浙江省杭州市九洲大药房草庄景墅店会员价值度分析数据

通过分析九洲大药房草庄景墅店的会员消费金额、消费次数、消费周期以及距最近一次消费天数，根据RFM模型确定会员价值度，不仅可用于门店会员分类管理、个性化精准营销以及服务优化策略制定；也可用于针对与医药行业相关、对消费者行为模式与消费能力分析等场景下有需求的企业或机构，例如同类医药电商与零售企业、健康管理与咨询服务机构、医药研发与生产企业、金融机构与保险公司等。1.数据来源 采集了浙江省杭州市九洲大药房草庄景墅店的会员、消费时间、距最近一次消费天数、消费频次、消费金额等数据。 2.数据处理 基于采集数据，分析该店会员在统计周期（一年）内的消费金额（累计会员在统计周期内的总支出）、消费次数（统计会员的购买频率）、消费周期（分析会员消费的时间间隔，判断其活跃程度）以及距最近一次消费天数，运用RFM（最近消费Recency, 消费频率Frequency, 消费金额Monetary）模型对会员进行了价值分级，具体分为： 重要保持：R值高、F值高、M值高； 重要价值：R值低、F值高、M值高； 重要发展：R值高、F值高、M值低； 重要挽留：R值低、F值低、M值高； 一般重要：R值低、F值高、M值低； 一般客户：R值高、F值低、M值高； 一般挽留：R值高、F值低、M值低； 无价值：R值低、F值低、M值低。 其中，F值大于a为高，M值大于b时为高，R值大于c为高，a、b、c的具体数值为申请人企业机密。By analyzing the member consumption amount, consumption frequency, consumption cycle, and days since last consumption of the members of Jiuzhou Pharmacy Caozhuangjingshu Store, and determining the member value level based on the RFM model, this dataset can be used not only for store member classification management, personalized precise marketing, and service optimization strategy formulation, but also for enterprises or institutions with needs in scenarios related to the pharmaceutical industry, such as consumer behavior pattern and consumption ability analysis, including similar pharmaceutical e-commerce and retail enterprises, health management and consulting service institutions, pharmaceutical research and development and production enterprises, financial institutions and insurance companies, etc. 1. Data Source The dataset collects data including member information, consumption time, days since last consumption, consumption frequency, and consumption amount of the members of Jiuzhou Pharmacy Caozhuangjingshu Store in Hangzhou, Zhejiang Province. 2. Data Processing Based on the collected data, the consumption amount (total expenditure of members within the one-year statistical cycle), consumption frequency (member purchase frequency), consumption cycle (time interval of member consumption to judge their activity level), and days since last consumption of the store's members are analyzed within the one-year statistical cycle. The RFM (Recency, Frequency, Monetary) model is used to classify member value into the following 8 levels: - Important to Retain: High Recency, High Frequency, High Monetary; - High-value Members: Low Recency, High Frequency, High Monetary; - Potential Development Customers: High Recency, High Frequency, Low Monetary; - Urgent to Retain: Low Recency, Low Frequency, High Monetary; - Generally Important Customers: Low Recency, High Frequency, Low Monetary; - Average Customers: High Recency, Low Frequency, High Monetary; - General Retention Customers: High Recency, Low Frequency, Low Monetary; - No Value Customers: Low Recency, Low Frequency, Low Monetary. Among them, a frequency value greater than a is regarded as high, a monetary value greater than b is regarded as high, and a recency value greater than c is regarded as high. The specific values of a, b, and c are trade secrets of the applicant enterprise.

搜集汇总

数据集介绍

特点

该数据集包含浙江省杭州市九洲大药房草庄景墅店的会员消费数据，通过RFM模型分析会员价值度，适用于会员分类管理和精准营销等场景。数据规模为94186条，更新频次为按需更新。