I’d like you to dive deep into my customer data and turn the raw numbers into clear, actionable insights I can use to improve retention and boost lifetime value. The primary aim here is research and analysis—no app development, system overhaul, or UX studies—just solid, defensible data analysis. You will receive anonymised customer records that include purchase history, demographic attributes, engagement metrics, and a few open-text feedback fields. The job begins with cleaning and validating the dataset, moves through exploratory analysis, and ends with a concise executive summary of findings. Please feel free to work in Python (pandas, NumPy, scikit-learn), R, or SQL—whatever lets you slice cohorts, uncover trends, and surface hidden correlations most effectively. If you prefer visual platforms such as Tableau or Power BI for dashboards, that’s perfectly fine as long as I can interact with the final outputs. Deliverables • A well-commented notebook or script that reproduces every step of the analysis • A polished report (PDF or slide deck) highlighting key patterns, anomalies, and customer segments worth targeting • At least three visualisations—charts, heatmaps, or dashboards—making the insights intuitive at a glance • A brief set of next-step recommendations tied directly to the data Acceptance Criteria All code must run end-to-end on my side, figures should be clearly labeled, and every claim in the report must be traceable to a specific metric or chart.