电商用户行为分析数据集

该数据集是一个模拟的中文电商用户行为数据集，共包含1000条交易记录。

主要内容：

用户信息：包含用户ID、随机生成的中文姓名、性别、年龄及分布在全国各地的城市信息。
商品信息：涵盖电子产品、服装、家居等7大类商品，每类包含具体的商品名称及单价。
交易细节：包括购买时间（2024年全年）、购买数量及自动计算的消费总金额。
适用场景： 此数据集结构清晰，非常适合用于电商数据分析练习，例如用户画像构建、地域消费习惯分析、热销商品统计以及RFM模型（最近一次消费、消费频率、消费金额）的实战演练。

我已经将其保存为 6个主流电商平台的模拟交易数据集，每个数据集均包含1000条2024年度的中文交易记录。数据集涵盖天猫（高端B2C，价格高5%）、淘宝（综合C2C，标准价格）、京东（品质自营，价格高2%）、拼多多（社交拼团，价格低15%）、苏宁易购（线上线下融合）和1688（批发采购平台）六大平台。每个数据集包含12个核心字段，覆盖用户维度（ID、姓名、性别、年龄、城市）、商品维度（ID、名称、类别、单价）和交易维度（购买时间、数量、金额）。数据覆盖电子产品、服装、家居等7大类目，用户分布于全国12个主要城市，年龄段18-65岁。所有数据集采用统一的CSV格式（UTF-8 BOM编码），可直接用于Excel、Python、Tableau等工具进行跨平台对比分析、用户画像构建、RFM模型分析、销售趋势预测等电商数据分析实战演练。

文件在您的工作区中。

This is a simulated Chinese e-commerce user behavior dataset containing a total of 1,000 transaction records.

Key Contents:
User Information: Includes user ID, randomly generated Chinese names, gender, age, and city information distributed across various cities nationwide.
Product Information: Covers 7 major product categories including electronics, apparel, home goods, etc., with specific product names and unit prices for each category.
Transaction Details: Includes purchase time (full year of 2024), purchase quantity, and automatically calculated total consumption amount.

Application Scenarios: With a clear structure, this dataset is highly suitable for e-commerce data analysis exercises, such as user portrait construction, regional consumption habit analysis, best-selling product statistics, and practical exercises for the Recency, Frequency, Monetary (RFM) model (last consumption time, consumption frequency, consumption amount).

I have saved it as simulated transaction datasets for 6 mainstream e-commerce platforms, each containing 1,000 Chinese transaction records from the year 2024. The datasets cover six major platforms: Tmall (high-end B2C, 5% higher prices), Taobao (comprehensive C2C, standard prices), JD.com (quality self-operated, 2% higher prices), Pinduoduo (social group buying, 15% lower prices), Suning.com (online-offline integration), and 1688 (wholesale procurement platform).

Each dataset includes 12 core fields, covering user dimensions (ID, name, gender, age, city), product dimensions (ID, name, category, unit price), and transaction dimensions (purchase time, quantity, amount). The data covers 7 major product categories including electronics, apparel, home goods, etc., with users distributed across 12 major cities nationwide and aged between 18 and 65 years old.

All datasets adopt a unified CSV format (UTF-8 BOM encoding) and can be directly used for e-commerce data analysis practical exercises such as cross-platform comparative analysis, user portrait construction, RFM model analysis, and sales trend prediction with tools like Excel, Python, Tableau, etc.

The files are available in your workspace.

搜集汇总

背景与挑战

背景概述

该数据集是一个模拟的中文电商用户行为数据集，包含6个主流电商平台（如天猫、淘宝、京东等）的模拟交易记录，每个平台有1000条数据，覆盖2024年全年。数据集包含12个核心字段，涵盖用户、商品和交易三个维度，适用于用户画像构建、RFM模型分析和跨平台对比等电商数据分析场景。