浙江省杭州市九洲大药房艮山西路店会员价值度分析数据

通过分析九洲大药房艮山西路店的会员消费金额、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艮山西路店的会员、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。

This dataset analyzes the consumption amount, consumption frequency, consumption cycle, and days since last consumption of members at Jiuzhou Pharmacy's Genshan West Road Store, and applies the RFM model to determine member value tiers. The dataset can be utilized for store member classification management, personalized precision marketing, and service optimization strategy formulation; it can also support enterprises or institutions with demands for consumer behavior pattern and consumption capacity analysis in the pharmaceutical industry, including peer pharmaceutical e-commerce and retail enterprises, health management and consulting service institutions, pharmaceutical research and development and production enterprises, financial institutions and insurance companies, among others.

1. Data Source
Data covering member information, consumption time, days since last consumption, purchase frequency, and total consumption amount were collected from Jiuzhou Pharmacy's Genshan West Road Store in Hangzhou, Zhejiang Province.

2. Data Processing
Based on the collected data, the following metrics of members within the one-year statistical cycle were analyzed: total consumption amount (cumulative total expenditure over the cycle), consumption frequency (members' purchase frequency), consumption cycle (time interval between purchases to assess member activeness), and days since last consumption. The RFM (Recency, Frequency, Monetary) model was employed to classify members into 8 value tiers, as detailed below:
- Important Retained: High Recency (R), High Frequency (F), High Monetary (M)
- Important Valuable: Low Recency (R), High Frequency (F), High Monetary (M)
- Important Developing: High Recency (R), High Frequency (F), Low Monetary (M)
- Important to Win Back: Low Recency (R), Low Frequency (F), High Monetary (M)
- Generally Important: Low Recency (R), High Frequency (F), Low Monetary (M)
- General Customers: High Recency (R), Low Frequency (F), High Monetary (M)
- General Retained: High Recency (R), Low Frequency (F), Low Monetary (M)
- Non-valuable: Low Recency (R), Low Frequency (F), Low Monetary (M)

Here, "high" for Frequency (F) is defined as F > a, "high" for Monetary (M) as M > b, and "high" for Recency (R) as R > c. The specific values of a, b, and c are trade secrets of the applicant's enterprise.