Turning customer data into retention strategies and revenue insights
Customer lifetime value analysis, retention modelling, credit risk assessment, and cohort analytics for financial services.
| Analytics | Engineering | Domain |
|---|---|---|
| Power BI (DAX) | SQL (Window Functions, CTEs) | Credit Risk |
| Tableau | dbt + BigQuery | Customer Analytics |
| Looker Studio | Python (pandas, scikit-learn) | Churn Prediction |
dbt + BigQuery — 5-tier customer scoring system for credit risk assessment, identifying profitable customers using payment behaviour analysis.
Power BI + DAX — CLV analysis, retention cohorts, and promotional effectiveness with £8,047 vs £7,435 CLV comparison and time-bias correction methodology.
MySQL — Window functions, CTEs, cumulative calculations, and trend analysis demonstrating financial services SQL patterns.
Python + Scikit-learn — 79% F1-score Gradient Boosting model with direct applications to deposit account and credit card attrition.
dbt + BigQuery + Looker Studio — £8.66M revenue analysis with RFM segmentation across 4,293 customers.
Mid-level Data Analyst roles in UK banking and fintech where I can apply customer analytics expertise to drive commercial decisions.
