Predictive Routing Genesys PureCloud Predictive Routing Documentation Study Notes Topic Description Predictive Routing Machine learning-powered intelligent routing that matches contacts to optimal agents AI Engine White-box models with explainability features showing feature importance scores Outcome Labels Custom KPI metrics tracked and used to retrain models continuously Queue Configuration Per-queue settings enabling/disabling predictive routing with KPI selection Model Retraining Weekly automated retraining with daily data updates ensuring current accuracy Data Retention No model data retained beyond 90 days for privacy and compliance Navigation Admin → Architect → Routing → Predictive Routing Configuration OR Admin → Contact Center → Routing → Predictive Routing Settings Predictive Routing Overview Genesys Cloud's predictive routing uses machine learning to rank agents for optimal handling of interactions, supporting key performance indicators like average handle time and next contact avoidance. Unlike traditional rule-based routing, predictive routing analyzes hundreds of data points in real-time to identify which agent is most likely to deliver the best customer outcome based on your defined business goals. Key Capabilities Predictive routing uses white-box models that allow gaining insights into how the features contribute to a prediction, with each input feature given a percentage/score that represents its importance Continuously improves scoring accuracy based on outcome data from previous interaction-agent matchups Real-time agent scoring based on historical performance and current state Automatically retrains and updates features used for agent scoring with daily data and retrains the data models weekly Support for custom KPI optimization (AHT, NCA, CSAT, etc.) Automatic fallback when models are unavailable How It Works Contact arrives at system Contact metadata extracted (type, queue, customer data) URS Strategy Subroutines submit interaction details to the Core Platform, which scores agents for their historical ability to handle such an interaction Machine learning model scores all available agents Contact routed to highest-scoring agent Interaction outcome captured and logged Models updated with new outcome data for future predictions Edition & Module Requirements Requirement Details Minimum Edition Premium Edition (Genesys Cloud CX 1-4) Module Workforce Optimization or Predictive Routing add-on License Type Included with qualifying editions AI Tokens May consume tokens depending on configuration Setup Admin access to Architect and routing configuration Study Notes - AI Model Features Feature Category Examples Impact Agent Profile Data Skills, tenure, department, certifications High - Core matching criteria Agent Performance Data Historic AHT, FCR, quality scores, tenure High - Predicts future performance Availability Data Current status, workload, after-call-work High - Real-time readiness Interaction Metadata Contact type, channel, customer segment Medium - Context matching Queue Statistics Historical patterns, time-of-day trends Medium - Volume prediction Customer Data History, value, previous interactions Low-Medium - Personalization AI Model Architecture White-Box Model Explainability Genesys helps you deduce the prediction by presenting a global interpretation that describes the average behavior of a model. A high value means that the feature will have a larger effect on the model's predictions and ranking of agents. While a small value is given to unimportant features whose contribution is mostly ignored for the model's predictions. Model Training & Retraining To keep up with changing levels of agent proficiency and customer interaction contexts, the data models continually retrain and learn from the latest features. Genesys Cloud updates the features used for agent scoring with daily data and retrains the data models weekly. No data is retained in the models for more than 90 days. Privacy & Compliance Genesys does not use PII for the agent scoring process. Genesys Cloud only uses transaction conversation data to train machine learning models. Data Requirements for Optimal Performance Recommended Data Volume Genesys recommends a periodic upload of outcome data to ensure better model training and prediction accuracy. The recommended frequency is once a day, or, at the minimum, once a week. Genesys recommends at least 90 days of data and ideally 180 days of data. If you retain fewer days of data, you can still use and benefit from predictive routing. However, the quality and effectiveness of your AI models and predictions is likely to suffer and the resulting benefit will be reduced compared to standard routing. Data Sources Agent profile data such as skills, tenure, department, certificates, employee type Agent performance data such as historic average handle time for a queue Custom outcome data via Outcome Attributions API External data sources for outcome-based KPIs Minimum Data Thresholds Data, even if available, is considered for the purpose of routing calculation only if the data volume meets a minimum requirement. If the volume of data does not meet the specified threshold value, the machine learning model does not use the available data. While not mandatory for Genesys predictive routing to operate, for better prediction results, Genesys recommends that you ensure the availability of sufficient volume of data against maximum number of fields. Outcome Labels & Custom KPIs Default KPIs Predictive Routing can optimize for standard Genesys metrics: Average Handle Time (AHT) - Minimize time to resolve Next Contact Avoidance (NCA) - Minimize customer repeat contacts Customer Satisfaction (CSAT) - Maximize customer satisfaction scores First Contact Resolution (FCR) - Prevent escalations and transfers Custom Outcome Labels If you use outcome-based custom KPIs, Genesys predictive routing relies on data from external data sources, which it receives through the Outcome Attributions API. Uploading Outcome Data Frequency : Daily recommended, weekly minimum Format : CSV or API integration via Outcome Attributions API Data Requirements : At least 90 days, ideally 180 days for optimal models Processing : Data uploaded to Genesys Cloud for model retraining Model Updates : Weekly retraining cycle incorporates latest outcomes Setting KPI Optimization per Queue During queue configuration, administrators select which KPI the predictive routing model should optimize for that specific queue. The system will then score agents based on their historical performance against that metric. Queue Configuration Steps Step 1: Navigate to Routing Settings Log into Genesys Cloud Admin Go to Architect → Routing Select queue to enable predictive routing Open queue configuration settings Step 2: Enable Predictive Routing Locate "Predictive Routing" toggle Toggle ON to enable feature System validates that requirements are met Configuration page appears Step 3: Select KPI Optimization Choose primary KPI from dropdown: Average Handle Time (AHT) Next Contact Avoidance (NCA) Customer Satisfaction (CSAT) Custom outcome metric If custom KPI: Verify outcome data is being uploaded via API Save selection Step 4: Configure Scoring Parameters Set minimum agent availability threshold Define required skills for queue Configure fallback routing behavior Set scoring timeout (default: 3 seconds) Enable/disable explainability logging Step 5: Data & Outcome Mapping Confirm queue is sending outcome data For outcome-based custom KPIs, ensure periodic upload of outcome data via Outcome Attributions API Map external outcome labels to routing KPI Verify historical data volume meets minimums Test data flow Step 6: Validation & Testing Enable predictive routing on small percentage of traffic Monitor model accuracy metrics Verify fallback routing works correctly Gather baseline performance data Review feature importance scores in reports Step 7: Gradual Rollout Increase traffic percentage gradually Monitor agent utilization and contact distribution Track KPI impact daily Make refinements as needed Scale to 100% when confident in performance Model Scoring Process Agent Scoring Flow Incoming Contact ↓ Extract Contact Metadata ├── Contact type (voice, chat, email) ├── Queue assignment ├── Customer segment └── Interaction intent ↓ Query AI Model ├── Input: Contact metadata ├── Features: Agent data, performance, availability ├── Process: ML scoring algorithm └── Output: Agent scores (0-100) ↓ Rank Agents ├── Agent A: 87 score ├── Agent B: 72 score ├── Agent C: 91 score (highest) └── Agent D: 65 score ↓ Route to Highest Scorer ├── Select: Agent C (91 score) ├── Fallback: If unavailable → Agent A (87) └── Escalation: If all unavailable → Queue ↓ Log Interaction ├── Selected agent score ├── Feature importance values ├── Outcome when complete └── Update training data Feature Importance Explanation The model provides transparency showing which factors most influenced each routing decision: High Importance Features (weight >10%) Directly impact agent selection Should be monitored and optimized Medium Importance Features (weight 5-10%) Contribute meaningfully to decisions Worth tracking for insights Low Importance Features (weight <5%) Minimal impact on routing May indicate data quality issues Real-World Implementation Scenario Banking Contact Center Queue: Mortgage Support KPI Optimization: Average Handle Time (AHT) Agent Profiles: ├─ Agent Sarah │ ├─ Skills: Mortgage, Refinance, Loan Modification │ ├─ Avg AHT: 6.2 minutes │ ├─ Tenure: 5 years │ └─ Current: Available (0 contacts) │ ├─ Agent James │ ├─ Skills: Mortgage, Basic Support │ ├─ Avg AHT: 8.1 minutes │ ├─ Tenure: 1 year │ └─ Current: Available (1 contact) │ └─ Agent Maria ├─ Skills: Mortgage, Refinance, Advanced ├─ Avg AHT: 5.9 minutes ├─ Tenure: 7 years └─ Current: Available (2 contacts) Incoming Contact: "I'd like information about refinancing my mortgage" Model Analysis: ├─ Contact Intent: Refinance inquiry ├─ Expected AHT: ~7 minutes ├─ Required Skills: Refinance, Mortgage └─ Channel: Voice Agent Scoring (for AHT optimization): ├─ Sarah: 89 score │ └─ Reasoning: Strong refinance skills, excellent AHT, │ lower current load ├─ James: 71 score │ └─ Reasoning: Has required skills but higher baseline AHT └─ Maria: 85 score └─ Reasoning: Best AHT historically but already has higher load (2 contacts) Routing Decision: Route to Agent Sarah (highest score: 89) Expected Outcome: ├─ Faster resolution (Sarah's AHT advantage) ├─ Better customer experience ├─ Optimizes against AHT KPI └─ Load distributed effectively Model Metrics & Dashboard Key Performance Indicators Tracked Metric Purpose Good Range Model Accuracy How often predictions are correct >75% Feature Coverage % of required data available >85% Agent Utilization Even work distribution 70-90% KPI Improvement Impact on selected metric +5-20% Fallback Rate When model can't score <5% Monitoring Model Health Daily : Check model score distribution Weekly : Review prediction accuracy vs. actual outcomes Monthly : Analyze feature importance changes Quarterly : Assess KPI impact and ROI Best Practices Data Quality Validate Outcome Data - Ensure outcome labels are accurate and complete Consistent Data Format - Maintain standardized data formats in API uploads Timely Uploads - Daily uploads ensure models have latest information Complete Records - Capture outcomes for all interactions, not just subset Model Optimization Start with One KPI - Master one optimization goal before multiple Monitor Feature Changes - Track how feature importance shifts over time A/B Test Approaches - Compare predictive routing vs. traditional on test queues Iterative Improvement - Refine based on real-world performance data Queue Configuration Gradual Rollout - Start with 10% traffic, increase to 100% gradually Skill Validation - Ensure agent skill assignments are accurate and current Outcome Mapping - Correctly map external KPIs to routing selection Fallback Testing - Verify routing works when models unavailable Change Management Communicate Changes - Inform agents about predictive routing changes Monitor Agent Sentiment - Track agent acceptance and satisfaction Provide Training - Explain how predictive routing affects their work Set Expectations - Clear goals and targets for improvement Common Implementation Issues & Solutions Issue Cause Solution Model not scoring agents Insufficient data volume Ensure 90+ days of outcome data available Uneven agent utilization Agents with similar skills Review and update skill assignments No KPI improvement Wrong KPI selected for queue Validate KPI choice aligns with business goals High fallback rate Skill mismatches in data Audit and correct agent skill inventory Slow model updates Outcome data not uploading Verify API integration and data flow Feature importance unclear New queue without history Wait for sufficient data accumulation Model accuracy low Poor quality training data Validate and clean outcome data Interview Cheat Sheet Question Answer What is Predictive Routing? ML-powered routing that matches contacts to optimal agents based on historical data What models does it use? White-box models with explainability showing feature importance percentages How often do models retrain? Weekly with daily data updates; no data retained beyond 90 days What data is needed? 90+ days recommended (ideally 180); daily or weekly outcome uploads What KPIs can it optimize? AHT, NCA, CSAT, or custom metrics via Outcome Attributions API Where do you configure it? Admin → Architect → Routing → Queue settings Does it use PII? No, only transaction conversation data for training What's the expected improvement? 5-20% improvement in selected KPI How do you upload outcomes? Via Outcome Attributions API or CSV upload to Genesys Cloud What if model unavailable? Falls back to traditional routing automatically How do agents get selected? Highest-scoring agent routed first; fallback to next highest if unavailable Can you explain routing decisions? Yes, white-box models provide feature importance scores for transparency Key Takeaways Intelligent Matching - By continuously learning from real-time and historical data, it helps optimize important KPIs like average handle time, first-contact resolution (through next contact avoidance (NCA)) and more White-Box Transparency - Models explain exactly which factors influenced each routing decision Continuous Learning - Weekly retraining with daily updates ensures models stay current Privacy-First - No PII used; data retained only 90 days Custom Optimization - Select any KPI (standard or custom) to optimize routing around Data-Driven - Requires quality outcome data for accurate predictions Graceful Fallback - Traditional routing if model unavailable Per-Queue Configuration - Each queue can optimize for different KPIs Explainability - Feature importance scores show transparency in AI decisions Proven ROI - 5-20% improvement typical on selected KPI Additional Resources Official Documentation Links Predictive Routing Overview: help.genesys.cloud/articles/predictive-routing-overview/ AI Model Scoring: help.genesys.cloud/articles/how-the-ai-model-scores-agents-for-predictive-routing/ Data Requirements: help.genesys.cloud/articles/sources-of-data-for-predictive-routing-decisions/ Use of AI in Routing: help.genesys.cloud/articles/use-of-ai-in-predictive-routing/ Support & Training Genesys University: genesys.com/training Community Forums: https://community.genesys.com Technical Support: https://support.genesys.com Document Version Info Last Updated: March 2026 Source: Genesys PureCloud Official Documentation (help.genesys.cloud) Validated: Current with January-March 2026 releases Version: 1.0