金华市婺城区电动自行车充电用户分层数据

通过对用户使用行为数据进行分析，定位用户级别，帮助企业更好地理解和服务用户，提升用户体验和业务成效。通过用户分层，可以识别出可能流失的高风险用户群体，企业可采取预防措施，如提供个性化关怀或优惠，以减少用户流失。对于低活跃用户，企业可采取相应的激活措施，如发送个性化的优惠信息或提醒，以提高用户的活跃度和留存率。用户分层助力丰富电动自行车充电行业用户画像，帮助行业更好地了解用户，为用户提供个性化的商品和服务。1、数据采集：通过内部系统收集2024年10月至12月婺城区电动自行车充电站点用户相关数据，对数据进行清洗、去除极限值等操作。 2、数据处理：用LOOKUP函数获取用户最近一次订单创建时间，用数据获取时间减去最近一次订单创建时间得到用户最近一次订单距今时间（天）R、利用COUNTIFS函数获取用户消费频次F、利用SUMIFS函数获取用户消费金额（元）M。用AVERAGE分别求R、F、M平均值，用IF函数判断用户的R、F、M与R、F、M平均值的大小关系并进行赋分操作得到R、F、M评分（单用户R≥R平均值，赋-1分反之赋1分。单用户F≥F平均值，赋1分反之赋-1分。单用户M≥M平均值，赋1分反之赋-1分），将用户R、F、M评分相加得到总分。 3、用户分层：用户总分取值空间为[-3，3]，根据用户总分对用户进行分层，当用户总分大于1用户评为A，用户总分小于-1评为C，其他评为B。

By analyzing user behavioral data, this dataset enables user tiering to help enterprises better understand and serve users, thereby improving user experience and business outcomes. User tiering allows enterprises to identify high-risk user groups at risk of churn, and take preventive measures such as personalized care or preferential offers to reduce user churn. For low-activity users, enterprises can implement corresponding activation measures, such as sending personalized preferential information or reminders, to enhance user activity and retention rates. Additionally, user tiering helps enrich user personas in the electric bicycle charging industry, enabling the industry to better understand users and provide personalized products and services.
1. Data Collection: Collect user-related data from electric bicycle charging stations in Wucheng District from October to December 2024 via internal systems, and perform data cleaning operations such as removing outliers.
2. Data Processing: Use the LOOKUP function to retrieve the creation time of the user's most recent order; calculate the number of days R since the user's most recent order by subtracting the most recent order creation time from the data acquisition time. Use the COUNTIFS function to obtain the user's consumption frequency F, and use the SUMIFS function to obtain the user's total consumption amount M (in yuan). Calculate the average values of R, F, and M respectively using the AVERAGE function. Use the IF function to compare the user's R, F, and M with their respective average values and assign scores to obtain R, F, and M ratings: assign -1 point if the user's R ≥ the average R, otherwise assign 1 point; assign 1 point if the user's F ≥ the average F, otherwise assign -1 point; assign 1 point if the user's M ≥ the average M, otherwise assign -1 point. Sum the R, F, and M scores to obtain the total score.
3. User Tiering: The total score range for users is [-3, 3]. Users are tiered based on their total scores: users with a total score greater than 1 are classified as Tier A, users with a total score less than -1 are classified as Tier C, and the remaining users are classified as Tier B.

搜集汇总

背景与挑战

背景概述

该数据集包含金华市婺城区1971条电动自行车充电用户的分层数据，涵盖订单、消费行为等多维度信息，通过R（最近消费时间）、F（频次）、M（金额）评分实现用户分级（A/B/C），用于优化用户服务和流失预警。