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RFM Customer Segmentation Analysis
I developed a comprehensive RFM (Recency, Frequency, Monetary) Customer Segmentation Analysis using Python to help businesses identify, understand, and target their most valuable customers. This data-driven approach transforms raw transaction data into actionable marketing strategies, enabling personalized customer engagement and improved ROI.
RFM is powerful when businesses actually act on it. Curious — did you segment using fixed score thresholds or percentile-based scoring? The methodology usually makes a big difference in interpretation.
I used percentile-based scoring (quintiles) for this analysis it adapts to the data distribution and prevents skew from outliers. Fixed thresholds can work for mature businesses with stable benchmarks, but percentiles ensure segments stay meaningful even as customer behavior evolves
That’s a strong choice. Percentiles definitely keep segments adaptive, especially in growth-phase businesses.
Have you ever compared how segment migration behaves month-over-month with quintiles? Sometimes the movement patterns reveal more than the segment labels themselves.
Absolutely! Segment migration tracking is where the real insights live quintiles can shift 15-20% month-over-month in volatile markets. I typically build migration matrices to spot 'at-risk' movers early. Haven't published that analysis yet, but it's on my roadmap.
Thx a lot! Really appreciate it 😍
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Data-Driven Sales Dashboard & Conversion Funnel Analysis: Following a deep dive into the client's sales and operational data, I developed this interactive dashboard to visualize key performance indicators (KPIs) in real-time. The tool provides business owners with a clear view of their conversion funnel—tracking the exact journey from initial conversations to calls and booked appointments. By highlighting daily trends, drop-off points, and conversion rates, this custom analytics solution empowers the management team to stop guessing and start making informed, data-backed decisions to optimize their sales pipeline.
Project Title: Blinkit Grocery Sales Analysis Dashboard
Project Summary:
This interactive dashboard provides a comprehensive analysis of $1.20M in total sales for Blinkit, transforming raw retail data into actionable business intelligence.
Core Focus
Sales Performance: Tracking key KPIs including Average Sales ($141) and Customer Ratings (3.9).
Inventory Insights: Analyzing performance across 8,500+ items, highlighting top categories like Fruits and Snack Foods.
Outlet Analytics: Evaluating revenue distribution across Outlet Sizes (Small/Medium/High) and Geographic Tiers (Tier 1–3).
Trend Mapping: Visualizing establishment growth and outlet types (Grocery vs. Supermarkets) to identify high efficiency locations
Solid breakdown of KPIs — especially tying outlet size and geographic tier into performance.
I’m curious: did you look at whether customer ratings correlate with revenue per outlet tier? Sometimes high-rating stores in Tier 2/3 outperform purely due to loyalty concentration...
In designing this CRM dashboard, our focus was on creating a clear and structured environment for managing sales data and customer relationships. The interface consolidates key metrics such as total deal value, average deal size, win rate, and revenue dynamics into a single workspace, allowing users to quickly understand performance trends and identify changes over time.
this is super clean🙌
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