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

How It Works

  1. Contact arrives at system
  2. Contact metadata extracted (type, queue, customer data)
  3. URS Strategy Subroutines submit interaction details to the Core Platform, which scores agents for their historical ability to handle such an interaction
  4. Machine learning model scores all available agents
  5. Contact routed to highest-scoring agent
  6. Interaction outcome captured and logged
  7. 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

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

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:

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

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

  1. Log into Genesys Cloud Admin
  2. Go to Architect → Routing
  3. Select queue to enable predictive routing
  4. Open queue configuration settings

Step 2: Enable Predictive Routing

  1. Locate "Predictive Routing" toggle
  2. Toggle ON to enable feature
  3. System validates that requirements are met
  4. Configuration page appears

Step 3: Select KPI Optimization

  1. Choose primary KPI from dropdown:
    • Average Handle Time (AHT)
    • Next Contact Avoidance (NCA)
    • Customer Satisfaction (CSAT)
    • Custom outcome metric
  2. If custom KPI: Verify outcome data is being uploaded via API
  3. Save selection

Step 4: Configure Scoring Parameters

  1. Set minimum agent availability threshold
  2. Define required skills for queue
  3. Configure fallback routing behavior
  4. Set scoring timeout (default: 3 seconds)
  5. Enable/disable explainability logging

Step 5: Data & Outcome Mapping

  1. Confirm queue is sending outcome data
  2. For outcome-based custom KPIs, ensure periodic upload of outcome data via Outcome Attributions API
  3. Map external outcome labels to routing KPI
  4. Verify historical data volume meets minimums
  5. Test data flow

Step 6: Validation & Testing

  1. Enable predictive routing on small percentage of traffic
  2. Monitor model accuracy metrics
  3. Verify fallback routing works correctly
  4. Gather baseline performance data
  5. Review feature importance scores in reports

Step 7: Gradual Rollout

  1. Increase traffic percentage gradually
  2. Monitor agent utilization and contact distribution
  3. Track KPI impact daily
  4. Make refinements as needed
  5. 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:

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

Best Practices

Data Quality

Model Optimization

Queue Configuration

Change Management

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

Additional Resources

Official Documentation Links

Support & Training

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

Revision #1
Created 13 March 2026 19:29:41 by Cesar Gzz
Updated 14 March 2026 19:35:03 by Cesar Gzz