瑞安数字生活平台百货零售行业用户消费行为分析数据

通过对数字生活平台上百货零售行业的用户进行分层，品牌可以识别高价值用户，提供差异化的服务和营销策略，提高用户粘性和忠诚度。RFM模型可与其他用户数据相结合，实现更精细化的客户细分和精准营销。此外，通过分析用户RFM模型，品牌可以预测用户生命周期价值，优化用户留存策略。1. 数据采集：通过数字生活平台的销售活动，采集销售过程中交易日期，用户id，订单编号，订单金额等数据； 2. 数据处理：对数据进行去重、分类、合并、累加； 3. 算法规则：计算用户最近消费间隔(R)、累计消费频次(F)和累计消费金额(M)，通过这三个维度来划分用户，确定用户价值分类。对于R维度，根据分析日期与用户最近购买日期来计算最近消费间隔R，基于用户R值中值划分为2个区间：小于等于中值为1区间，大于中值为0区间；对于F维度，根据用户累计的支付订单量与退款订单量计算累计消费频次F，基于用户F值中值划分为2个区间：大于等于中值为1区间，小于中值为0区间；对于M维度，根据用户在累计的消费金额M与用户M值中值划分两个区间，大于等于中值为1区间，小于中值为0区间。基于三个维度的区间，将所有用户划分为8种用户类型，即重要价值用户(111)、重要唤回用户(011)、重要培养用户(101)、重要挽回用户(001)、潜力用户(110)、新用户(100)、一般维持用户(010)、流失用户(000)，基于这用户价值分类实现精细化的客户细分和精准营销。（注：中值即中位数，将数据集合划分为两部分）

By segmenting users in the general merchandise retail industry on digital life platforms, brands can identify high-value users, provide differentiated services and marketing strategies, and improve user stickiness and loyalty. The RFM model can be combined with other user data to achieve more refined customer segmentation and targeted marketing. Additionally, by analyzing the user RFM model, brands can predict user lifetime value and optimize user retention strategies.
1. Data Collection: Collect data such as transaction date, user ID, order number, order amount and other relevant information during the sales process through the sales activities of the digital life platform.
2. Data Processing: Perform deduplication, classification, merging and accumulation on the collected data.
3. Algorithm Rules: Calculate the recency (R), cumulative consumption frequency (F) and cumulative consumption amount (M) of users, and segment users based on these three dimensions to determine user value categories.
For the R dimension: Calculate the recency interval R based on the analysis date and the user's most recent purchase date. Divide users into two intervals according to the median of the user's R values: the interval with R ≤ median is assigned a value of 1, and the interval with R > median is assigned a value of 0.
For the F dimension: Calculate the cumulative consumption frequency F based on the user's cumulative paid order volume and refunded order volume. Divide users into two intervals according to the median of the user's F values: the interval with F ≥ median is assigned a value of 1, and the interval with F < median is assigned a value of 0.
For the M dimension: Divide users into two intervals based on the user's cumulative consumption amount M and the median of the user's M values: the interval with M ≥ median is assigned a value of 1, and the interval with M < median is assigned a value of 0.
Based on the interval values of the three dimensions, all users are divided into 8 user types: High-Value Important Users (111), Re-engaging Important Users (011), Cultivatable Important Users (101), Win-back Important Users (001), Potential Users (110), New Users (100), General Maintenance Users (010), and Churned Users (000). Refined customer segmentation and targeted marketing can be realized based on this user value classification.
Note: The median refers to the value that divides a data set into two equal parts.

搜集汇总

数据集介绍

特点

该数据集由瑞安市数据管理发展有限公司提供，包含3200条百货零售行业用户的消费行为数据，每年更新一次。数据通过RFM模型对用户进行分类，帮助品牌识别高价值用户并优化营销策略。