E-Commerce Sales Performance Analysis

Comprehensive analysis of 17,049 customer transactions across 8+ product categories. Uncover hidden patterns and optimize your sales strategy with data-driven insights.

17,049
Transactions
8+
Categories
Kaggle
Dataset
Key Performance Indicators
Top Category
Electronics
Revenue Leader
48.13%
Best Delivery
6.3 days
Highest Rating
3.93/5
Returning %
88.21%
Avg Discount
5.14%

Methodology & Data Flow

Step-by-step process of our data analysis pipeline

1
Data Loading & Validation
Loaded the dataset (17,049 transactions) and verified main columns: Age, Gender, Category, Customer Type, Delivery Time, Rating, Discount, Total Amount, Quantity. Ensured data types were correct and consistent.
2
Data Cleaning & Preparation
Fixed data types, handled missing values, and created clear age groups (18–24, 25–34, 35–44, 45–54, 55–64, 65–75) for demographic analysis.
3
Risk Analysis
Grouped data by product category, calculated average delivery time and average rating per category. Plotted delivery vs rating to identify risk zones (slow delivery + low ratings).
4
Revenue Analysis
Grouped by customer type (returning/new) and category, summed total revenue to compare which customer group brings more revenue per category.
5
Demographic Analysis
Analyzed purchase volume by gender and discount effectiveness. Compared discount amount vs total spending using scatter plots to identify correlations.
6
Age Group Analysis
Grouped by age group + category, calculated total revenue, then converted to revenue share (%) within each age group. Produced heatmaps and identified top categories per age group.
7
Performance Scoring
Created category performance scores (Revenue, Rating, Delivery), computed overall score as average of the three, and ranked categories for strategic insights.

Analytical Insights

Key findings from comprehensive data analysis

Delivery Time vs Customer Rating Risk Analysis

Categories in bottom-right quadrant indicate operational risk

Risk Analysis: Delivery vs Rating

This analysis identifies operational risks by plotting average delivery time against average customer rating for each product category. Categories in the bottom-right quadrant (long delivery times + low ratings) require immediate attention.

Key Findings:

  • Food and Toys categories show slower delivery times and lower ratings
  • Electronics maintain optimal balance of delivery time and ratings
  • Bottom-right quadrant indicates highest operational risk
Revenue by Returning vs New Customers

Returning customers dominate revenue across categories

Revenue Analysis: Returning vs New Customers

Comparison of revenue generated by returning versus new customers across product categories reveals the critical importance of customer retention for sustainable growth.

Key Insights:

  • Returning customers generate more revenue than new customers
  • Electronics and Home & Garden show highest retention value
  • Customer retention is stronger revenue driver than acquisition
Purchase Volume by Gender

Balanced purchase volume across genders with category variations

Purchase Volume by Gender

Analysis of total units purchased across genders reveals relatively balanced purchase volumes with some category-specific variations that can inform targeted marketing strategies.

Key Findings:

  • Overall purchase volume balanced across male and female customers
  • Beauty and Fashion show slightly higher female engagement
  • Sports and Electronics show slightly higher male engagement
Discount Effectiveness Analysis

Positive correlation with wide dispersion in spending

Discount Effectiveness Analysis

Scatter plot analysis of discount amount versus total spending reveals the relationship between discount strategies and customer purchasing behavior across transactions.

Key Insights:

  • Positive upward trend: higher discounts correlate with higher spending
  • Wide dispersion indicates discounts aren't the only spending driver
  • Optimal discount range identified for maximum basket size increase
Revenue Share Heatmap

Electronics dominate revenue across all age groups

Age Group Revenue Share Analysis

Heatmap visualization of revenue distribution by age group and product category reveals how purchasing preferences and spending patterns evolve across different demographic segments.

Key Findings:

  • Electronics consistently rank #1 across all age groups
  • Revenue share increases with age for Electronics category
  • Younger groups show more diversified spending across categories
Age Group Category Preferences

Preferences shift from broad interests to focused purchases with age

Age Group Category Preferences

Analysis of top 3 preferred categories by age group shows how consumer interests evolve through different life stages, providing valuable insights for targeted marketing and inventory planning.

Key Insights:

  • Younger groups (18-34) prefer Books, Sports, and Beauty products
  • Middle age groups (35-54) show balanced preferences
  • Older groups (55+) concentrate on Electronics and Home essentials

Performance Metrics

Real data from comprehensive analysis

Revenue Share by Age Group & Category

Top categories per age group (scroll for more)

Age Group Product Category Revenue Share (%) Orders Trend

Showing top 5 rows per age group. Scroll within the table to view more data.

Category Performance Scores

Composite scores combining revenue, rating, and delivery performance

Rank Category Revenue Score Rating Score Delivery Score Overall Score

Overall Score = Average of Revenue, Rating, and Delivery Scores

Age Group Category Preferences

Order distribution and share percentage by age group

Age Group Product Category Orders Share (%) Rank

Showing category preferences by age group based on order distribution