Customer behaviour analysis for Ironhack Payment, a financial services provider, to uncover insights into retention, revenue trends, and transaction success. Using Python, Pandas, and Plotly, I created cohort-based views that highlight customer lifecycle patterns and seasonal trends to support strategic growth.
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Revenue by Cohort: October 2020 cohort led in revenue, with strong seasonal growth patterns in spring and summer.
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Retention Analysis: May 2020 cohort showed the highest early retention, visualized through heatmaps.
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Transaction Success: Post-June 2020, transaction failures decreased, while October 2020 cohort generated the highest revenue.
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RFM Segmentation: Identified high-value customer groups, providing insights for targeted retention strategies.
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Loan Amount & Frequency: Positive correlation found, with larger loans linked to higher borrowing frequency.
- Exploratory Analysis - Exploratory_Analysis.ipynb: Initial data cleaning and preparation.
- Main Analysis - Main.ipynb: Cohort analysis and visualizations.
The cohort analysis provided valuable insights for Ironhack Payment’s growth strategy:
- Seasonal Revenue Trends: Identified opportunities to boost engagement during spring and summer.
- High-Value Customer Segmentation: RFM analysis highlighted key customer groups for targeted retention efforts.
- Improved Operational Efficiency: Transaction failures decreased post-June 2020, indicating enhanced reliability.
- Actionable Strategies: Insights support initiatives to increase customer loyalty, optimize revenue, and improve transaction success.
Together, these findings guide strategic actions to strengthen Ironhack Payment's market position.
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