Workplace Service Delivery Insights

Overview

This skill covers generating insights and analytics for Workplace Service Delivery (WSD) in ServiceNow:

• Analyzing space utilization rates across buildings, floors, and zones
• Tracking desk and room booking patterns to optimize space allocation
• Monitoring facility management service request volumes, categories, and SLA compliance
• Identifying peak usage periods and underutilized spaces for capacity planning
• Generating facility management KPI dashboards with trend analysis
• Producing actionable recommendations for workplace optimization

When to use: When reviewing workplace space efficiency, planning office reconfigurations, analyzing facility service delivery performance, optimizing hot-desking strategies, or preparing workplace analytics for real estate decisions.

Prerequisites

• Roles: wsd_admin, facility_manager, admin, or workspace_admin
• Plugins: com.snc.workplace_service_delivery (Workplace Service Delivery), com.snc.facility_management (Facility Management) recommended
• Access: Read access to wsd_space, wsd_reservation, wsd_floor, wsd_building, fm_facility_request, sc_req_item tables
• Data: Active workplace spaces with reservation and sensor data
• Related Skills: reporting/executive-dashboard for dashboard creation, reporting/trend-analysis for trend analytics

Procedure

Step 1: Retrieve Building and Floor Inventory

Establish the baseline of available workspace inventory.

MCP Approach:

Tool: SN-Query-Table
Parameters:
  table_name: wsd_building
  query: active=true
  fields: sys_id,name,location,total_floors,total_capacity,address,time_zone,operational_status
  limit: 50

Tool: SN-Query-Table
Parameters:
  table_name: wsd_floor
  query: building=[BUILDING_SYS_ID]^active=true
  fields: sys_id,name,building,floor_number,total_spaces,available_spaces,floor_plan
  limit: 20

REST Approach:

GET /api/now/table/wsd_building
  ?sysparm_query=active=true
  &sysparm_fields=sys_id,name,location,total_floors,total_capacity,address,time_zone,operational_status
  &sysparm_display_value=true

GET /api/now/table/wsd_floor
  ?sysparm_query=building=[BUILDING_SYS_ID]^active=true
  &sysparm_fields=sys_id,name,building,floor_number,total_spaces,available_spaces
  &sysparm_display_value=true

Step 2: Analyze Space Utilization

Query space records to calculate utilization metrics.

MCP Approach:

Tool: SN-Query-Table
Parameters:
  table_name: wsd_space
  query: floor.building=[BUILDING_SYS_ID]^active=true
  fields: sys_id,name,space_type,capacity,floor,is_reservable,status,amenities
  limit: 500

Space Type Classification:

Step 3: Analyze Reservation Patterns

Query booking data to identify usage patterns.

MCP Approach:

Tool: SN-Query-Table
Parameters:
  table_name: wsd_reservation
  query: space.floor.building=[BUILDING_SYS_ID]^start_date>=javascript:gs.daysAgo(30)^ORDERBYstart_date
  fields: sys_id,space,user,start_date,end_date,state,check_in_time,check_out_time,no_show,duration
  limit: 2000

REST Approach:

GET /api/now/table/wsd_reservation
  ?sysparm_query=space.floor.building=[BUILDING_SYS_ID]^start_date>=javascript:gs.daysAgo(30)
  &sysparm_fields=sys_id,space,user,start_date,end_date,state,check_in_time,check_out_time,no_show,duration
  &sysparm_limit=2000
  &sysparm_display_value=true

Key Metrics to Calculate:

Step 4: Identify Peak and Off-Peak Patterns

Analyze time-based usage patterns for capacity planning.

MCP Approach:

Tool: SN-Query-Table
Parameters:
  table_name: wsd_reservation
  query: space.floor.building=[BUILDING_SYS_ID]^start_date>=javascript:gs.daysAgo(30)^state=completed
  fields: start_date,end_date,space.space_type,space.floor
  limit: 5000

Aggregate by day of week and hour to build a heatmap:

=== UTILIZATION HEATMAP ===
Building: [name]
Period: Last 30 days

         Mon   Tue   Wed   Thu   Fri
08:00    35%   42%   68%   55%   28%
09:00    62%   71%   88%   78%   45%
10:00    78%   85%   95%   89%   52%
11:00    82%   88%   97%   91%   48%
12:00    45%   52%   60%   55%   30%
13:00    70%   78%   92%   82%   42%
14:00    75%   82%   90%   85%   38%
15:00    68%   72%   85%   78%   32%
16:00    45%   50%   62%   52%   22%
17:00    20%   25%   30%   25%   10%

PEAK: Wednesday 11:00 (97%)
LOW: Friday 17:00 (10%)

Step 5: Analyze Facility Service Requests

Review facility management request patterns.

MCP Approach:

Tool: SN-Query-Table
Parameters:
  table_name: fm_facility_request
  query: sys_created_on>=javascript:gs.daysAgo(30)
  fields: sys_id,number,category,subcategory,priority,state,location,assignment_group,opened_at,closed_at,close_code
  limit: 500

REST Approach:

GET /api/now/table/fm_facility_request
  ?sysparm_query=sys_created_on>=javascript:gs.daysAgo(30)
  &sysparm_fields=sys_id,number,category,subcategory,priority,state,location,assignment_group,opened_at,closed_at,close_code
  &sysparm_limit=500
  &sysparm_display_value=true

Step 6: Check SLA Compliance for Facility Services

Measure service delivery performance against SLA targets.

MCP Approach:

Tool: SN-Query-Table
Parameters:
  table_name: task_sla
  query: task.sys_class_name=fm_facility_request^sys_created_on>=javascript:gs.daysAgo(30)
  fields: sys_id,task,sla,stage,has_breached,planned_end_time,percentage,business_percentage
  limit: 500

Calculate SLA metrics:

Step 7: Analyze Workplace Incidents

Review facility-related incidents for recurring issues.

MCP Approach:

Tool: SN-Query-Table
Parameters:
  table_name: incident
  query: category=facilities^sys_created_on>=javascript:gs.daysAgo(30)
  fields: sys_id,number,short_description,category,subcategory,priority,state,location,assignment_group,opened_at,resolved_at
  limit: 200

Step 8: Generate Workplace Insights Report

Compile all analytics into an actionable insights report.

=== WORKPLACE SERVICE DELIVERY INSIGHTS ===
Report Period: [start_date] - [end_date]
Scope: [Building/Campus/All Locations]

SPACE INVENTORY:
Buildings: [count] | Floors: [count] | Total Spaces: [count]
Desks: [count] | Meeting Rooms: [count] | Phone Booths: [count]

UTILIZATION SUMMARY:
Overall Space Utilization: [%]
  Desks: [%] | Meeting Rooms: [%] | Phone Booths: [%]
Peak Day: [day] ([%]) | Lowest Day: [day] ([%])
Peak Hour: [time] ([%]) | Lowest Hour: [time] ([%])

BOOKING ANALYTICS:
Total Reservations (30 days): [count]
Unique Users: [count]
Avg Daily Bookings: [count]
No-Show Rate: [%] ([count] no-shows)
Avg Booking Duration: [hours]
Most Popular Spaces: [list top 5]
Least Used Spaces: [list bottom 5]

FACILITY SERVICE METRICS:
Total Requests (30 days): [count]
Open: [count] | In Progress: [count] | Closed: [count]
Avg Resolution Time: [hours/days]
SLA Compliance: [%]

TOP REQUEST CATEGORIES:
| Category | Volume | Avg Resolution | SLA Met |
|----------|--------|---------------|---------|
| [category] | [count] | [time] | [%] |

FACILITY INCIDENTS:
Total: [count] | Recurring: [count]
Top Issues: [list]

RECOMMENDATIONS:
1. [Space optimization recommendation based on utilization data]
2. [No-show reduction strategy based on booking patterns]
3. [Service improvement recommendation based on SLA data]
4. [Capacity planning recommendation based on peak analysis]
5. [Cost optimization based on underutilized spaces]

Step 9: Generate Desk Booking Analytics

Deep-dive into hot-desking and desk booking metrics.

MCP Approach:

Tool: SN-Query-Table
Parameters:
  table_name: wsd_reservation
  query: space.space_type=desk^start_date>=javascript:gs.daysAgo(30)
  fields: sys_id,space,user,start_date,state,no_show,space.floor,space.floor.building
  limit: 2000

=== DESK BOOKING ANALYTICS ===
Total Desk Reservations: [count]
Unique Desk Users: [count]
Avg Desks Booked/Day: [count] of [total] ([%])

USER PATTERNS:
- Regular Bookers (>3x/week): [count] users
- Occasional (1-2x/week): [count] users
- Rare (<1x/week): [count] users

FLOOR-LEVEL BREAKDOWN:
| Floor | Desks | Avg Utilization | No-Show Rate |
|-------|-------|----------------|--------------|
| [floor] | [count] | [%] | [%] |

NO-SHOW ANALYSIS:
Total No-Shows: [count] ([%] of bookings)
Cost of No-Shows: ~$[estimated wasted space cost]
Top No-Show Times: [pattern]
Recommendation: [auto-release policy suggestion]

Step 10: Generate Trend Analysis

Compare current period metrics against previous periods.

=== TREND ANALYSIS ===
Metric Comparison: Current Month vs Previous Month

| Metric | Previous | Current | Change |
|--------|----------|---------|--------|
| Overall Utilization | [%] | [%] | [+/-]% |
| Daily Bookings | [avg] | [avg] | [+/-]% |
| No-Show Rate | [%] | [%] | [+/-]% |
| Facility Requests | [count] | [count] | [+/-]% |
| SLA Compliance | [%] | [%] | [+/-]% |
| Unique Users | [count] | [count] | [+/-]% |

TRENDS:
- Space utilization [increasing/decreasing/stable] over past 3 months
- [Day] consistently the busiest day ([%] avg utilization)
- Meeting room demand [exceeding/meeting/below] capacity
- Facility service SLA compliance [improving/declining]

Tool Usage

Best Practices

1. Analyze at least 30 days of data for meaningful utilization patterns
2. Exclude holidays and closures from utilization calculations to avoid skewing metrics
3. Segment by space type -- desks, meeting rooms, and booths have different utilization benchmarks
4. Include no-show analysis -- no-shows represent significant waste in hot-desking environments
5. Correlate with headcount -- utilization should be measured against actual employee count, not total capacity
6. Consider hybrid work patterns -- Monday/Friday typically have lower utilization in hybrid environments
7. Track trends over time -- single-point metrics are less valuable than trends
8. Include cost context -- translate underutilization into dollar impact for stakeholders
9. Factor in amenities -- spaces with better amenities typically show higher utilization
10. Benchmark across locations -- compare similar buildings to identify best practices

Troubleshooting

Examples

Example 1: Monthly Workplace Report for Real Estate Team

Scenario: Generate monthly space utilization report for real estate planning.

Tool: SN-Query-Table
Parameters:
  table_name: wsd_reservation
  query: start_date>=javascript:gs.beginningOfLastMonth()^start_dateONLast month@javascript:gs.beginningOfLastMonth()@javascript:gs.endOfLastMonth()
  fields: space,user,start_date,state,no_show,space.space_type,space.floor.building
  limit: 5000

Output:

MONTHLY WORKPLACE REPORT - February 2026
Headquarters Campus

Overall Utilization: 67% (up from 61% in January)
Peak Day: Wednesday at 89% capacity
Underutilized Floors: Building B, Floors 4-5 (32% avg)

RECOMMENDATION: Consolidate Building B floors 4-5 to reduce
leased space by 15,000 sq ft. Estimated annual savings: $450K.

Example 2: Facility Service Performance Review

Scenario: Generate quarterly facility service delivery scorecard.

FACILITY SERVICES SCORECARD - Q1 2026
Total Requests: 1,247
SLA Compliance: 94.2% (target: 95%)

BY CATEGORY:
- Cleaning: 412 requests, 97% SLA met
- Maintenance: 298 requests, 91% SLA met (BELOW TARGET)
- HVAC: 187 requests, 93% SLA met
- Security: 156 requests, 98% SLA met
- Other: 194 requests, 92% SLA met

ACTION: Maintenance SLA below target due to parts procurement delays.
Recommend pre-stocking common replacement parts at each building.

Related Skills

• reporting/executive-dashboard - Build executive dashboards for workplace metrics
• reporting/trend-analysis - Advanced trend analysis for facility data
• reporting/sla-analysis - Detailed SLA compliance analysis
• admin/instance-management - Instance configuration for WSD module