Retail Member Activity Analysis
An AWS data pipeline translating high-volume purchase activity into actionable weekly member segments.
Technology
SQLAWSApache Spark
Selected project note
The business context
The business needed a clearer and more timely view of member activity based on in-store purchase behaviour in order to support more targeted marketing and retention actions. High-volume transaction streams made it difficult to maintain a reliable and repeatable process using manual or fragmented approaches.
What was delivered
Built an automated ETL pipeline for member footfall and activity analysis using AWS services including Athena and Glue. The solution processed high-volume purchase data, applied business rules to classify members by recency of activity into dormant, lapsed, lapsing, and active segments, and supported reliable weekly execution in collaboration with data engineering teams.
The practical impact
Improved the business view of customer activity by turning raw transaction data into actionable behavioural segments. This enabled more targeted marketing responses, more consistent weekly reporting, and a stronger understanding of shifting member engagement patterns over time.