宁波区域共享单车用户分级数据

通过数据的处理计算，可以对该地区用户实行分级管理，帮助企业动态调整运营策略，提升企业服务水平，优化资源分配。本行业均可采用该方法对用户进行分级管理，提高用户忠诚度和满意度，有助于推行绿色低碳出行，助力中国早日实现“碳中和”“碳达峰”。1、数据采集：用户ID，用户消费、使用等信息。 2、数据处理：对采集到的原始数据进行处理，去除缺失和异常数据。3、数据分析：基于熵权法的AHP模型，目标层为用户价值，指标层将用户价值指标体系分为以下四大价值维度。1）最近使用时间T={≤3天：10分，≤7天：8分，≤15天：6分，≤30天：4分，≤60天：2分，其余：0分}；2）使用频次P={≥60次/月：10分，≥45次/月：8分，≥30次/月：6分，≥20次/月：4分，≥10次/月：2分，其余：0分}；3）使用忠诚度D={≥12个月：10分，≥10个月：8分，≥8个月：6分，≥6个月：4分，≥2个月：2分，其余：0分}；4）消费等级K={≥1000元:10分，≥800元:8分，≥500元:6分，≥300元:4分，≥100元:2分，其余：0分}。Q=T+P+D+K，用户类型=IFS（Q≥36，“4星用户”，Q≥30，“3星用户”，Q≥20，“2星用户”，Q＞10，“1星用户”，Q≤10，“新用户”）4、数据应用：核心用户需要重点关注，采用定期发放优惠券或打折等方式进行维护，采取多重优惠吸引新用户。

Through data processing and calculation, tiered management can be implemented for users in this region, enabling enterprises to dynamically adjust operational strategies, improve service levels, and optimize resource allocation. This method can be applied to user tiered management across the entire industry, enhancing user loyalty and satisfaction, promoting green and low-carbon travel, and supporting China in achieving the goals of "peak carbon dioxide emissions" and "carbon neutrality" at an early date.
1. Data Collection: User ID, and information related to user consumption and usage.
2. Data Processing: Process the collected raw data by removing missing and abnormal data entries.
3. Data Analysis: Adopt the Analytic Hierarchy Process (AHP) model based on the Entropy Weight Method, with the target layer being user value. The indicator layer divides the user value indicator system into the following four value dimensions:
1) Recency (T): {≤3 days: 10 points, ≤7 days: 8 points, ≤15 days: 6 points, ≤30 days: 4 points, ≤60 days: 2 points, others: 0 points}
2) Usage Frequency (P): {≥60 times/month: 10 points, ≥45 times/month: 8 points, ≥30 times/month: 6 points, ≥20 times/month: 4 points, ≥10 times/month: 2 points, others: 0 points}
3) Usage Loyalty (D): {≥12 months: 10 points, ≥10 months: 8 points, ≥8 months: 6 points, ≥6 months: 4 points, ≥2 months: 2 points, others: 0 points}
4) Consumption Level (K): {≥1000 yuan: 10 points, ≥800 yuan: 8 points, ≥500 yuan: 6 points, ≥300 yuan: 4 points, ≥100 yuan: 2 points, others: 0 points}
Q = T + P + D + K, and user type is categorized using the IFS function: IFS(Q≥36, "4-star user", Q≥30, "3-star user", Q≥20, "2-star user", Q>10, "1-star user", Q≤10, "new user")
4. Data Application: Core users require focused attention, and maintenance measures such as regularly issuing coupons or providing discounts should be adopted. Multiple preferential policies should be implemented to attract new users.

搜集汇总

数据集介绍

特点

该数据集包含宁波地区共享单车用户的分级信息，通过用户的使用行为和消费数据，采用AHP模型对用户进行价值评估和分级，旨在帮助企业提升服务水平和用户满意度，推动绿色低碳出行。