浙江省杭州市九洲大药房百井坊巷店会员价值度分析数据

通过分析九洲大药房百井坊巷店的会员消费金额、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百井坊巷店的会员、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, purchase frequency, consumption cycle, and days since last consumption of members at Jiuzhou Pharmacy (Baijingfang Lane Store), and determining member value levels using the RFM model, this dataset can be applied not only to store member classification management, personalized precision marketing, and the formulation of service optimization strategies, but also to enterprises or institutions with needs for analyzing consumer behavior patterns and consumption capacity in the pharmaceutical industry, such as 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
Data was collected on members, consumption time, days since last consumption, purchase frequency, and consumption amount at Jiuzhou Pharmacy (Baijingfang Lane Store) in Hangzhou, Zhejiang Province.

2. Data Processing
Based on the collected data, consumption metrics of members at this store within the one-year statistical cycle were analyzed, including total consumption expenditure (cumulative total spending of members in the statistical cycle), purchase frequency (member purchase frequency), consumption cycle (time interval between member consumption to judge their activity level), and days since last consumption. The RFM (Recency, Frequency, Monetary) model was used to classify member values into 8 categories:
- Important Retained: High R value, High F value, High M value;
- Important Value: Low R value, High F value, High M value;
- Important Development: High R value, High F value, Low M value;
- Important Reactivation: Low R value, Low F value, High M value;
- General Important: Low R value, High F value, Low M value;
- General Customers: High R value, Low F value, High M value;
- General Reactivation: High R value, Low F value, Low M value;
- Non-valuable: Low R value, Low F value, Low M value.
Specifically, F value is regarded as high when it exceeds a, M value is regarded as high when it exceeds b, and R value is regarded as high when it exceeds c. The specific values of a, b, and c are trade secrets of the applicant enterprise.