共享单车用户分析管理数据

分析用户骑行的次数，骑行时间，支付的金额，客户购买套餐情况（数量，金额），根据相应的权重计算出用户的活跃度得分，对会员进行分类。方便公司后期为用户提供更合适的套餐由中控系统采集用户的骑行数据和套餐数据，对采集到的原始数据进行处理，通过去除重复和有缺失的数据、采用统计学模型排序、聚类的方法，对用户骑行的次数，骑行时间，支付的金额，客户购买套餐情况（数量，金额）等数据进行统计，基于骑行次数a，骑行时间b，支付金额c，套数购买次数d，额度e五个维度对用户活跃度进行评价，用户活跃度得分=a*k1+b*k2+c*k3+d*k4+e*k5，k为权重，采用主观赋权法，通过对权重调整，调优用户的分类结果。由利用IFS函数对用户活跃度得分对用户进行分类，用户类型=IFS(活跃度得分>=80, "高活跃用户",用户活跃度得分>=60, "中活跃度用户",用户活跃度得分<60, "低活跃用户")。

This dataset aims to analyze users' ride count, riding duration, payment amount, and customers' package purchase status (quantity and total amount), calculate user activity scores based on corresponding weights, and classify members, so as to help the company provide more suitable packages for users in the later stage. First, the central control system collects user riding data and package purchase data. Then, the collected raw data is preprocessed: duplicate and missing data are removed, and statistical model-based sorting and clustering methods are adopted to count the target data including ride count, riding duration, payment amount, and package purchase status (quantity and total amount) of users. User activity is evaluated based on five dimensions: ride count (a), riding duration (b), payment amount (c), number of package purchases (d), and quota (e). The user activity score is calculated as: Activity Score = a*k1 + b*k2 + c*k3 + d*k4 + e*k5, where k represents the weight. The subjective weighting method is adopted, and the weights are adjusted to optimize the user classification results. Finally, users are classified based on their activity scores using the IFS function, with the user type determined by the following formula: User Type = IFS(Activity Score >= 80, "Highly Active User", Activity Score >= 60, "Moderately Active User", Activity Score < 60, "Low Active User")

搜集汇总

数据集介绍

特点

该数据集包含共享单车用户的骑行和支付数据，用于分析用户行为和活跃度，支持企业优化用户套餐和服务。数据每月更新，通过统计学模型和聚类方法进行用户分类。