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Cohort analysis and customer lifetime value mapping extracting sales retention indicators from raw e-commerce datasets.
This data analytics project structures over 500,000 transaction rows. It extracts user signup cohorts, maps month-on-month retention rates, and flags categories showing higher churn risk.
Dealing with raw transaction logs containing duplicate customer records and missing return metrics.
Created a staging Python cleaning pipeline using Pandas to resolve null values and deduplicate data.
Wrote optimized SQL window queries performing cohort grouping calculations.
Mapped customer lifetime value (CLV) variables showing profitable user segments.
Built a dashboard with filters for category, region, and acquisition dates.
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