Customer Segmentation for Traveltide
February 2026 – Present
An end-to-end customer segmentation analysis for Traveltide, a travel booking platform, identifying high-value user groups and optimising marketing strategy through data-driven insights.
Data & Methodology
- Cleaned and prepared transactional and behavioural datasets
- Performed exploratory data analysis (EDA) to detect trends and anomalies
- Engineered features (recency, frequency, monetary value, booking patterns)
- Applied RFM analysis and clustering techniques such as K-Means
- Evaluated model performance using silhouette scores and cluster validation
Key Insights
- Identified high-value repeat travellers
- Detected price-sensitive seasonal customers
- Segmented inactive users for reactivation campaigns
- Enabled targeted offers based on travel behaviour and booking frequency
Tech Stack
- Python (Pandas, NumPy, Scikit-learn)
- SQL for data extraction
- Data visualisation (Matplotlib / Seaborn)
- Feature engineering and clustering algorithms
Business Impact
- Improved targeting accuracy for marketing campaigns
- Increased customer lifetime value through segmentation-based offers
- Provided actionable dashboards for decision-makers