That sounds like a great dataset to start exploring data analysis with! Here are some cool things you can do to find interesting insights:
1. Top Sellers and Revenue Generators:
- Identify best-selling products: Find the products with the highest total sales amount or quantity sold. This can reveal what your customers like and what generates the most revenue.
- Customer segmentation by spending: Group customers based on their total spending. This can help you identify high-value customers and tailor marketing strategies accordingly.
- Least profitable products: Uncover products with low sales margins or high discounts. This can help you decide if these products need improvement or removal.
2. Trends and Seasonality:
- Daily/Weekly/Monthly Trends: Analyze sales data over time to identify trends. Look for spikes or dips in sales that might be related to holidays, promotions, or seasonal changes.
- Compare year-over-year sales: See how your sales are performing compared to the same period last year. This can highlight growth or decline and areas needing attention.
3. Customer Behavior Analysis:
- Customer purchase frequency: See how often customers typically make purchases. This can help with planning marketing campaigns and loyalty programs.
- Product bundling analysis: Find out if customers tend to buy certain products together. This can inform product recommendations and promotions.
- Customer churn rate: Calculate the percentage of customers who stop buying. Analyze their purchase history to understand why they might be churning.
This can be useful for you : Data Visualization @ Julias.ai