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New in Release 9.0.009.00 (03/28/2018)
- Journey Optimization Platform (JOP) was renamed to AI Core Services (AICS).
- When generating the Lift Estimation report, Predictive Routing now provides the option to produce a report for each unique value for a selected column (feature). Previously, any feature with a cardinality of more than 20 was excluded, which meant that you could not produce reports with a granularity higher than 20 unique features.
- The agent pool for lift estimation is now constructed on a per-day basis for the interactions in the dataset. Previously, you might have observed a negative lift for higher agent availability or an unexpectedly high lift for low agent availability due to overcorrection caused by a mismatch between the input sample size and the actual sizes encountered through daily simulation.
- Predictive Routing now supports LDAP authentication when logging in.
New in Release 9.0.008.00 (03/05/2018)
- You can now enable Predictive Routing to look up updated values for certain agent attributes, based on customer or interaction attributes during a scoring request. For instance, you can look up agent performance by virtual queue, enabling you to evaluate the agent’s previous performance when handling interactions from that queue. This avoids comparing agent performance for a specific queue against other agents who handle interactions from a different mixture of virtual queues.
- You can now view an entire Agent Profile or Customer Profile record from the Agents Details and Customers Details tabs or an entire record on the Datasets Details tab. Click a single record to open a new window containing a table with all the related key-value pairs.
- Agent State Connector now supports connection to a secured Configuration Server port and TLS 1.2 connections to Stat Server.
- You can now configure Agent State Connector to automatically create an Agent Profile schema, if none exists, or to verify the existing schema.
- You can now have Agent State Connector collect call connId data from Stat Server and write it to the Agent Profile schema for use in Predictive Routing.
- Agent State Connector is now supported on Windows. For exact versions supported, see the Genesys Supported Operating Environment Reference Guide.
- Predictive Routing now supports datasets of up to 250 columns for predictor data generation, model training, and analysis.
- Model training speed has been considerably improved.
- Predictive Routing now provides progress indicators when loading predictor data and generating predictors. The progress indicators show the percent complete and the number of data rows already loaded.
- The maximum supported cardinality for the Group By functionality in the Lift Estimation report has been increased to 20. All features with cardinalities between 1 and 20 are now available in the Group By selection menu.
- You can now enter a maximum value of 500 simulations in the Lift Estimation analysis report settings. This prevents you from entering numbers too large to efficiently analyze and which can lead to an out-of-memory situation. The Number of Simulations field accepts any value larger than 0 and less than or equal to 500.
- Predictive Routing now provides a text search field for use when selecting attributes for analysis.
New in Release 9.0.007.01 (01/05/2018)
- The user interface and the documentation have been updated to reflect the product name change from Genesys Predictive Matching to Genesys Predictive Routing.
New in Release 9.0.007.00 (12/22/2017)
- The product name has changed from Genesys Predictive Matching to Genesys Predictive Routing. This change is not yet reflected in the application interface or in the documentation.
- Genesys Predictive Routing now supports both single-site and multi-site HA architectures.
- Genesys Predictive Routing now supports historical reporting, provided by the Genesys Reporting solution. The following reports are available in Genesys Interactive Insights/GCXI: Predictive Routing AB Testing Report, Predictive Routing Agent Occupancy Report, Predictive Routing Detail Report, Predictive Routing Operational Report, and Predictive Routing Queue Statistics Report.
- Two new real-time reporting templates are available for use in Pulse dashboards: Agent Group KPIs by Predictive Model and Queue KPIs by Predictive Model.
- Two new analysis reports have been added to the Genesys Predictive Routing application: Agent Variance and Lift Estimation.
- The Model creation interface now includes additional model quality and agent coverage reporting.
- The Feature Analysis report, the model creation and training functionality, and the dataset import functionality have been improved to handle large datasets.
- You can now combine simple predictors to create composite predictors.
- Health checks and monitoring have been improved for both Journey Optimization Platform (JOP) and Agent State Connector (ASC). ASC now enables you to set alarms if there are persistent connection issues with Configuration Server or Stat Server.
New in Release 9.0.006.00 (09/26/2017)
- You can now deploy the Journey Optimization Platform (JOP) in Docker containers.
- This release includes updates to the Predictive Matching web interface for improved usability and rebranding.
- This release includes context-sensitive Help.
- You can now update and retrain models that have not yet been activated. You can also make changes to activated models by cloning them, editing the parameters, then activating the new model in place of the old one.
- Agent State Connector and the Strategy Subroutines components can be deployed in a high availability configuration.
- Predictive Matching now supports HTTPS.
- Predictive Matching now supports TLS 1.2 encryption. Support for TLS 1.1 has been discontinued.
- Routing using Predictive Matching can now take agent occupancy into account when selecting the best target.
- The workflow for creating Predictors has been made more logical and straightforward.
- Users can now reset their passwords from the Predictive Matching web interface.
New in Release 9.0.005.00 (06/27/2017)
- Predictive Matching now provides REST APIs for scoring, agent profile updates, and customer profile updates.
- Predictive Matching now supports strategies created in Composer and processed by Orchestration Server (ORS). These strategies utilize common Universal Routing Server (URS) subroutines to store scores returned from the scoring server and to set callback functions in URS.
- Schema modification has been extended to enable manual creation of fields not included in an imported dataset. This extended functionality also enables discovery of additional fields by uploading further data and thereby extending the schema.
New in Release 9.0.004.04 (03/28/2017)
- Extended datasets functionality now includes built-in analysis capabilities to make data exploration and feature analysis more straightforward without requiring customers to first build a predictive model.
- Customer profile data can now be loaded to the platform by means of a REST-API and joined at run time for scoring. This simplifies the integration requirements for deploying Predictive Matching, requiring less modification to existing routing strategies or run time CRM integrations.
- Predictive Matching now enables logging of routing decisions, required for accurate A/B testing, to JOP rather than Genesys Info Mart. This simplifies Predictive Matching deployment, by removing the need to make changes to Interaction Concentrator and Genesys Info Mart to support Predictive Matching.
- The new Predictive Matching Help now opens when you click the Help link in the Predictive Matching interface.
New in Release 9.0.003.00
- Improvements to the analytics and reporting functionality:
- Reports can now indicate whether a predictive score was generated (that is, A/B testing), and whether it was interleaved, or time-divided.
- The range of visualization on the Reporting Dashboard page has been improved.
- Predictive Routing can now perform analysis and data discovery on factors driving the KPI that is being optimized.
- You can now upload data sets in CVS format, enabling you to have Predictive Routing analyze the data, define predictors based on it, and report on it. You can use these data sets for model training and testing, and you can calculate statistics for correlation and cardinality from them.
- Self-service predictor management and model creation. Note the following properties of predictors and models:
- You can have multiple models built from one related data set.
- You can only use a predictor to optimize a single metric (that is, a column in the data set); each model under the same predictor optimizes the same metric.
- You can use a subset of features from the data set to define a predictor.
- A predictor can be based on a subset of data (such as a time range, or a subset created by filtering data set column values).
- Once you define a predictor, you can append new data to its underlying data set.
- A predictor can use any source of data matching the source data set schema to retrain and update models.
New in Release 9.0.000.00
- Routing subroutine support to score agents in agent-surplus mode, where there are more agents in ready status than interactions requiring agent handling. Agents can be scored on various criteria you configure so that interactions go to the most suitable agent.
- Routing subroutine support to score agents in customer-surplus mode, where more customer interactions are waiting in queue than there are available agents. Interactions are scored based on criteria you define so that customers you consider highest priority are handled first.
- Logging, monitoring, and alarm capabilities.
- Ability to combine various criteria for scoring agents or interactions for suitability, leading to a more nuanced matching of agents and interactions.
- Fault Tolerance. If Predictive Routing is unavailable, or if a Predictive Routing strategy subroutine takes more than a specified amount of time to process an interaction, the routing strategy defaults back to standard behavior.
Genesys Predictive Routing
Genesys Predictive Routing draws on accumulated agent and interaction data, enabling you to analyze omnichannel interactions and outcomes and generate models to predict outcomes. From this analysis, combined with machine learning, you can determine the best possible match between waiting interactions and available agents, and then route the interactions accordingly.
In addition, you can report on the predicted versus actual outcomes. The actual outcome is also used to further train the machine-learning model, improving the accuracy of predicted outcomes between similar customer profiles and agent profiles.
In addition, Predictive Routing includes an open REST-API for scoring and for providing feedback, enabling continuous or periodic automated improvement of models.
Genesys Predictive Routing is part of 9.0, which can include component releases from both 9.0.x and 8.5.x code streams. Use the table below to check which component releases are part of 9.0.
|All 9.0 products||9.0 Genesys Predictive Routing Release Notes|
|Predictive Routing - Agent State Connector|
|Predictive Routing - Composer Strategy Subroutines|
|Predictive Routing - URS Strategy Subroutines|
|AI Core Services|
Explains how to deploy Genesys Predictive Routing, enable its features, start, stop, and monitor its condition, and understand its functionality.
Explains how to use the Genesys Predictive Routing user interface.
The API Reference for Genesys Predictive Routing.
Records new features and functionality in all 9.0.0 releases, with links to the relevant documentation.