Comprehensive Retail Transaction Analysis:
Data-Driven Consumer Behavior Investigation
Commercial Intelligence Research Division
July 31, 2025
1 Comprehensive Retail Transaction Dataset
The foundational dataset encompasses ten discrete commercial exchanges, providing substantial empirical evidence for consumer behavioral
pattern analysis. Each transactional record contains nine distinctive variables encompassing demographic characteristics, merchandise
specifications, financial metrics, and temporal indicators.
Table 1: Complete Retail Transaction Dataset
excelblue Transaction Customer Gender Product Quantity Unit Price Total Spend Payment Purchase
ID Age Category (£) (£) Method Date
excellightblue 1001 25 Male Electronics 1 299.99 299.99 Credit Card 01-06-2024
1002 34 Female Clothing 2 49.99 99.98 Debit Card 02-06-2024
excellightblue 1003 40 Male Home & Kitchen 1 79.99 79.99 PayPal 03-06-2024
1004 22 Female Electronics 1 199.99 199.99 Credit Card 04-06-2024
excellightblue 1005 29 Male Grocery 5 10.00 50.00 Cash 05-06-2024
1006 35 Female Clothing 3 39.99 119.97 Debit Card 06-06-2024
excellightblue 1007 28 Male Home & Kitchen 2 49.99 99.98 PayPal 07-06-2024
1008 45 Female Electronics 1 399.99 399.99 Credit Card 08-06-2024
excellightblue 1009 50 Male Grocery 10 5.00 50.00 Cash 09-06-2024
1010 38 Female Clothing 1 69.99 69.99 Debit Card 10-06-2024
The demographic composition demonstrates equitable gender representation throughout the consumer base, encompassing purchasers
of both masculine and feminine classifications. Chronological demographics range from 22 to 50 years, signifying a heterogeneous adult
clientele portfolio. The calculated arithmetic mean age of 34.6 years indicates targeting methodologies should concentrate on established
professionals possessing substantial disposable income capabilities.
2 Merchandise Classification Assessment and Performance Metrics
Product category performance necessitates sophisticated analytical methodologies to comprehend market dynamics and consumer incli-
nations. The deployment of frequency examination, central tendency computations, and variance calculations delivers comprehensive
understanding of category-specific behavioral patterns.
Table 2: Product Category Frequency and Expenditure Analysis
excelblue Product Category Transaction Average Average Unit Total Category
Frequency Quantity Price (£) Revenue (£)
excellightblue Electronics 3 1.00 299.99 899.97
Clothing 3 2.00 53.32 289.94
excellightblue Home & Kitchen 2 1.50 64.99 179.97
Grocery 2 7.50 7.50 100.00
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Transaction Frequency
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Product Categories
Figure 1: Product Category Transaction Frequency Distribution
The modal category examination identifies Electronics and Clothing as equally predominant in transactional frequency, each consti-
tuting 30% of aggregate transactions. Nevertheless, revenue contribution varies substantially, with Electronics generating 60% elevated
average transaction values relative to Clothing acquisitions.
3 Payment Mechanism Preferences and Channel Enhancement
Contemporary retail environments require comprehensive comprehension of payment inclinations to optimize checkout procedures and
minimize transactional resistance. The distribution of payment mechanisms reflects evolving consumer perspectives toward digital trans-
actions and cash alternatives.
Table 3: Payment Method Distribution and Customer Demographics
excelblue Payment Method Frequency Percentage Average Customer Average Transaction
Age Value (£)
excellightblue Credit Card 3 30% 30.67 266.66
Debit Card 3 30% 35.67 96.65
excellightblue PayPal 2 20% 34.00 89.99
Cash 2 20% 39.50 50.00
Figure 2: Payment Method Distribution
Payment method selection demonstrates correlation with transaction values, whereby Credit Card utilization aligns with higher-value
Electronics purchases, while Cash transactions concentrate in lower-value Grocery categories.
4 Visual Representation of Critical Data Patterns
Data visualization functions as the foundation for transforming complex statistical relationships into actionable business intelligence.
Multiple graphical representations illuminate distinct aspects of consumer behavior and transactional patterns.
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Male
Female
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Average Quantity
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Product Categories
Figure 3: Quantity Distribution by Gender and Product Category
Electronics
Product Categories
Clothing
Home & Kitchen
Grocery
0 100 200 300 400 500 600 700 800 900 1,000
Total Revenue (£)
Figure 4: Total Revenue Contribution by Product Category
The comprehensive analytical framework demonstrates distinct purchasing behaviors across demographic segments, with masculine
consumers exhibiting preference for bulk grocery acquisitions and home improvement products, while feminine consumers demonstrate
inclination toward clothing and electronics categories. These insights provide strategic foundations for targeted marketing initiatives and
inventory optimization strategies.
