Business Context

FlexSpace operates 120 coworking facilities across 18 cities. In the last quarter, member satisfaction fell while facility issue volume increased, and regional operations leaders want a data-driven framework for deciding which issues to fix first.

Metric Scenario

Last month, FlexSpace logged 9,600 facility issues across HVAC, internet, cleaning, access control, plumbing, and elevator categories. Overall member satisfaction (CSAT) dropped from 4.4 to 4.1, monthly member retention declined from 92% to 89%, and workspace booking conversion fell from 68% to 63%. Issue resolution SLA compliance also declined from 88% to 76%. The COO wants to know which operational problems are causing the most business impact and how to allocate the next month's $400K maintenance budget.

A sample of recent issue patterns:

• HVAC issues: 1,400 tickets, average resolution time 30 hours, CSAT in affected facilities 3.8
• Internet outages: 620 tickets, average resolution time 9 hours, booking conversion in affected facilities 54%
• Cleaning issues: 3,100 tickets, average resolution time 18 hours, retention in affected facilities 87%
• Access control issues: 480 tickets, repeat issue rate 22%, peak-hour complaints up 35%

Requirements

1. Define the primary KPI you would use to prioritize facility issues and explain why.
2. Show how you would calculate it and compare issue categories.
3. Decompose the KPI to identify the highest-impact drivers by facility, issue type, and severity.
4. Distinguish leading indicators from lagging business outcomes.
5. Recommend how to allocate the maintenance budget based on the data.

Data Available

• facility_issues: ticket_id, facility_id, issue_type, severity, opened_at, resolved_at, vendor_id, repeat_flag
• member_feedback: facility_id, date, CSAT, NPS, complaint_category
• bookings: facility_id, date, visits, booking_starts, completed_bookings
• memberships: member_id, facility_id, start_date, renewal_date, churn_flag