What you get

• Data collection strategy: Targeted plan to capture the right events, metrics, and sources (customers, operations, bookings, POS) with minimal engineering overhead.
• Advanced analysis: Cohorts, segmentation, funnel analysis, anomaly detection, and root‑cause exploration that reveal where to act first.
• Actionable dashboards: Executive and operational dashboards that surface decisions, not raw numbers—turn insights into daily actions.
• Predictive models: Demand and churn forecasts, appointment no‑show probability, and simple ML models that drive proactive decisions.
• Data literacy & adoption: Workshops and playbooks so teams use dashboards, interpret results, and run hypothesis‑driven experiments.
• Continuous improvement: Ongoing measurement, A/B testing guidance, and iterative model retraining to keep insights relevant.

Our approach

• Discovery & metric design: Define business objectives, success metrics (LTV, CAC, booking conversion), and tracking plan.
• Instrumentation & quality: Implement event tracking, data validation, and ETL that keep reports reliable.
• Analysis & insights: Deliver prioritized recommendations with impact estimates and playbooks for execution.
• Deploy predictive models: Prototype, validate, and operationalize simple forecasts or risk scores.
• Train & iterate: Hands‑on training, monthly health checks, and roadmap updates based on outcomes.

Typical timeline and outcomes

• Initial assessment & tracking plan: 1–2 weeks.
• Dashboard and analysis delivery: 2–4 weeks for core dashboards and 1–2 high‑impact analyses.
• Predictive prototype: 4–8 weeks depending on data quality and scope.
• Expected early results (30–90 days): clearer lead-to-booking funnels, prioritized operational fixes, and measurable increases in conversion or retention depending on focus.

Security, privacy, and governance

• Controls: Role‑based access, PII minimization, encryption in transit and at rest.
• Compliance: Guidance for HIPAA‑relevant workflows and data handling where required.
• Governance: Data dictionary, tagging taxonomy, retention policy, and runbooks for incident response.

KPIs we track (examples)

Booking conversion rate; time to first fill; average revenue per booking; churn or cancellation rate; no‑show rate; cost per acquisition; prediction accuracy for forecasts.