers the interaction usually, 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 call disconnects the customer or agent.

Routing Step 9

  • The agent desktop or a server-side process captures the outcome of the interaction. Genesys APIs invoke 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.
  • If the customer completes (optionally) the survey, it 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

Customer Interface Requirements

There are no specific customer interface requirements.

Agent Desktop Requirements

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


Real-time Reporting

Predictive Routing real-time reports include:

(Forthcoming) - Replace with KPI Outcome, Feature Coverage and Model Accuracy?

Historical Reporting

The historical reports available within the Predictive Matching Data Store include: (ARE THESE IN GCXI?)

  • 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.


General Assumptions

The following use cases are exceptions where Predictive Routing cannot be integrated:

  • [[UseCases/Current/GenesysEngage-onpremises/CE38|]] Routing to a Genesys Digital Auto-response.
  • Genesys Schedule-based Routing (EE04) for Genesys Engage on premises: Enable Schedule-based routing.
  • Self-Service Use Cases
  • Outbound preview and agent reservation used for Predictive and Progressive outbound
  • Callback, web monitor, proactive assist, co-browse, and Knowledge Management.

Customer Assumptions

  • 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


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


        Workforce Engagement



                Workforce Engagement

                  On-premises Assumptions

                  • Predictive Routing solution is offered to On-Premise customers from the Genesys Cloud in a Cloud-only supporting Hybrid Architecture.
                  • Requires PM approval.
                  • Predictive Routing is offered as a managed service by Genesys Professional Services, who deals 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 only based on Info Mart data only.
                  • Omnichannel and outbound integrations require Genesys Product Management approval and Genesys Professional Services support.
                  • Integration of additional data sources, Genesys or 3rd-party, requires a dedicated assessment and implementation by Genesys Professional Services.
                  • Customer should have one or more Genesys channels.
                  • 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.
                  • The capture and analysis of FCR KPIs is not part of Info Mart out-of-box statistics and is developed during model creation.
                  • Routing to agents is based on skill expressions or group-based routing.

                  Cloud Assumptions

                  Related Documentation

                  Document Version

                  • v 2.2.0