电商用户数据

抖音电商用户分群与精准营销策略分析
User_ID 每个用户的唯一标识符，便于追踪和分析。
Age 用户的年龄，提供对人口统计偏好的洞察。
Gender 用户的性别，使能性别特定的推荐和定位。
Location 用户所在地区：郊区、农村、城市，影响偏好和购物习惯。
Income 用户的收入水平，表明购买力和支付能力。
Interests 用户的兴趣，如运动、时尚、技术等，指导内容和产品推荐。
Last_Login_Days_Ago 用户上次登录以来的天数，反映参与频率。
Purchase_Frequency 用户进行购买的频率，表明购物习惯和忠诚度。
Average_Order_Value 用户下单的平均价值，对定价和促销策略至关重要。
Total_Spending 用户消费的总金额，表明终身价值和购买行为。
Product_Category_Preference 用户偏好的特定产品类别。
Time_Spent_on_Site_Minutes 用户在电子商务平台上花费的时间，表明参与程度。
Pages_Viewed 用户在访问期间浏览的页面数量，反映浏览活动和兴趣。
Newsletter_Subscription 用户是否订阅了营销活动通知。

Analysis of Douyin E-commerce User Segmentation and Precision Marketing Strategies
User_ID: Unique identifier for each user to facilitate tracking and analysis.
Age: User's age, providing insights into demographic preferences.
Gender: User's gender, enabling gender-specific recommendations and targeting.
Location: User's region (suburban, rural, urban), which influences preferences and shopping habits.
Income: User's income level, indicating purchasing power and payment capacity.
Interests: User interests such as sports, fashion, technology, etc., guiding content and product recommendations.
Last_Login_Days_Ago: Number of days since the user's last login, reflecting engagement frequency.
Purchase_Frequency: User's purchase frequency, indicating shopping habits and loyalty.
Average_Order_Value: Average order value placed by the user, critical for pricing and promotional strategies.
Total_Spending: Total amount spent by the user, indicating lifetime value and purchasing behavior.
Product_Category_Preference: Specific product categories preferred by the user.
Time_Spent_on_Site_Minutes: Total time (in minutes) a user spends on the e-commerce platform, reflecting engagement level.
Pages_Viewed: Number of pages viewed by the user during a visit, reflecting browsing activity and interests.
Newsletter_Subscription: Whether the user has subscribed to marketing campaign notifications.

搜集汇总

背景与挑战

背景概述

该数据集名为“电商用户数据”，包含用户ID、年龄、性别、位置、收入、兴趣、登录天数、购买频率、平均订单价值、总消费、产品类别偏好、网站停留时间和页面浏览等字段，用于抖音电商用户分群与精准营销策略分析。数据以CSV格式提供，大小为74.51KB，覆盖用户人口统计和行为特征，适用于机器学习建模和商业洞察。数据集发布于2025年4月14日，采用GPL 2.0许可证，具有结构化、多维度特点，支持电商平台优化营销策略。