This dataset represents customer purchase behavior and preferences for an e-commerce or retail clothing and footwear platform. Here’s a column-wise breakdown of the dataset:

1. Customer ID (Column A): A unique identifier for each customer (not shown completely but inferred from context).
2. Age (Column B): The customer’s age.
3. Gender (Column C): The gender of the customer (all shown here are “Male”).
4. Item Purchased (Column D): The specific item bought by the customer (e.g., Blouse, Sweater, Jeans).
5. Category (Column E): The broad category of the item (e.g., Clothing, Footwear, Outerwear).
6. Purchase Location (Column F): Numerical code, possibly representing store ID or purchase region.
7. Location (Column G): The U.S. state where the customer is located.
8. Size (Column H): The size of the purchased item (e.g., L, M, S).
9. Color (Column I): The color of the item.
10. Season (Column J): The season associated with the purchase, possibly indicating seasonal demand or use.
11. Review Rating (Column K): Customer rating of the product (out of 5).
12. Subscription Status (Column L): Indicates if the customer has a subscription (all shown are “Yes”).
13. Shipping Type (Column M): Method of shipping (e.g., Express, Free Shipping, Next Day Air).
14. Discount Applied (Column N): Indicates if a discount was applied (“Yes” in all shown).
15. Promo Code Used (Column O): Indicates if a promo code was used (“Yes” in all shown).
16. Previous Purchases (Column P): The number of past purchases made by the customer.
17. Payment Method (Column Q): How the customer paid (e.g., Venmo, PayPal, Credit Card).
18. Frequency of Purchase (Column R): How often the customer makes purchases (e.g., Weekly, Annually, Fortnightly).

Dataset Link: https://github.com/slidescope/data/blob/master/shopping_trends_updated.csv

This type of dataset is valuable for customer segmentation, recommendation systems, personalized marketing, and analyzing purchase behavior trends.

Here are KPIs and visualization ideas for building a customer purchase behavior dashboard in Power BI using this dataset:

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🔑 Key Performance Indicators (KPIs)

1. Total Customers
   • Unique count of customer IDs.
2. Total Sales / Purchases
   • Sum of purchase counts (if monetary value is available, then total revenue).
3. Average Review Rating
   • Average of the “Review Rating” column.
4. Repeat Purchase Rate
   • Percentage of customers with “Previous Purchases” > 1.
5. Subscription Rate
   • % of customers with “Subscription Status” = Yes.
6. Average Purchase Frequency
   • Mode or weighted frequency (Weekly, Monthly, etc.).
7. Top Payment Methods
   • Distribution of payment methods used.
8. Promo Code Utilization Rate
   • % of purchases where “Promo Code Used” = Yes.
9. Discount Usage Rate
   • % of transactions with a discount applied.
10. Top Performing Categories
    • Category-wise purchase counts or revenue.
11. Most Popular Shipping Type
    • Count or % of each shipping type used.
12. Purchase Season Trend
    • Purchases broken down by season (Winter, Summer, etc.).

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📊 Visual Ideas

1. Customer Demographics Panel

• Pie Chart: Gender Distribution
• Histogram: Age Distribution
• Map: Purchase distribution by state (Location)

2. Sales & Reviews Overview

• KPI Cards: Total Sales, Avg. Review Rating, Subscription Rate
• Line Chart: Review Rating trends over time or age

3. Category Performance

• Stacked Bar Chart: Items Purchased by Category
• Treemap: Category vs. Item Purchased counts

4. Customer Behavior

• Bar Chart: Frequency of Purchases by Payment Method
• Funnel Chart: Subscription → Promo Use → Discount Use → Repeat Purchases

5. Shipping & Seasonal Trends

• Donut Chart: Shipping Type breakdown
• Stacked Column Chart: Purchase counts by Season and Category

6. Promo & Discount Analytics

• Gauge: Promo Code Usage %
• Clustered Column Chart: Promo Code Used vs Not Used by Payment Method

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🧠 Advanced Ideas

• Churn Prediction (using Previous Purchases and Frequency)
• Customer Segmentation (age, purchase pattern, subscription status)