美妆行业客户消费行为分析数据
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此数据是通过一定规模美妆行业消费客户的消费行为分析，为行业消费水平和趋势提供决策支持。此数据可以帮助企业：1、客群价值分析。分析不同人群的消费能力差异。2. 营销策略优化。低频次高单价客户设计组合促销（提升频次）。高频次低单价客群推荐高附加值商品（提升客单价）。根据新老客消费差异制定差异化营销方案。3. 会员体系评估。验证会员特权是否有效提升消费金额和频次。4. 运营健康度监控。笔单价下降、频次上升可能源于营销过度。老客频次下滑需预警流失风险。5. 行业对标分析。对比行业均值判断自身客户消费水平。细分市场增长机会。6.数据应用：分析客群价值，优化营销策略，评估会员体系，监控运营健康，对标行业水平。 数据采集： 通过数云自研CRM系统采集全渠道交易数据、会员数据并进行加工。获取数据完整进行加工，单位为元。 数据加工： 1. 组成行业的用户样本筛选：用户最大根类目与行业一致;销售金额占比超过全店70%；用户最近12个月有连续的规模交易数据。 2. 订单新老客打标：根据历史全量交易数据计算每个客户的首次购买时间。若客户首次购买时间在统计时间段之前，则该笔订单标记为【老客订单】，否则为【新客订单】。 3.订单会员打标：根据【会员用户关系表】，用订单上的用户和客户信息关联会员状态。关联出客户会员状态为有效，则该笔订单标记为【会员订单】，否则为【非会员订单】。 4. 环比增长=（R12指标-R13_24指标）/R13_24指标 5. R12指标：指最近12个月的指标数据；R13_24指标：指往前13-24个月的指标数据。 6. 新老客户数指标根据客户ID去重计数。 7. 交易数：订单合并，统计客户购买次数 8. 人均购买金额=订单金额 / 客户数 9. 人均购买频次=交易数 / 客户数 10. 平均客单价=订单金额 / 交易数
This dataset is generated from consumption behavior analysis of a large-scale cohort of beauty industry consumers, providing decision-making support for the industry's consumption levels and development trends. It assists enterprises in the following aspects: 1. Customer group value analysis: Analyze differences in consumption capabilities across different consumer groups. 2. Marketing strategy optimization: Design combined promotions for low-frequency, high-unit-price customers to increase purchase frequency; recommend high-value-added products to high-frequency, low-unit-price customers to raise average order value; develop differentiated marketing plans based on consumption differences between new and returning customers. 3. Member system evaluation: Verify whether member privileges effectively boost total consumption amount and purchase frequency. 4. Operational health monitoring: A decline in average order value paired with an increase in purchase frequency may stem from excessive marketing; a decline in purchase frequency among returning customers warrants early warning of customer churn risk. 5. Industry benchmarking analysis: Compare with industry averages to assess one's own customer consumption level, and identify growth opportunities in segmented markets. 6. Data applications: Analyze customer group value, optimize marketing strategies, evaluate member systems, monitor operational health, and benchmark against industry levels. Data Collection: Full-channel transaction data and member data are collected and processed via Shuyun's self-developed CRM system. All collected data is fully processed, with the currency unit denominated in Chinese Yuan (CNY). Data Processing: 1. Industry user sample screening: Select users whose core top-level product category aligns with the industry's scope; whose sales amount accounts for more than 70% of the total store's sales; and who possess continuous valid transaction data in the most recent 12 months. 2. New/Returning customer order labeling: Calculate each customer's first purchase timestamp based on their full historical transaction data. If a customer's first purchase occurred before the statistical period, the order is labeled as [Returning Customer Order]; otherwise, it is labeled as [New Customer Order]. 3. Member order labeling: Associate the order's user and customer information with member status using the [Member User Relationship Table]. If the associated member status is valid, the order is labeled as [Member Order]; otherwise, it is labeled as [Non-Member Order]. 4. Month-over-Month Growth = (R12 Indicator - R13_24 Indicator) / R13_24 Indicator 5. R12 Indicator: Refers to indicator data from the most recent 12 months; R13_24 Indicator: Refers to indicator data from the 13th to 24th months prior to the statistical period. 6. New and Returning Customer Count: Count distinct customer IDs. 7. Transaction Count: Merge duplicate orders and count the total number of customer purchases. 8. Per Capita Purchase Amount = Total Order Amount / Number of Customers 9. Per Capita Purchase Frequency = Transaction Count / Number of Customers 10. Average Order Value (AOV) = Total Order Amount / Transaction Count
