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Overview

Operational data is often the richest but least analyzed. SkoutLab helps operations teams find inefficiencies, predict issues, and optimize processes.

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Common Questions

Efficiency

  • Where are the bottlenecks?
  • What’s causing delays?
  • How can we reduce cycle time?

Quality

  • What’s driving defects/errors?
  • Where are SLAs at risk?
  • What predicts quality issues?

Capacity

  • Are we over/under capacity?
  • What’s utilization by team/resource?
  • When will we hit limits?

Patterns

  • When do issues occur?
  • What’s the root cause of recurring problems?
  • Are there hidden correlations?

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Example: Support Operations

Question asked:
“Why has average ticket resolution time increased?”
What SkoutLab found:
Status: ConfirmedTicket growth significantly outpaced team growth. Average tickets per agent increased from 28 to 36 per week.Recommended action: Evaluate hiring plan or explore automation
Status: Confirmed“Integration” category tickets up 89%, require 3x longer than average. Product launch created new support burden.Recommended action: Create integration documentation; consider specialist team
Status: ConfirmedTime-to-escalation increased from 4 hours to 11 hours. Tier 2 backlog growing.Recommended action: Review escalation criteria and Tier 2 capacity

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Data You’ll Need

DataWhy It Helps
Tickets/CasesSupport, service requests, incidents
Time trackingCycle times, wait times, resolution times
Resource dataTeam capacity, assignments, utilization
Quality metricsErrors, rework, SLA compliance

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Operations-Specific Analysis

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Process Mining

SkoutLab identifies:
  • Actual process flows (vs documented)
  • Bottlenecks and delays
  • Rework loops
  • Handoff inefficiencies

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Predictive Signals

Finds leading indicators:
  • What predicts escalation?
  • What signals quality issues?
  • What drives cycle time?

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Anomaly Detection

Catches operational issues:
  • Unusual volumes or patterns
  • SLA breach risk
  • Capacity constraints

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Tips for Operations Analysis

Define Metrics

“Resolution time” is clearer than “performance”

Time Boundaries

Specify periods for meaningful comparison

Segment by Type

“For enterprise tickets” or “in the billing queue”

Ask About Root Causes

“What’s causing X” gets deeper than “show me X”

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Sample Questions to Try

"What's causing delays in our order fulfillment process?"

"Which support ticket types have the lowest customer satisfaction?"

"Are there patterns in when system incidents occur?"

"What predicts whether a ticket will need escalation?"

"Where are we losing time in the customer onboarding process?"