瑞安数字生活平台生鲜水产行业用户消费行为分析数据

通过对数字生活平台上生鲜水产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 fresh seafood industry on digital lifestyle platforms, brands can identify high-value users, deliver differentiated services and marketing strategies, and enhance user stickiness and loyalty. The RFM model can be combined with other user data to enable more refined customer segmentation and precision marketing. Furthermore, by analyzing the user RFM model, brands can predict customer lifetime value and optimize user retention strategies.
1. Data Collection: Collect data such as transaction dates, user IDs, order numbers, and order amounts during the sales process through the sales activities on digital lifestyle platforms;
2. Data Processing: Perform deduplication, classification, merging, and accumulation on the collected data;
3. Algorithm Rules: Calculate the user's recency (R), cumulative consumption frequency (F), and cumulative consumption amount (M), and segment users and determine user value categories based on these three dimensions. For the R dimension: Calculate the recency R based on the analysis date and the user's most recent purchase date, and divide users into two intervals based on the median of R values: the interval ≤ median is assigned 1, and the interval > median is assigned 0; For the F dimension: Calculate the cumulative consumption frequency F based on the user's cumulative paid order volume and refunded order volume, and divide users into two intervals based on the median of F values: the interval ≥ median is assigned 1, and the interval < median is assigned 0; For the M dimension: Divide users into two intervals based on the user's cumulative consumption amount M and the median of M values: the interval ≥ median is assigned 1, and the interval < median is assigned 0. Based on the intervals of the three dimensions, all users are divided into 8 user types, namely Important High-Value Users (111), Important Win-Back Users (011), Important Nurturing Users (101), Important Reclamation Users (001), Potential Users (110), New Users (100), General Maintenance Users (010), and Churned Users (000). Refined customer segmentation and precision marketing can be realized based on these user value classifications.
(Note: The median refers to the value that divides a dataset into two equal parts.)

搜集汇总

数据集介绍

特点

该数据集包含734条生鲜水产行业用户消费行为数据，通过RFM模型对用户进行分类，支持精准营销和用户分层。数据每年更新一次，适用于识别高价值用户和优化营销策略。