Appointment No-Show Rate Analysis Builder

You are an operations data analyst. Build an appointment no-show analysis for the organization below.

Organization type: [clinic, salon, repair service, tutoring center, field service, government office, etc.]
Appointment types: [consult, follow-up, installation, class, intake, etc.]
Dataset fields available: [appointment date/time, booked date, customer segment, location, provider, reminder sent, cancellation, reschedule, attendance]
Time period: [date range]
No-show definition: [missed without notice, late cancellation window, reschedule rules]
Business impact: [lost revenue, unused capacity, waitlist delays, provider idle time, customer outcomes]
Segments to compare: [location, service type, provider, lead time, new vs returning, channel, day/time]
Reminder process: [SMS, email, phone, portal, none, timing]
Policy context: [fees, deposits, grace periods, accessibility constraints, legal/privacy limits]
Data quality concerns: [missing fields, inconsistent statuses, manual overrides, duplicate appointments]
Goal: [reduce no-shows, improve scheduling, protect access, forecast capacity]

Produce:
1. Data cleaning plan and status mapping rules.
2. Core KPI definitions for no-show rate, late cancellation rate, fill rate, and lost capacity.
3. Segment analysis plan with tables and charts to create.
4. Lead-time, day-of-week, time-of-day, reminder, and appointment-type diagnostics.
5. Root-cause hypotheses grounded in the data, not stereotypes.
6. Intervention ideas with expected impact, effort, fairness risk, and measurement plan.
7. Forecasting or risk-scoring approach if enough data exists.
8. Dashboard layout for leaders and schedulers.
9. Data privacy and fairness checks before changing policies.
10. Executive summary template with recommended next experiments.

Make the analysis useful for operations decisions while avoiding punitive assumptions about customers.