安徽省抖音平台食品类客户分级评价数据
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收集归纳抖音销售记录表中安徽省地区客户的数据，通过客户在2025年1月1日距离2025年5月30日间隔内距离5月30日的最近一次消费时间天数（R）、客户在2025年1月1日至2025年5月30日之间的最近一段时间消费频次(F)和的最近一段时间消费金额(M)， 采用 RFM 模型对客户进行价值评级，实现精准化运营，通过对地区客户价值管理，满足不同价值客户的个性化需求。对A级客户，每个月进行一次回访维护，对B级客户，每个季度进行一次回访维护，对C级客户每半年进行一次回访维护，对D级客户每年进行一次回访维护。另外可以为本地区客户群体高度重叠企业提供不同价值类型的客户个性化服务的数据支持。数据处理：1、对从销售记录表中采集到的数据进行脱敏、降噪、清洗、聚集、分析。2、数据加工：运用RFM模型结合客户在2025年1月1日距离2025年5月30日间隔间隔内距离5月30日的最近一次消费时间天数（R）、客户在2025年1月1日至2025年5月30日之间的最近一段时间消费频次(F)和客户的最近一段时间消费金额(M)的得分排名对客户进行一个综合排名，最终得出一个RFM总评分。a.提取出最近一次消费时间距离2025年5月30日的天数（R）、客户在2025年1月1日至2025年5月30日之间消费频次(F)和消费金额(M)进行分类，最近一次消费时间间隔最短的客户排在最上面。按照从1-5评分，前20%的客户获得5分，接下来的20%用户获得4分,再下来20%的客户为3分，再下来20% 的客户为2分，最后20% 的客户为1分。 b.根据客户在最近一段时间消费频次(F)从高到底依次对用户进行分类，前20%的客户在用户活动频率的分数为5，以此类推。 c, 根据客户在最近一段时间消费金额(M)，前20%的客户在消费金额的分数为5，以此类推。消费金额最少的20%客户则分数为1。 RFM得分=0.3*（R得分）+0.3*（F得分）+0.4*（M得分） 评分大于等于4分的为A级客户，大于等于3小于4的为B级客户，大于等于2小于3的为C 级客户，低于2的为D 级客户。
This dataset is compiled by collecting and aggregating customer data from the Anhui Province region in the Douyin sales record sheet. Three core metrics are calculated for each customer: Recency (R), which refers to the number of days between the customer's most recent purchase date within the observation window (January 1, 2025 to May 30, 2025) and May 30, 2025; Frequency (F), which is the total number of purchases made by the customer during the observation window; and Monetary (M), which is the total consumption amount of the customer during the observation window. The RFM model is adopted to grade customer value, enabling precise operational management and meeting the personalized needs of customers across different value tiers through regional customer value management. For Class A customers, follow-up maintenance visits are conducted once per month; for Class B customers, once per quarter; for Class C customers, once every six months; and for Class D customers, once per year. Additionally, this dataset can provide data support for enterprises with highly overlapping customer groups in this region to deliver personalized services tailored to different customer value types. The data processing workflow is as follows: 1. Preprocessing of collected data: The data extracted from the sales record sheet is subjected to desensitization, noise reduction, data cleaning, aggregation and preliminary analysis. 2. Quantitative scoring and ranking: The RFM model is applied to generate a comprehensive customer ranking based on the score rankings of R, F and M, and finally calculate the overall RFM score. a. Recency scoring: Extract the number of days between the customer's most recent purchase date and May 30, 2025 (R), purchase frequency (F) and total consumption amount (M) during the observation window for classification. Customers with the shortest interval between their most recent purchase and May 30, 2025 are ranked highest. Scores are assigned on a 1-5 scale: the top 20% of customers receive 5 points, the next 20% receive 4 points, the following 20% receive 3 points, the next 20% receive 2 points, and the bottom 20% receive 1 point. b. Frequency scoring: Classify customers in descending order of their purchase frequency (F) during the observation window. The top 20% of customers are assigned a score of 5 for their activity frequency, with the remaining groups following the same scoring rule. c. Monetary scoring: Classify customers based on their total consumption amount (M) during the observation window. The top 20% of customers receive a score of 5 for their consumption amount, with the bottom 20% of customers with the lowest consumption amount receiving 1 point, and the remaining groups following the same rule. The overall RFM score is calculated using the formula: RFM Score = 0.3 * (R Score) + 0.3 * (F Score) + 0.4 * (M Score). Customer tiers are defined as follows: Class A customers with a score >= 4; Class B customers with 3 <= score < 4; Class C customers with 2 <= score < 3; and Class D customers with a score < 2.
