Purchase-Based Customer Segmentation Analysis
Budget: ₹12,500 – ₹37,500 INR
I have a complete purchase-history dataset ready to go and I want to turn it into actionable customer segments. The single objective is clear: identify distinct target customer groups so I can market to them more effectively.
The analysis must surface three core insights:
• Which customers are truly high-value
• Who buys most frequently
• Who shows signs of churning
You’re free to choose the right statistical or machine-learning approach—RFM scoring, K-means, hierarchical clustering, or a hybrid model—as long as it fits the data. I’ll supply the raw CSV and a short data dictionary; you return:
1. A cleaned, well-documented dataset with the segment labels appended
2. A concise notebook or script (Python, R, or SQL-based) that reproduces the segmentation step-by-step
3. Visual summaries—charts or dashboards—that make the three insights immediately clear
4. A brief write-up on how to act on each segment
Accuracy, reproducibility, and clear explanations matter more to me than fancy visuals, so keep the code readable and the recommendations practical.
The analysis must surface three core insights:
• Which customers are truly high-value
• Who buys most frequently
• Who shows signs of churning
You’re free to choose the right statistical or machine-learning approach—RFM scoring, K-means, hierarchical clustering, or a hybrid model—as long as it fits the data. I’ll supply the raw CSV and a short data dictionary; you return:
1. A cleaned, well-documented dataset with the segment labels appended
2. A concise notebook or script (Python, R, or SQL-based) that reproduces the segmentation step-by-step
3. Visual summaries—charts or dashboards—that make the three insights immediately clear
4. A brief write-up on how to act on each segment
Accuracy, reproducibility, and clear explanations matter more to me than fancy visuals, so keep the code readable and the recommendations practical.