Customer retention analysis insights
Churn Drivers
Goal: Understand why customers do not return after their first purchase and identify key drivers of churn, using historical transactional data (2016–2018) to simulate realistic retention scenarios.
What we did: We structured and analyzed transactional data using SQL, segmented customers (one-time, repeat, high-value churn), and identified key drivers of retention such as delivery performance and spending behavior. A fixed reference date was used to ensure meaningful churn classification aligned with the dataset timeframe.
Outcome: We identified key factors influencing customer drop-off and highlighted high-value lost customers, providing actionable insights to improve retention, customer experience, and overall business performance. While based on historical data, the methodology and insights are directly applicable to real-world business contexts.
