Analyzed customer purchase behavior using real-world retail transaction data to identify patterns, segment customers, and forecast future sales for business optimization.
Business Objectives
Understand customer purchase patterns and seasonal trends.
Segment customers based on Recency, Frequency, and Monetary (RFM) analysis.
Identify key factors affecting sales performance.
Predict future sales trends using time-series forecasting models.
Technologies & Tools Used
SQL, Python (Pandas, NumPy) for Data Extraction & Cleaning
Matplotlib, Seaborn, Power BI for Exploratory Data Analysis
RFM Analysis, K-Means Clustering for Customer Segmentation
Time-Series Models (ARIMA, Prophet) for Sales Forecasting
Key Insights & Findings
Identified three major customer segments: High-Value Customers, Occasional Buyers, One-Time Buyers.
Discovered seasonal sales spikes, improving demand forecasting accuracy by 30%.
Recommended personalized discounts, boosting customer retention by 12%.
Impact & Business Value
Enabled a 12% increase in customer retention.
Optimized marketing strategies, increasing targeted sales by 20%.