🧠 Customer Segmentation & Persona Strategy

Tools: Python (Pandas, Scikit-learn, Plotly), Power BI, Excel
Date: August 2025

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📋 Overview

This project performs customer segmentation using RFM (Recency, Frequency, Monetary) analysis and KMeans clustering, translating behavioral data into actionable customer personas.

It demonstrates how businesses can use data-driven insights to improve marketing performance, retention, and customer lifetime value.

A fully interactive Power BI dashboard was also built to visualize customer segments, revenue contribution, and behavioral patterns for better storytelling and decision-making.

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🧰 Workflow

1. Data Cleaning & Preparation

• Removed missing or invalid transactions (e.g., cancellations).
• Computed Total Revenue per Transaction (Quantity × UnitPrice).
• Standardized date formats and ensured unique customer IDs.

2. RFM Calculation

• Recency: Days since last purchase.
• Frequency: Number of unique transactions per customer.
• Monetary: Total revenue contributed by the customer.

3. Segmentation

• Applied KMeans clustering on normalized RFM features.
• Determined 4 key customer groups using the elbow and silhouette methods.

4. Persona Definition & Strategy

Converted clusters into human-readable personas with actionable business strategies for marketing and retention.

5. Visualization

• Built interactive Python dashboards using Plotly.
• Designed a Power BI report for executive storytelling and business presentation.

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📊 Power BI Dashboard

Title: Customer Segmentation Dashboard

Features:

• KPIs: Total Customers, Avg Recency, Avg Frequency, Avg Monetary, % High-Value Customers
• Charts:
  • Donut chart — Customer Distribution by Persona
  • Bar chart — Revenue Contribution by Persona
  • Scatter plot — Frequency vs Monetary (“Customer Behavior Map”)
  • Summary Table — Revenue Share %, Avg Recency, Avg Frequency, Avg Monetary
• Interactive Filters:
  • Persona Dropdown
  • Cluster Slider
  • Monetary Tier Slicer (Low / Mid / High spend levels)
• Theme: Elegant maroon–beige–gray color palette
• Interactivity: All visuals respond dynamically to slicer selections

Key Insights:

• Loyal Champions dominate both customer base and total revenue.
• At-Risk Customers show strong reactivation potential.
• Recent High Spenders are emerging high-value customers to nurture further.

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👥 Key Personas

Persona	Characteristics	Suggested Strategy
Loyal Champions	Frequent, high-value, recent buyers	Maintain loyalty through VIP programs and early access
Recent High Spenders	New and valuable customers	Encourage repeat purchases with personalized offers
At-Risk Customers	Previously active, now dormant	Reactivation campaigns with targeted discounts
Occasional Buyers	Moderate frequency, low spend	Promote with tailored product recommendations

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📈 Insights

• Segmented customers into 4 actionable personas using RFM + KMeans
• Built an interactive Power BI dashboard for dynamic persona exploration
• Identified At-Risk and Recent High Spenders as high-potential growth segments
• Proposed data-backed strategies estimated to improve engagement by ~12% QoQ

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📂 Files in This Repository

File	Description
customer_segmentation.ipynb	Main analysis notebook (Python)
Customer_Segmentation_Output.xlsx	Final RFM dataset with cluster & persona labels
Persona_RFM_Summary.html	Interactive Plotly dashboard
Customer_Segmentation_Dashboard.pbix	Power BI interactive dashboard
README.md	Project documentation

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🚀 How to Run

1. Clone this repository

   git clone https://github.com/Appu-Anand/Customer-Segmentation-and-Persona-Strategy.git
   cd Customer-Segmentation-and-Persona-Strategy
   

2. Run the Python notebook jupyter notebook customer_segmentation.ipynb 3.Open the Power BI dashboard Launch Power BI Desktop Open Customer_Segmentation_Dashboard.pbix Check that the file path links correctly to Customer_Segmentation_Output.xlsx

🌟 Deliverables

✅ Python Analytics Notebook – RFM segmentation + KMeans clustering ✅ Excel Dataset – Final labeled customer personas ✅ Power BI Dashboard – Interactive executive visualization ✅ Business Recommendations – Persona-specific engagement strategy

🧩 Tech Stack

Python: pandas, numpy, scikit-learn, matplotlib, seaborn, plotly

BI Tool: Microsoft Power BI

Data Source: Online retail transactional dataset (Excel/CSV)

🧠 Key Learning

Translating machine learning output into business-ready personas

Integrating Python analytics → Power BI storytelling

Designing dashboards aligned with executive decision-making