Analytics Settings

Topic	Detail
Navigation	Admin → Contact Center → Analytics Settings
Purpose	Configure abandon intervals and analytics capture settings for queue reporting
Abandon Intervals	7 configurable intervals (A–G) categorizing when customers disconnect from queue

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Overview

Analytics in Genesys Cloud transforms raw interaction data into actionable insights. Configuration here directly affects how abandonment is measured and reported across all queues.

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Abandon Intervals

Abandon intervals measure how long customers waited in queue before disconnecting without reaching an agent. This metric helps identify queue tolerance, IVR issues, and staffing problems by grouping abandons into time ranges.

Interval	Default Wait Range	Interpretation
A	0–6 seconds	Immediate disconnects — misrouting, robocalls, misdials, IVR confusion
B	6–20 seconds	Early abandons after entering queue
C	20–40 seconds	Short wait abandonment
D	40–60 seconds	Moderate wait abandonment
E	60–120 seconds	Customers leaving after ~1–2 minutes
F	120–240 seconds	Long queue wait frustration
G	>240 seconds	Very long wait abandonment

⚠️ A large percentage in Interval A typically indicates misrouting, IVR confusion, or non-intentional calls — not a staffing problem.

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Analytics Implementation Steps

Step	Action
Step 1	Set Service Level targets per queue — Admin → Contact Center → Queues
Step 2	Configure Abandon Intervals — Admin → Contact Center → Analytics Settings
Step 3	Ensure all queues have Wrap-Up Codes assigned so agents can tag interactions
Step 4	Create Dashboards at Performance → Dashboards with relevant KPI widgets

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Real-Time Analytics

Feature	Location
Performance Views	Performance → Workspace — pre-built views for Queues, Agents, and Interactions
Dashboards	Customizable screens with widgets for KPIs (Service Level, Agents On-Queue, Active Interactions, etc.)
Alerting Rules	Trigger email or browser notifications when metrics hit thresholds (e.g., Wait Time > 5 minutes)

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Historical Analytics

Feature	Description
Standard Reports	Pre-packaged PDF or CSV reports (e.g., Queue Abandonment Detail, Agent Log-level Report)
Dynamic Views	Filter by date range, media type, wrap-up codes
Exporting	Manual export or scheduled delivery to S3 bucket or email address

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Core Analytics Metrics

Interaction Volume

Metric	Description
Offered	Total interactions entering the queue
Answered	Interactions handled by agents
Flow-Outs	Interactions exiting queue through routing or IVR actions
Connected	Interactions successfully connected to agents

Queue Performance

Metric	Description
Service Level	Percentage of interactions answered within SLA target
ASA	Average Speed of Answer — average time before agent answers
Average Wait Time	Average time customers wait in queue
Longest Wait	Longest interaction currently waiting

Customer Behavior

Metric	Description
Abandoned	Interactions disconnected before reaching an agent
Abandon %	Abandoned ÷ Offered
Average Abandon Time	Average wait time before customer hangs up
Short Abandon	Disconnects within a configured short-time threshold

Agent Handling

Metric	Description
AHT	Average Handle Time = Talk Time + Hold Time + ACW
Talk Time	Active speaking time with customer
Hold Time	Time interaction placed on hold
ACW	After Call Work time
Transfers	Interactions transferred between agents or queues

IVR / Flow Metrics

Metric	Description
Flow Outcomes	Where customers exit an Architect flow (Success vs. Failure)
Containment Rate	Percentage of interactions resolved within IVR without reaching an agent
IVR Disconnects	Customers disconnecting during IVR navigation

Advanced Metrics

Metric	Description
Agent Utilization	Percentage of agent time spent handling interactions
Concurrency	Simultaneous digital interactions handled
Callback Rate	Percentage of callers choosing callback instead of waiting
Recontact Rate	Customers contacting support again after a recent interaction

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High Abandonment Troubleshooting

When investigating high abandonment, analyze these five together:

1. ASA — Is average wait time excessive?
2. Abandon Intervals — Which interval has the highest %? (Interval A = routing/IVR issue; Interval F/G = staffing issue)
3. Service Level — Is the SLA target being met?
4. Queue Staffing — How many agents are On-Queue vs. interactions waiting?
5. Flow Outcomes — Are callers exiting the IVR before reaching the queue?

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Knowledge Analytics

Knowledge Analytics measures how effectively knowledge base articles help resolve customer issues — for both agents and bots.

Search & Discovery

Metric	Description
Knowledge Searches	Total searches performed in the knowledge base
Search Success Rate	Percentage of searches that returned useful articles
Search Failure Rate	Searches that produced no relevant results
Popular Search Terms	Most frequently searched keywords

Article Usage

Metric	Description
Article Views	Number of times a knowledge article was opened
Articles Shared	Articles sent to customers during interactions
Top Articles	Most frequently accessed articles
Article Feedback	Ratings or feedback from agents or customers

Self-Service & Automation

Metric	Description
Knowledge Match	Bot successfully finds a relevant knowledge article
Confidence Score	AI confidence in the article match
Knowledge Fallback	Bot cannot find a suitable article
Containment Rate	Issues resolved through self-service without an agent

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Interview Cheat Sheet

Question	Answer
What do Abandon Intervals measure?	How long customers waited before disconnecting without reaching an agent
What does high % in Interval A suggest?	Misrouting, IVR confusion, or non-intentional calls — not a staffing problem
What is AHT?	Average Handle Time = Talk Time + Hold Time + ACW
What is ASA?	Average Speed of Answer — average wait time before an agent answers
What is Containment Rate?	Percentage of interactions resolved in IVR without reaching an agent
Where are Abandon Intervals configured?	Admin → Contact Center → Analytics Settings