SL06 - Titles and Canonical Info

From Genesys Documentation
Jump to: navigation, search
This information is shared by SL06 use cases across all offerings.

Administration Dashboard

Go back to admin dashboard to create and manage platform-specific use cases in the system:

Titles and Taxonomy

Main Title Subtitle Taxonomy Product Category Draft Published Edit

Genesys Predictive Routing for Sales

Place revenue generation at the center of your routing decisions by using AI to match each customer opportunity with the best agent



No draft

Canonical Information

Platform Challenge and Solution

Platform Challenge: Your existing routing strategy doesn’t use machine learning to adapt to the changing patterns of interactions and optimize for sales conversions. You want customers to speak with a rep who can fulfill their need quickly and is predicted best to increase revenue, based on customer journey. Don't let your CX scores suffer!

Platform Solution: Create a differentiated experience by connecting customers with your best-fit sales reps. Genesys Predictive Routing provides the finest grain matching between sales reps and customers and appropriately routes the interaction on the customer’s preferred channel.

Platform Benefits

The following benefits are based on benchmark information captured from Genesys customers and may vary based on industry or lines of business:

Canonical Benefit Explanation
Improved Customer Experience Predictive routing enables personalization at scale to improve customer experiences, growing customer loyalty and lifetime value
Increased Revenue AI is used to predict the best customer and agent match to improve customer experiences to grow customer loyalty and grow revenue
Reduced Customer Churn Predictive Routing identifies the best agent for each customer interaction, reducing the likelihood of customer churn to protect revenues

High Level Flow

High Level Flow Steps

  1. A customer calls your sales department and elects to speak with an agent
  2. The reason for speaking with an agent is identified​
  3. The customer is queued for a highly skilled agent​
  4. Predictive Matching is used to rank the targeted agents​
  5. Unfortunately all agents are working on other customers inquiries​
  6. Target is expanded to include additional agent skills and proficiency levels​
  7. Predictive Matching is used to rank the next best agents and still meet your service levels​
  8. An agent becomes available and answers the best matched customer​

Data Sheet Image

CE11 - genesys outbound dialer - header (2).png

Canonical Sales Content


  • Head of Business Units
  • Head of Customer Experience
  • Head of Customer Service

Qualifying Questions

  1. Can your existing routing strategy predict the best available sales rep to increase revenue or reduce churn or improve NPS?
  2. Does your existing routing strategy adapt to the changing patterns of interactions using continuous machine learning?
  3. Are you looking to innovate your existing routing strategy with predictive analytics and gain competitive advantage?

Pain Points (Business Context)

  • Low revenue per agent or customer. Variability in business outcomes.
  • Frustrated customers or repeat interactions – cannot get access to an agent providing the proper proficiency on the offer
  • Resource inefficiency and high costs – agents have a low conversion rate and waste customer’s and contact center resources
  • Reduced employee satisfaction – employees don’t achieve their goals.

Desired State - How to Fix It

  • Leverage Predictive analytics and machine learning to identify the optimal agent-customer match to improve Revenue before routing
  • Employ continuous learning by feeding back actual outcomes to update predictive models
  • Managers can gain insights to help identify root causes and employee training opportunities
  • Managers can view reports identifying attributes that influence KPI
  • A/B testing methodologies can be applied to evaluate lift
  • Deep integration with Genesys routing and orchestration

Retrieved from " (2023-06-10 01:33:37)"
Comments or questions about this documentation? Contact us for support!