Analyzed loyalty member data to identify high-value customers, measure tier effectiveness, and quantify financial risk from unredeemed points. Built SQL queries using CTEs and window functions to surface churn patterns, cohort behavior, and regional profitability. Delivered 7 actionable recommendations projectedd to improve retention of top 10% CLV members and reduce $2M+ in points liability exposure.
Analyzed EHR data across encounters, patients, payers and procedures to surface revenue leakage, payer performance, and utilization patterns. Write SQL to validate referential integrity, calculate payer coverage % by service line, audit procedure-to-claim rollups, and ranked super-utilizer patients for care management. Key insights: inpatient encounters drove 68% of revenue at 79% coverage, and top 1% of patients represented 13% of total cost. Identified $3.2K avg underbilling in surgical cases and delivered monthly exec trend model.
Audited HRIS data to quantify attrition cost and diagnose turnover drivers across department, tenure, and compensation. Developed SQL logic using CTEs and risk scoring to test if overtime, lack of promotion, commute distance, and new managers predict churn. Built a composite risk model and dollarized attrition using a 0.5x salary replacement rule. Findings: employees with overtime churn at 30% vs 10% without, and $3.4M was lost to attritionn last year.Delivered a save-list of high-risk, high-value employees and ROI case for rentention programs.
Analyzed 800K+ CFPB-style customer complaints to identify SLA breaches, emerging product risks, and geographic hospots. Used SQL YoY trend analysis, percentile-based outlier detection, and exception reporting with LAG, PERCENTILE_CONT, and conditional aggregation. Key findings: email channel had 23% SLA breach rate vs 6% for phone, and 'Debt collection' complaints spiked 158% YoY. Delivered 9 recommendations for Ops and compliance, including a seasonal staffing model and vendor risk alerts projected to reduce regulatory exposure.