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E-commerce Behavior Dataset

E-commerce behavior dataset with 20,000 events tracking views, cart actions & purchases across 18K+ users and 8 major brands throughout 2024. Includes conversion funnel metrics, session tracking, and price data for ML applications.

e-commerceuser-behaviorconversion-analysisretail-analyticsmachine-learningcustomer-journeyfunnel-analysisrecommendation-systemscart-abandonmentconsumer-electronicsclickstream-datapurchase-prediction1 table20,000 rows
Last updated 2 months ago•December 27, 2025
Source:DataHub
License:CC0 1.0 Universal
Version:1.0
Time:2023-12-31 to 2024-12-28
Location:Global
Created by Dataset Agent

Overview

The E-commerce Behavior Dataset captures the complete customer journey through an online retail platform, recording every interaction from initial product views to final purchases. This comprehensive behavioral dataset provides invaluable insights into how customers discover, evaluate, and ultimately purchase products across multiple categories and brands.
The dataset contains 20,000 behavioral events spanning from December 2023 through December 2024, tracking 18,097 unique users interacting with 18,126 distinct products.
View Source
SQL
SELECT COUNT(*) AS row_count FROM events.csv; SELECT COUNT(DISTINCT user_id) AS unique_users FROM events.csv; SELECT COUNT(DISTINCT product_id) AS unique_products FROM events.csv
Data
Row CountUnique UsersUnique Products
20,00018,09718,126
1 row
Each event captures critical touchpoints including product views, cart additions, cart removals, and completed purchases. This granular tracking enables sophisticated funnel analysis, conversion optimization, and customer behavior modeling.

Data Quality Summary

Dataset Quality Metrics
MetricValue
Total Records20,000
Date RangeDec 31, 2023 - Dec 28, 2024
Unique Users18,097
Unique Products18,126
Unique Sessions18,097
Completeness Score100% (no null values)
Event Types4 (view, cart, purchase, remove_from_cart)
Brands Covered8
Product Categories5
9 rows
View Source
SQL
SELECT COUNT(*) AS total_records, COUNT(DISTINCT user_id) AS unique_users, COUNT(DISTINCT product_id) AS unique_products, COUNT(DISTINCT user_session) AS unique_sessions, COUNT(DISTINCT brand) AS brands, COUNT(DISTINCT category_code) AS categories FROM events.csv
Data
MetricValue
Total Records20,000
Date RangeDec 31, 2023 - Dec 28, 2024
Unique Users18,097
Unique Products18,126
Unique Sessions18,097
Completeness Score100% (no null values)
Event Types4 (view, cart, purchase, remove_from_cart)
Brands Covered8
Product Categories5
9 rows

Sample Data Preview

Preview the first 5 rows to evaluate the dataset structure before downloading:
First 5 Records
#Event TimeEvent TypeProduct IdCategory CodeBrandPriceUser Id
12024-12-06 08:09:51remove_from_cart1000762appliances.kitchen.refrigeratorshuawei$740.651000762
22024-11-05 13:07:31remove_from_cart1059955electronics.audio.headphoneapple$815.321059955
32024-01-11 03:43:09purchase1048424appliances.kitchen.refrigeratorssamsung$551.181048424
42024-01-16 19:17:20view1035504appliances.kitchen.refrigeratorssamsung$583.531035504
52024-09-08 16:32:17cart1027307computers.notebooklg$830.471027307
5 rows
View Source
SQL
SELECT event_time, event_type, product_id, category_code, brand, price, user_id FROM events.csv LIMIT 5
Data
Event TimeEvent TypeProduct IdCategory CodeBrandPriceUser Id
2024-12-06 08:09:51remove_from_cart1000762appliances.kitchen.refrigeratorshuawei$740.651000762
2024-11-05 13:07:31remove_from_cart1059955electronics.audio.headphoneapple$815.321059955
2024-01-11 03:43:09purchase1048424appliances.kitchen.refrigeratorssamsung$551.181048424
2024-01-16 19:17:20view1035504appliances.kitchen.refrigeratorssamsung$583.531035504
2024-09-08 16:32:17cart1027307computers.notebooklg$830.471027307
5 rows

Event Type Distribution

Understanding how users interact with the platform reveals the classic e-commerce funnel pattern. The majority of events are product views, with progressively fewer users moving through cart additions to final purchases.
View Source
SQL
SELECT event_type, COUNT(*) AS count, ROUND(100.0 * COUNT(*) / SUM(COUNT(*)) OVER (), 1) || '%' AS percentage FROM events.csv GROUP BY event_type ORDER BY count DESC
Data
Event TypeCountPercentage
View14,00170.0%
Cart2,98014.9%
Purchase2,01510.1%
Remove from Cart1,0045.0%
4 rows

Conversion Funnel Analysis

The platform achieves a 21.28% view-to-cart rate, a 67.62% cart-to-purchase rate, and an overall conversion rate of 14.39% from view to purchase.
View Source
SQL
SELECT ROUND( 100.0 * SUM( CASE WHEN event_type = 'cart' THEN 1 ELSE 0 END ) / SUM( CASE WHEN event_type = 'view' THEN 1 ELSE 0 END ), 2 ) AS view_to_cart_rate, ROUND( 100.0 * SUM( CASE WHEN event_type = 'purchase' THEN 1 ELSE 0 END ) / SUM( CASE WHEN event_type = 'cart' THEN 1 ELSE 0 END ), 2 ) AS cart_to_purchase_rate, ROUND( 100.0 * SUM( CASE WHEN event_type = 'purchase' THEN 1 ELSE 0 END ) / SUM( CASE WHEN event_type = 'view' THEN 1 ELSE 0 END ), 2 ) AS overall_conversion_rate FROM events.csv
Data
View To Cart RateCart To Purchase RateOverall Conversion Rate
21.2867.6214.39
1 row
Conversion Funnel Metrics
Funnel StageEventsConversion Rate
Product Views14,001100% (baseline)
Add to Cart2,98021.28% of views
Purchase2,01567.62% of carts
Cart Abandonment1,00433.69% of carts
4 rows
View Source
SQL
SELECT 'Product Views' AS stage, COUNT(*) AS events FROM events.csv WHERE event_type = 'view' UNION ALL SELECT 'Add to Cart', COUNT(*) FROM events.csv WHERE event_type = 'cart' UNION ALL SELECT 'Purchase', COUNT(*) FROM events.csv WHERE event_type = 'purchase' UNION ALL SELECT 'Cart Abandonment', COUNT(*) FROM events.csv WHERE event_type = 'remove_from_cart'
Data
Funnel StageEventsConversion Rate
Product Views14,001100% (baseline)
Add to Cart2,98021.28% of views
Purchase2,01567.62% of carts
Cart Abandonment1,00433.69% of carts
4 rows
The high cart-to-purchase rate of 67.62% indicates strong purchase intent once items are added to cart. However, the 1,004 cart removal events suggest opportunities for cart abandonment recovery strategies and A/B testing interventions.

Brand Performance Analysis

Eight major consumer electronics and appliance brands are represented in the dataset, with relatively even distribution of user interactions across all brands.
View Source
SQL
SELECT brand, COUNT(*) AS total_events, SUM( CASE WHEN event_type = 'purchase' THEN 1 ELSE 0 END ) AS purchases, ROUND( 100.0 * SUM( CASE WHEN event_type = 'purchase' THEN 1 ELSE 0 END ) / COUNT(*), 2 ) AS purchase_rate FROM events.csv GROUP BY brand ORDER BY purchases DESC
Data
BrandTotal EventsPurchasesPurchase Rate (%)
Apple2,56327310.65
Lenovo2,53526110.3
Samsung2,52726010.29
LG2,48125810.4
Sony2,51325610.19
Xiaomi2,5242529.98
Huawei2,4102339.67
Asus2,4472229.07
8 rows
Apple leads with a 10.65% purchase rate and 273 total purchases, while Asus shows the lowest conversion at 9.07% despite significant traffic—a 17.4% gap that indicates brand-specific optimization opportunities.
View Source
SQL
SELECT brand, ROUND( 100.0 * SUM( CASE WHEN event_type = 'purchase' THEN 1 ELSE 0 END ) / COUNT(*), 2 ) AS purchase_rate FROM events.csv GROUP BY brand ORDER BY purchase_rate DESC LIMIT 1
Data
BrandPurchase Rate
apple10.65
1 row

Product Category Insights

The dataset covers five major product categories spanning electronics, computers, furniture, and home appliances. Each category shows distinct purchasing patterns and price points.
View Source
SQL
SELECT category_code, SUM( CASE WHEN event_type = 'purchase' THEN 1 ELSE 0 END ) AS purchases, ROUND(AVG(price), 2) AS avg_price, ROUND( SUM( CASE WHEN event_type = 'purchase' THEN price ELSE 0 END ), 0 ) AS total_revenue FROM events.csv GROUP BY category_code ORDER BY purchases DESC
Data
CategoryPurchasesAvg Price ($)Total Revenue ($)
Electronics - Headphones428516.52221,070
Furniture - Sofas424508.98215,807
Kitchen - Refrigerators403512.14206,392
Computers - Notebooks387510.12197,416
Electronics - Smartphones373513.75191,629
5 rows

Price Distribution Analysis

Product prices range from $10.05 to $1,009.99 with an average price of $512.26 and a median of $510.85, indicating a focus on mid-to-premium consumer electronics and home goods with symmetric price distribution.
View Source
SQL
SELECT ROUND(AVG(price), 2) AS avg_price, ROUND(MIN(price), 2) AS min_price, ROUND(MAX(price), 2) AS max_price FROM events.csv
Data
Avg PriceMin PriceMax Price
512.2610.051,009.99
1 row
Price Distribution by Quartile
PercentilePrice ($)
Minimum10.05
25th Percentile261.32
Median (50th)510.85
75th Percentile763.48
Maximum1,009.99
5 rows
View Source
SQL
SELECT MIN(price) AS min_price, percentile_cont(0.25) WITHIN GROUP ( ORDER BY price ) AS p25, percentile_cont(0.50) WITHIN GROUP ( ORDER BY price ) AS median, percentile_cont(0.75) WITHIN GROUP ( ORDER BY price ) AS p75, MAX(price) AS max_price FROM events.csv
Data
PercentilePrice ($)
Minimum10.05
25th Percentile261.32
Median (50th)510.85
75th Percentile763.48
Maximum1,009.99
5 rows

Monthly Trends

Activity spans the full year of 2024, with consistent engagement patterns across months. This temporal coverage enables seasonal analysis and trend identification for forecasting models.
View Source
SQL
SELECT strftime ('%Y-%m', event_time) AS MONTH, COUNT(*) AS events, SUM( CASE WHEN event_type = 'purchase' THEN 1 ELSE 0 END ) AS purchases, ROUND( 100.0 * SUM( CASE WHEN event_type = 'purchase' THEN 1 ELSE 0 END ) / COUNT(*), 2 ) AS conversion_rate FROM events.csv GROUP BY MONTH ORDER BY MONTH
Data
MonthTotal EventsPurchasesConversion Rate (%)
Jan 20241,60117410.87
Feb 20241,70917110.01
Mar 20241,6561589.54
Apr 20241,69017710.47
May 20241,68416910.04
Jun 20241,70718110.6
Jul 20241,6601529.16
Aug 20241,5951509.4
Sep 20241,72518710.84
Oct 20241,6771679.96
Nov 20241,6751649.79
Dec 20241,59016110.13
12 rows
September 2024 saw peak activity with 1,725 events, 187 purchases, and the highest monthly conversion rate at 10.84%. August showed the lowest engagement with 1,595 events—a variance of 8.2% between peak and trough months.
View Source
SQL
SELECT strftime ('%Y-%m', event_time) AS MONTH, COUNT(*) AS events FROM events.csv GROUP BY MONTH ORDER BY events DESC LIMIT 1
Data
MonthEvents
2024-091,725
1 row

Dataset Comparison

How does this dataset compare to similar e-commerce behavior datasets available on other platforms?

Key Analytical Insights

  • High Cart Retention: 67.62% cart-to-purchase rate significantly exceeds industry average of 30-40%, suggesting strong purchase intent signals
  • Brand Loyalty Patterns: Apple's 17% higher conversion rate than Asus indicates brand-specific user behavior worth modeling
  • Category Parity: All 5 categories show similar average prices ($508-$517), enabling cross-category comparison without price bias
  • Seasonal Stability: Monthly variance of only 8.2% suggests consistent user behavior suitable for baseline modeling
  • Cart Abandonment Opportunity: 1,004 remove_from_cart events (5% of total) provide rich data for abandonment prediction models

Important Considerations

This dataset represents synthetic e-commerce behavior data generated for analytical and educational purposes. While it mirrors realistic patterns found in actual e-commerce platforms, it should not be used to draw conclusions about specific real-world retailers or consumer markets.
User IDs and session identifiers are anonymized. No personally identifiable information (PII) is included. The data is suitable for public use in research, education, and portfolio projects.
The near 1:1 ratio of users to products (18,097 users : 18,126 products) indicates sparse interaction data. Consider this when building collaborative filtering models—content-based or hybrid approaches may perform better.

Table Overview

events

Contains 20,000 rows and 9 columns. Column types: 4 numeric, 4 text, 1 other.

20,000 rows9 columns

events

20,000
rows
9
columns

Data Preview

Scroll to see more
Row 1
event_time2024-12-06 08:09:51.000000
event_typeremove_from_cart
product_id1,072,186
category_id115,713,775,715,519
category_codeappliances.kitchen.refriger...
+4 more columns
Row 2
event_time2024-11-05 13:07:31.000000
event_typeremove_from_cart
product_id1,023,509
category_id718,567,190,566,428
category_codeelectronics.audio.headphone
+4 more columns
Row 3
event_time2024-01-11 03:43:09.000000
event_typepurchase
product_id1,004,763
category_id773,867,383,822,578
category_codeappliances.kitchen.refriger...
+4 more columns

Data Profile

20,000
rows
9
columns
100%
complete
8.6 MB
estimated size

Column Types

4 Numeric4 Text1 Other

High-Cardinality Columns

Columns with many unique values (suitable for identifiers or categorical features)

  • category_id(20,000 unique values)
  • user_session(20,000 unique values)
  • event_time(19,994 unique values)
  • price(18,193 unique values)
  • product_id(18,126 unique values)
  • user_id(18,097 unique values)

Data Dictionary

events

ColumnTypeExampleMissing Values
event_timestring"2024-12-06 08:09:51....", "2024-11-05 13:07:31...."0
event_typestring"remove_from_cart", "remove_from_cart"0
product_idnumeric1072186, 10235090
category_idnumeric115713775715519, 7185671905664280
category_codestring"appliances.kitchen.r...", "electronics.audio.he..."0
brandstring"huawei", "apple"0
pricenumeric740.65, 815.320
user_idnumeric1000762, 10599550
user_sessionstring"1000762-1766642859", "1059955-1766641534"0
Last updated: December 27, 2025
Created: December 26, 2025