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Analytics Settings

TopicDetail
NavigationAdmin → Contact Center → Analytics Settings
PurposeConfigure abandon intervals and analytics capture settings for queue reporting
Abandon Intervals7 configurable intervals (A–G) categorizing when customers disconnect from queue

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.


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.

IntervalDefault Wait RangeInterpretation
A0–6 secondsImmediate disconnects — misrouting, robocalls, misdials, IVR confusion
B6–20 secondsEarly abandons after entering queue
C20–40 secondsShort wait abandonment
D40–60 secondsModerate wait abandonment
E60–120 secondsCustomers leaving after ~1–2 minutes
F120–240 secondsLong queue wait frustration
G>240 secondsVery long wait abandonment

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


Analytics Implementation Steps

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

Real-Time Analytics

FeatureLocation
Performance ViewsPerformance → Workspace — pre-built views for Queues, Agents, and Interactions
DashboardsCustomizable screens with widgets for KPIs (Service Level, Agents On-Queue, Active Interactions, etc.)
Alerting RulesTrigger email or browser notifications when metrics hit thresholds (e.g., Wait Time > 5 minutes)

Historical Analytics

FeatureDescription
Standard ReportsPre-packaged PDF or CSV reports (e.g., Queue Abandonment Detail, Agent Log-level Report)
Dynamic ViewsFilter by date range, media type, wrap-up codes
ExportingManual export or scheduled delivery to S3 bucket or email address

Core Analytics Metrics

Interaction Volume

MetricDescription
OfferedTotal interactions entering the queue
AnsweredInteractions handled by agents
Flow-OutsInteractions exiting queue through routing or IVR actions
ConnectedInteractions successfully connected to agents

Queue Performance

MetricDescription
Service LevelPercentage of interactions answered within SLA target
ASAAverage Speed of Answer — average time before agent answers
Average Wait TimeAverage time customers wait in queue
Longest WaitLongest interaction currently waiting

Customer Behavior

MetricDescription
AbandonedInteractions disconnected before reaching an agent
Abandon %Abandoned ÷ Offered
Average Abandon TimeAverage wait time before customer hangs up
Short AbandonDisconnects within a configured short-time threshold

Agent Handling

MetricDescription
AHTAverage Handle Time = Talk Time + Hold Time + ACW
Talk TimeActive speaking time with customer
Hold TimeTime interaction placed on hold
ACWAfter Call Work time
TransfersInteractions transferred between agents or queues

IVR / Flow Metrics

MetricDescription
Flow OutcomesWhere customers exit an Architect flow (Success vs. Failure)
Containment RatePercentage of interactions resolved within IVR without reaching an agent
IVR DisconnectsCustomers disconnecting during IVR navigation

Advanced Metrics

MetricDescription
Agent UtilizationPercentage of agent time spent handling interactions
ConcurrencySimultaneous digital interactions handled
Callback RatePercentage of callers choosing callback instead of waiting
Recontact RateCustomers contacting support again after a recent interaction

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?


Knowledge Analytics

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

Search & Discovery

MetricDescription
Knowledge SearchesTotal searches performed in the knowledge base
Search Success RatePercentage of searches that returned useful articles
Search Failure RateSearches that produced no relevant results
Popular Search TermsMost frequently searched keywords

Article Usage

MetricDescription
Article ViewsNumber of times a knowledge article was opened
Articles SharedArticles sent to customers during interactions
Top ArticlesMost frequently accessed articles
Article FeedbackRatings or feedback from agents or customers

Self-Service & Automation

MetricDescription
Knowledge MatchBot successfully finds a relevant knowledge article
Confidence ScoreAI confidence in the article match
Knowledge FallbackBot cannot find a suitable article
Containment RateIssues resolved through self-service without an agent


Interview Cheat Sheet

QuestionAnswer
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