Difference between revisions of "BO06/Canonical"

Latest revision as of 14:10, March 31, 2021

Administration Dashboard

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

Administration Dashboard

Titles and Taxonomy

Main Title	Subtitle	Taxonomy	Product Category	Draft	Published	Edit
Genesys Predictive Routing for Customer Service	Place CX and agent efficiency at the center of your routing decisions using AI to match each customer interaction with the best agent	Business Optimization	Inbound	No draft	GenesysEngage-onpremises

Canonical Information

Platform Challenge and Solution

Platform Challenge: Today’s contact centers generate large volumes of data and have outgrown legacy skill and queue-based routing for matching customers and agents. It is almost impossible to optimize for metrics such as First Call Resolution (FCR) or Average Handling Time (AHT) because thousands of if-then rules have to be built and managed.

Platform Solution: Genesys Predictive Routing works in real-time, using AI to analyze 100s of data points to discover patterns to match customers to the best agents. With Genesys Predictive Routing, contact centers can improve customer experiences, grow revenue, improve efficiency, and optimize for important KPIs.

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 First Contact Resolution	AI models can optimize for specific KPIs such as FCR by using outcome prediction to match customers to the agent most likely to resolve their query.
Increased Revenue	AI is used to match customers to the best agent to deliver the best customer experience, which increases brand loyalty and reduces churn to grow customer lifetime value.
Reduced Handle Time	Matching customers to the best agent to solve their query optimizes contact center resources by reducing average handle time.

High Level Flow

High Level Flow Steps

A customer decides to interact with your Business
The customer elects to speak with an agent
The reason for speaking with an agent is identified
The customer is queued for a highly skilled agent
Predictive Routing is used to rank the targeted agents
Unfortunately all agents are working on other customers inquiries
Target is expanded to include additional agent skills and proficiency levels
Predictive Routing is used to rank the next best agents and still meet your service levels
An agent becomes available and answers the best matched customer

Data Sheet Image

Canonical Sales Content

Personas

Chief Data Officer
Head of Business Units
Head of Contact Center(s)

Qualifying Questions

1.Can your existing routing strategy predict the best available agent to increase revenue or reduce AHT or improve FCR?

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)

Frustrated customers or repeat interactions – increased number of transfers to identify the right person to resolve the issue
Resource inefficiency and high costs – unable to identify the right agent to quickly solve the issue
Reduced employee satisfaction – employees don’t feel empowered and managers are unable to identify the right training opportunities

Desired State - How to Fix It

Leverage Predictive analytics and machine learning to identify the optimal agent-customer match to improve First Contact Resolution
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

@@ Line 6: / Line 6: @@
 }}
 {{SMART Canonical
+|PlatformChallenge=Today’s contact centers generate large volumes of data and have outgrown legacy skill and queue-based routing for matching customers and agents. It is almost impossible to optimize for metrics such as First Call Resolution (FCR) or Average Handling Time (AHT) because thousands of if-then rules have to be built and managed.
+|PlatformSolution=Genesys Predictive Routing works in real-time, using AI to analyze 100s of data points to discover patterns to match customers to the best agents. With Genesys Predictive Routing, contact centers can improve customer experiences, grow revenue, improve efficiency, and optimize for important KPIs.
+|PainPoints=*Frustrated customers or repeat interactions – increased number of transfers to identify the right person to resolve the issue
+*Resource inefficiency and high costs – unable to identify the right agent to quickly solve the issue
+*Reduced employee satisfaction – employees don’t feel empowered and managers are unable to identify the right training opportunities
+|DesiredState=*Leverage Predictive analytics and machine learning to identify the optimal agent-customer match to improve First Contact Resolution
+*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
+|HighLevelFlowLucid=6bc7a3f3-9bab-4383-8fc8-4872d2740326
 |BuyerPersonas=Chief Data Officer, Head of Business Units, Head of Contact Center(s)
 |QualifyingQuestions=1.Can your existing routing strategy predict the best available agent to increase revenue or reduce AHT or improve FCR?
@@ Line 15: / Line 27: @@
 .Are you looking to innovate your existing routing strategy with predictive analytics and gain competitive advantage?
 |DataSheetImage=BO06.jpg
-|PlatformChallenge=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.
-|PlatformSolution=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.
-|HighLevelFlowLucid=https://www.lucidchart.com/documents/edit/6bc7a3f3-9bab-4383-8fc8-4872d2740326/0
 }}
 {{SMART DataSheetFlow
@@ Line 48: / Line 57: @@
 {{SMART Benefits
 |CanonicalBenefitID=Increased Revenue
-|CanonicalBenefit=By applying machine learning to rich data, route to the best agent predicted to optimize revenue.
+|CanonicalBenefit=AI is used to match customers to the best agent to deliver the best customer experience, which increases brand loyalty and reduces churn to grow customer lifetime value.
 }}
 {{SMART Benefits
 |CanonicalBenefitID=Reduced Handle Time
-|CanonicalBenefit=By applying machine learning to rich data, route to the best agent predicted to reduce AHT.
+|CanonicalBenefit=Matching customers to the best agent to solve their query optimizes contact center resources by reducing average handle time.
 }}
 {{SMART Benefits
 |CanonicalBenefitID=Improved First Contact Resolution
-|CanonicalBenefit=By applying machine learning to rich data, route to the best agent predicted to improve FCR.
+|CanonicalBenefit=AI models can optimize for specific KPIs such as FCR by using outcome prediction to match customers to the agent most likely to resolve their query.
 }}