浙江省杭州市九洲大药房孩儿巷店会员价值度分析数据

通过分析九洲大药房孩儿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孩儿巷店的会员、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。

This study analyzes member consumption amount, consumption frequency, consumption cycle, and days since last consumption of Haixiang Store of Jiuzhou Pharmacy in Hangzhou, Zhejiang Province, and determines member value segmentation using the RFM model. The results can not only be applied to member classification management, personalized precise marketing, and service optimization strategy formulation for the store, but also be utilized by enterprises and institutions in the pharmaceutical industry with demands for consumer behavior pattern and consumption capability analysis, including peer pharmaceutical e-commerce and retail enterprises, health management and consulting service institutions, pharmaceutical R&D and manufacturing enterprises, financial institutions and insurance companies, etc.

1. Data Source
Data were collected from members of Haixiang Store of Jiuzhou Pharmacy in Hangzhou, Zhejiang Province, including member information, consumption time, days since last consumption, consumption frequency, consumption amount and other related data.

2. Data Processing
Based on the collected data, the consumption amount (total cumulative expenditure of members within the one-year statistical cycle), consumption times (purchase frequency of members), consumption cycle (time interval between member consumption to judge their activeness), and days since last consumption of members in this store were analyzed. The RFM (Recency, Frequency, Monetary) model was used to classify members into value segments, which are specifically divided as follows:
- Important Retained Customers: High Recency (R), High Frequency (F), High Monetary (M);
- Important Valuable Customers: Low Recency (R), High Frequency (F), High Monetary (M);
- Important Developing Customers: High Recency (R), High Frequency (F), Low Monetary (M);
- Important Win-back Customers: Low Recency (R), Low Frequency (F), High Monetary (M);
- General Important Customers: Low Recency (R), High Frequency (F), Low Monetary (M);
- General Customers: High Recency (R), Low Frequency (F), High Monetary (M);
- General Win-back Customers: High Recency (R), Low Frequency (F), Low Monetary (M);
- Non-valuable Customers: Low Recency (R), Low Frequency (F), Low Monetary (M).

Specifically, a high F value is defined as F > a, a high M value as M > b, and a high R value as R > c. The exact values of a, b, and c are trade secrets of the applicant enterprise.