Incident Volume per User

Goal of the analysis:

Quantify how many incidents (break/fix tickets) are generated per user over a defined period to assess technology stability, end-user experience, and support efficiency. Executives use this to pinpoint chronic friction in the digital workplace, size the service desk workload, and prioritize investments in endpoint standardization, application reliability, training, and automation. A balanced view distinguishes true reliability problems from underreporting or request deflection effects.

Data required:

• Incident management records (ITSM):
  • Ticket ID, type (incident vs service request), category/subcategory (e.g., device, access, network, SaaS), priority.
  • Requester/affected user ID, department, location, resolver group.
  • Timestamps: created, first response, resolved, closed; major incident flag and parent/child linkage.
  • Channel: portal, email, phone, chat, bot; source (monitoring vs user-reported).
• User directory and HR context:
  • Unique user identifier, employment status (active/terminated), FTE %, job family/role, cost center, region/time zone.
  • Start/leave dates to compute exposure days within the period.
• Asset and endpoint management:
  • Device inventory and assignments (Intune/Endpoint Manager, Jamf, SCCM), OS/version, device age.
  • VDI/virtual app usage and thin client mappings.
• Service catalog and application usage (optional but valuable):
  • List of core apps/services by user segment; criticality/tier.
  • Active user metrics by application (SSO logs, O365/Google Workspace telemetry) to define “active users.”
• Calendars and SLA rules:
  • Business hours, holidays per region for normalization (if comparing business-only operations).
  • Definitions for incident vs request; deduplication rules for mass/major incidents.

Detailed step-by-step instruction on how to conduct the analysis:

1. Define scope and metric:
   • Period (e.g., monthly or quarterly), user population (employees, contractors; exclude service accounts), and ticket types (incidents only; requests excluded).
   • Primary KPI: Incidents per user per month (or per 100 users). Document treatment of major incidents and duplicate tickets.
2. Extract data:
   • From ITSM (ServiceNow, Jira Service Management, Remedy): full ticket export with requester and category fields plus parent/child links.
   • From directory/HR (Azure AD/Okta + Workday/SuccessFactors): user roster with org attributes and start/end dates.
   • From endpoint tools (Intune/Jamf/SCCM): device assignments, OS, age for segmentation.
   • Optional: SSO/app telemetry for “active user” denominator.
3. Cleanse and normalize:
   • Standardize user IDs and join tickets to user records; resolve duplicates and shared mailboxes to the correct person or team.
   • Exclude service requests and non-user incidents (monitoring-only, infrastructure) unless analyzing separately.
   • Collapse duplicate tickets from mass incidents: use major incident parent to avoid counting multiple child tickets separately, or report with and without deduplication.
4. Compute denominators:
   • Average active users = sum of user-days in period ÷ days in period. Include partial-month joiners/leavers.
   • Optionally compute “active digital users” using app sign-in activity (e.g., at least one SSO in period) for a more precise denominator.
   • For FTE normalization, multiply user-days by FTE % where relevant.
5. Calculate metrics:
   • Total incident count = number of scoped incidents in period after deduplication rules.
   • Incident Volume per User = Total incidents ÷ Average active users.
   • Express as per 100 users for communication: Incidents per 100 Users = (Total incidents ÷ Average active users) × 100.
   • Distribution metrics: share of incidents generated by top 10% of users; % of users with zero incidents; % with 3+ incidents.
6. Segment and compare:
   • By department/function, location, role, seniority, employment type, and resolver group.
   • By technology: device type/OS, device age, specific applications/services.
   • By channel: chat/portal vs email/phone to assess deflection and digital adoption.
7. Trend analysis:
   • Plot monthly incidents per 100 users and the composition by category over 6–12 months.
   • Overlay change events: rollouts, OS patches, app releases, and hardware refresh cycles.
   • Measure seasonality (e.g., new hire waves, fiscal close) and after-hours patterns.
8. Root cause indicators:
   • Pareto of top categories/subcategories by volume per 100 users (e.g., password resets, VPN, conferencing, laptop hardware).
   • Correlate high-volume user cohorts with device age, OS version, or app usage to isolate drivers.
   • Identify chronic users (e.g., ≥5 incidents/quarter) and chronic assets with repeated incidents.
9. Validation:
   • Spot-check classification accuracy for high-volume categories; confirm deduplication behavior for major incidents.
   • Ensure user roster aligns with period (no terminated users inflating denominator).

Format of the output of analysis:

• Executive summary slide: incidents per 100 users (current, trend), top categories, top cohorts, and 3–5 recommended actions.
• Time-series charts of incidents per 100 users with stacked category composition and key rollout markers.
• Heatmaps: incidents per 100 users by department/location and by OS/device age bands.
• Distribution views: histogram of incidents per user; table of top 10% user cohort share; list of chronic users/assets (de-identified for executive audiences).
• Pareto chart of categories and a channel mix chart (portal/chat vs email/phone).
• Benchmark table comparing internal targets and peer ranges (if available).

How to interpret results:

• High overall incidents per user suggest unstable endpoints/apps, insufficient automation (e.g., password resets), or poor self-service adoption. Investigate top categories and device/app cohorts.
• Low incidents per user can be positive (robust environment, effective self-help) or indicate underreporting. Cross-check with employee CSAT, shadow IT usage, or outage metrics.
• Heavy tail concentration (top 10% users driving disproportionate volume) signals training gaps, defective hardware, or role-specific workflows that need targeted solutions.
• Category shifts over time point to root causes: spikes in VPN or conferencing often follow policy or client updates; hardware spikes correlate with device age.
• Channel mix matters: higher portal/chat share with stable or falling volume usually indicates better deflection; rising email/phone share may reflect dissatisfaction with self-service.
• Compare departments/regions: persistent outliers often reflect localized technology stacks, bandwidth constraints, or support coverage gaps.

Steps a company can take to improve on this measure:

• Process and self-service:
  • Expand knowledge articles and guided workflows for top categories (e.g., self-service password reset, VPN checks).
  • Introduce chatbots/virtual agents with proven containment for repetitive issues; improve portal search and taxonomy.
  • Standardize ticket categorization and routing to improve analytics and faster resolution.
• Endpoint and application reliability:
  • Accelerate device refresh for high-incident age bands; standardize images and harden configurations.
  • Improve patching cadence and pre-deployment testing for OS/app updates that correlate with spikes.
  • Instrument SSO and key apps for health; remediate recurring client-side failures.
• Training and adoption:
  • Deliver targeted micro-training to high-volume cohorts (e.g., finance close tools, remote access, conferencing etiquette).
  • Onboarding programs emphasizing core digital workflows to reduce early-tenure incidents.
• Support model optimization:
  • Proactive outreach to chronic users/assets; implement “Tech Bar” or floor-walking in hotspots.
  • Enhance shift-left: empower L1 with runbooks and remote tools; route complex categories directly to specialists.
  • Measure and increase digital channel adoption (portal/chat) with SLAs and clear incentives.
• Governance and vendors:
  • Retire or consolidate redundant tools causing confusion; enforce standards for peripherals and drivers.
  • Hold vendors accountable for high-defect rates; deploy telemetry to substantiate issues.
• Scenario tips:
  • If incidents per user rise while password-related tickets dominate: deploy/mandate self-service password reset and MFA recovery flows.
  • If specific OS versions show higher volume: fast-track upgrades or roll back problematic patches.
  • If one department is an outlier: assess specialized apps or workflows; consider embedded IT support.
  • If underreporting suspected: pair with pulse CSAT or “raise your hand” campaigns and monitor channel mix.

Benchmark comparisons:

General benchmarks:

• Internal IT incidents (break/fix only): 0.2–0.6 incidents per user per month is typical; high-performing environments achieve 0.1–0.3.
• Total tickets (incidents + requests): 0.6–1.5 per user per month; strong self-service and automation trend toward the lower bound.
• Healthy distributions show 50–70% of users with zero incidents in a month and <10% with 3+.

Segment- or industry-specific benchmarks:

• Highly standardized VDI/call centers: 0.1–0.3 incidents per user per month.
• Engineering/R&D-heavy or field-force organizations: 0.5–0.9 incidents per user per month due to complex toolchains/connectivity.
• Rapid-growth/new-hire periods may temporarily elevate volume by 10–25%.

If external benchmarks are limited, construct internal references: compare month-over-month, across departments/sites, and against the internal top quartile. Set targets for top categories (e.g., reduce VPN incidents per 100 users by 30% in two quarters) and periodically recalibrate as technology stacks evolve.