瑞安数字生活平台烘焙食品行业用户消费行为分析数据

通过对数字生活平台上烘焙食品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 bakery food industry on digital lifestyle platforms, brands can identify high-value users, provide differentiated services and marketing strategies, and enhance user engagement and loyalty. Combining the RFM model with other user data enables more refined customer segmentation and targeted marketing. Moreover, 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 sales activities via the digital lifestyle platform.
2. Data Processing: Deduplicate, classify, merge and accumulate the collected data.
3. Algorithm Rules: Calculate the recency (R), frequency (F) and monetary (M) metrics of users, and segment users based on these three dimensions to determine user value categories.
- For the R dimension: Calculate the recency interval R using the analysis date and the user's most recent purchase date. Divide users into two intervals based on the median of R values: Interval 1 for R ≤ median, Interval 0 for R > median.
- For the F dimension: Calculate the cumulative consumption frequency F based on the user's cumulative paid orders and refunded orders. Divide users into two intervals based on the median of F values: Interval 1 for F ≥ median, Interval 0 for F < median.
- For the M dimension: Divide the cumulative consumption amount M of users into two intervals based on the median of M values: Interval 1 for M ≥ median, Interval 0 for M < median.
Based on the intervals of the three dimensions, all users are divided into 8 user types, namely: Important Value Users (111), Important Win-Back Users (011), Important Cultivation Users (101), Important Recovery Users (001), Potential Users (110), New Users (100), General Maintenance Users (010), and Churned Users (000). Refined customer segmentation and targeted marketing can be implemented based on these user value classifications.
Note: The median refers to the value that splits a dataset into two equal parts.