Customer Behavior & Sales Trends in H&M Ladieswear
H&M
This project analyzes over 5 million H&M transactions to uncover purchasing behavior and sales trends in Ladieswear. Using Python (pandas, seaborn, matplotlib), I cleaned and structured the dataset, explored customer demographics, seasonal sales patterns, best-selling products, and the impact of discounts. Key insights showed that women aged 20–29 are the most active buyers, Dresses and Black-colored items dominate sales, Summer is the peak season, and 54% of purchases are discounted. Based on these findings, I developed marketing recommendations for H&M focused on customer targeting, inventory planning, and discount strategies.
Team
Sheila Géa
Duration
1 week
September 2025
Project Type
Data Analysis · Data Cleaning · Dashboard · Marketing Strategy · Customer Behavior