泰国地区面料客户分级评价数据

通过收集和分析泰国地区客户对面料消费相关数据，使用RFM客户价值模型，了解客户对面料的购买力水平和消费偏好对客户进行等级评级，实现精准化运营，通过对客户价值管理，满足不同价值客户的个性化需求。对于A等级客户可每月1至2次与之沟通，对于B等级客户可每季度1至2次与之客户沟通，对于C等级客户可每半年1至2次与之沟通。另外可以为本地区客户群体高度重叠企业提供不同等级客户个性化服务的数据支持。1.数据采集：采集泰国地区客户对面料的相关交易数据。其中，采集数据中“下单时间”为距离统计时间最近的一次订单时间，“订单金额”指的是距离统计时间最近的这次订单金额，“历史购买总次数”“历史购买总金额”指的是历史服务时间段内统计得出的购买次数和购买金额。2.数据处理：对采集到本次订单金额（万元）、历史购买总金额（万元）等数据进行分类、合并、累加，便于分析使用，其中客户编号已进行脱敏转换处理。3.算法加工：R评分：根据用户下单时间距离统计时间的天数（D）划分为5个等级: 0≤D≤10为5分，10＜D≤20为4分，20＜D≤30为3分，30＜D≤50为2分，50<D 为1分；F评分：消费频率评分根据历史购买总次数（S）,划分为5个等级: 0<S≤2为1分，2＜S≤5 为2分，5＜S≤10 为3分，10＜S≤15为4分，15< S为5分；M评分：根据历史购买总金额（Z）,划分为5个等级，0<Z≤20为1分，20＜Z≤30为2分，30＜Z≤40为3分，40＜Z≤50为4分，50<Z为5分；RFM综合评分（X）=0.3*R+0.4*F+0.6*M；会员等级分为ABC三级，0≤X≤3为C级，3＜X≤6为B级，6＜X为A级This dataset is developed by collecting and analyzing fabric consumption-related data of customers in Thailand, and adopting the RFM customer value model to conduct hierarchical grading of customers based on their fabric purchasing power and consumption preferences, so as to achieve precise operation management and meet the personalized demands of customers with different value tiers via customer value management. For customers at Tier A, communicate with them 1 to 2 times per month; for Tier B customers, conduct communication 1 to 2 times per quarter; for Tier C customers, communicate 1 to 2 times every half a year. Additionally, this dataset can provide data support for local enterprises with highly overlapping customer bases to deliver personalized services to customers of different tiers. 1. Data Collection: Collect relevant transaction data of fabric-related purchases from customers in Thailand. Specifically, the "order time" in the collected dataset refers to the timestamp of the most recent order placed relative to the statistical cutoff time; the "order amount" refers to the transaction amount of that most recent order. "Total historical purchase times" and "total historical purchase amount" are the total number of purchases and total purchase amount calculated within the predefined historical service period, respectively. 2. Data Preprocessing: Classify, merge and accumulate the collected data including the current order amount (in ten thousand yuan) and total historical purchase amount (in ten thousand yuan) to facilitate subsequent analysis. Customer IDs have been desensitized and converted. 3. Algorithm Implementation: - R Score: Divide the number of days (D) between the customer's most recent order time and the statistical cutoff time into 5 tiers: 5 points for 0≤D≤10, 4 points for 10<D≤20, 3 points for 20<D≤30, 2 points for 30<D≤50, and 1 point for D>50. - F Score: The consumption frequency score is divided into 5 tiers based on the total historical purchase times (S): 1 point for 0<S≤2, 2 points for 2<S≤5, 3 points for 5<S≤10, 4 points for 10<S≤15, and 5 points for S>15. - M Score: Divide into 5 tiers based on the total historical purchase amount (Z): 1 point for 0<Z≤20 (ten thousand yuan), 2 points for 20<Z≤30 (ten thousand yuan), 3 points for 30<Z≤40 (ten thousand yuan), 4 points for 40<Z≤50 (ten thousand yuan), and 5 points for Z>50 (ten thousand yuan). - RFM Comprehensive Score (X) is calculated as: X = 0.3*R + 0.4*F + 0.6*M. - Customer membership tiers are divided into three levels: Tier C for 0≤X≤3, Tier B for 3<X≤6, and Tier A for X>6.