Understanding sales trends and customer purchasing behavior is essential for optimizing business performance and maximizing revenue. This analysis explores key sales metrics, customer demographics, and product performance to uncover valuable insights. The study aims to highlight opportunities for growth and strategic decision-making by identifying top-selling items, high-revenue brands, and customer preferences based on gender and age. The findings from this analysis will support data-driven decision-making to enhance overall sales performance.
-
Identify the Top 5 best-selling items based on sales volume.
-
Determine the Top 3 brands contributing the highest revenue.
-
Analyze the day of the week with the highest sales volume.
-
Customer Analysis
-
Examine the gender-wise and age-wise distribution of customers.
-
Identify the most popular product category among different genders and age groups.
-
Advanced Analysis
-
Calculate the Average Basket Value per customer and identify purchasing patterns.
-
Identify customer segments that generate the highest revenue (e.g., age group, gender, or category preference).
The dataset consists of the following columns:
-
TRANSDATE: Date of the transaction.
-
RECEIPTID: Unique identifier for each transaction.
-
ITEMNAME: Name of the purchased item.
-
DEPARTMENT, CATEGORY, FINELINE: Hierarchical categorization of items.
-
BRAND: Brand name of the item.
-
QTY: Quantity purchased.
-
NETAMOUNTINCLTAX: Total transaction amount, including tax.
-
PRICE: Price per unit.
-
CUSTOMERACC: Unique identifier for customers.
-
GENDER: Gender of the customer.
-
AGE: Age of the customer.
This analysis will help businesses understand customer behavior, identify top-performing products, and optimize sales strategies for improved revenue growth. The insights generated can support marketing campaigns, inventory management, and pricing decisions.
-
Python: Data cleaning, analysis, and visualization.
-
Pandas & NumPy: Data manipulation.
-
Matplotlib & Seaborn: Data visualization.