This project explores shopping trends and invoice data to gain insights into customer behavior and preferences using exploratory data analysis techniques. Visualizations are created using Matplotlib and Plotly Libraries of Python to present key findings and trends within the dataset.
Retail businesses can benefit greatly from understanding their customers' behavior and preferences. Exploratory data analysis (EDA) provides a valuable approach to uncovering insights that can drive business decisions. In this project, we analyze retail data to understand various aspects of customer behavior, such as purchase patterns, popular products, and sales trends.
Two datasets have been used for this analysis. The first one is Shopping Trends which focuses on the frequency of purchases, preferences, product categories, shipping types, and customer demographics. The second dataset contains actual data of customers including purchase amount, products, invoice details, and customer details.
- Visualization of the top-selling products by analyzing the frequency of purchases and revenue generated by them.
- Exploration of customer demographics and segmenting of customers based on various attributes such as age, gender, location, and seasonality of products. Visualized the distribution of customers across different segments.
- Analyze popular products based on sales volume or revenue generated. Identify best-selling products and visualize their performance over time.
- Examined and concluded the similarities and dissimilarities between the trends and true results of sales based on purchasing patterns and preferences.
- Geographically visualized customer distribution and sales performance across different regions or locations and its variation with seasons.
- Observation of the reviews and ratings of different products of customers. Also, judged the effect of discounts and promotional offers on sales.
The exploratory analysis reveals several insights into customer behavior and retail trends:
- Seasonal sales patterns, with peak sales during specific months or holidays.
- Different customer segments with varying purchasing behaviors and preferences.
- Best-selling products and their performance over time.
- Geographic variations in customer distribution and sales.
Exploratory data analysis using Matplotlib graphs provides valuable insights into customer retail analysis. By understanding customer behavior and preferences, businesses can optimize their marketing strategies, product offerings, and overall operations to better serve their customers and improve profitability. Refer to the Jupyter Notebook provided in this repository for clear visualizations.