浙江省杭州市九洲大药房湖墅南路店会员价值度分析数据

通过分析九洲大药房湖墅南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湖墅南路店的会员、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。

By analyzing the consumption amount, consumption frequency, consumption cycle, and days since last consumption of members at the Hushu South Road Store of Jiuzhou Pharmacy, and determining member value levels based on the RFM model, this dataset can be applied not only to member classification management, personalized precision marketing, and formulation of service optimization strategies for the store, but also to enterprises and institutions with demand for consumer behavior pattern and consumption capacity analysis in scenarios related to the pharmaceutical industry, such as 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
The dataset collects data including member information, consumption time, days since last consumption, consumption frequency, and consumption amount from the Hushu South Road Store of Jiuzhou Pharmacy in Hangzhou, Zhejiang Province.

2. Data Processing
Based on the collected data, we analyzed the consumption amount (total expenditure of members within the one-year statistical cycle), consumption frequency (purchase frequency of members), consumption cycle (time interval between member consumptions to judge their activity level), and days since last consumption of the store's members within the statistical cycle. The RFM (Recency, Frequency, Monetary) model was used to classify members into value tiers, which are specifically categorized as follows:
- Important Sustained: High R, High F, High M
- Important Valuable: Low R, High F, High M
- Important Developmental: High R, High F, Low M
- High-Priority Retention Targets: Low R, Low F, High M
- General Important: Low R, High F, Low M
- General Casual Customers: High R, Low F, High M
- General Retention Candidates: High R, Low F, Low M
- Non-Valuable Customers: Low R, Low F, Low M

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

搜集汇总

数据集介绍

特点

浙江省杭州市九洲大药房湖墅南路店会员价值度分析数据，包含114218条会员消费记录，通过RFM模型分析会员的消费金额、频次和周期，用于会员分类管理和精准营销。