电商用户行为分析

项目GMV的拆解公式为：GMV=UV（独立访客数）* 用户下单转化率 * 客单价，由于数据集不涉及客单价相关的内容，故优化GMV主要考虑提高UV和提升转化率这两个方面。提高UV的方法就是拉新，这需要对我们的用户进行拆解，找出新客户的来源的主要渠道，拉来更多的新用户。提高转化率主要有两方面：一方面是提高转化率高的那群人的占比，这需要找出哪些因素（随机森林模型）影响了用户的转化率，另一方面是找出转化率的环节，看是否有异常，如果有就优化掉。

The breakdown formula for project GMV is: GMV = UV (Unique Visitors) * User Order Conversion Rate * Average Order Value (AOV). Since the dataset does not cover content related to average order value, optimizing GMV primarily focuses on two aspects: increasing UV and improving user conversion rate. The approach to increasing UV is customer acquisition, which requires segmenting our user base to identify the primary channels for new customers, so as to attract more new users. Improving user conversion rate involves two main facets: first, increasing the proportion of users with high conversion rates, which necessitates identifying the factors that impact user conversion rates via the Random Forest model; second, identifying each stage within the conversion workflow to check for anomalies and optimize them if any exist.

搜集汇总

背景与挑战

背景概述

该数据集专注于电商用户行为分析，旨在通过提高独立访客数和用户下单转化率来优化GMV。数据集包含用户基本信息（如年龄、性别、设备）和页面浏览行为（如浏览主页、产品详情页等）等关键字段，并已进行数据清洗、规范化等预处理，适用于用户行为建模和转化率分析任务。