脏衣篮类客户消费能力分析评价数据

统计分析公司销售平台购买脏衣篮类客户消费记录数据，通过对历史下单客户建立画像，对客户进行标签制定，定位客户消费级别，为精准营销提供必要的客户分类数据，针对不同级别客户有针对性的制定广告营销策略提供数据支持。客户分类的算法规则采用RFM数据模型排序、聚类的方法，对平台上下单脏衣篮的客户进行汇总，通过对客户的消费频次和消费时间间隔、消费总金额的排序、聚类，对客户进行分类。 1.数据来源：采集公司网络平台的销售数据，对数据进行清洗、去除无效数据等操作。 2.数据处理：采用RFM数据模型。通过对客户ID的聚类汇总消费频次F、消费总金额M、最近一次消费时间距离当前天数R，以此为维度对客户进行分类。 3.数据计算：R值得分=（30-R)/30*10，当R大于30天，则计0分；M值得分=M/最高消费总金额*10，最高消费总金额为采集时间段内客户下单总额的最高值；F值得分=F/最高消费频次*10，最高消费频次为采集时间段内客户消费频次的最高值；RFM综合评分=a*R值得分+b*F值得分+c*M值得分，a，b，c为权重系数分别为0.3，0.3，0.4。再根据RFM综合评分对客户进行分类，RFM综合评分≥7，为A类，RFM综合评分≥4，分为B类，RFM综合评分＜4，为C类，对客户进行标签制定，定位客户消费级别，为精准营销提供必要的客户分类数据，针对不同级别客户有针对性的制定广告营销策略提供数据支持。Statistical analysis is conducted on the consumption record data of customers who purchased laundry baskets from the company's sales platform. By building customer profiles for historical purchasers, formulating customer tags, and identifying their consumption tiers, this dataset provides necessary customer classification data for precision marketing, and supports the development of targeted advertising and marketing strategies for customers at different tiers. The customer classification adopts the RFM data model-based sorting and clustering methodology. Customers who placed orders for laundry baskets on the platform are aggregated, and then classified via sorting and clustering based on their consumption frequency, purchase interval, and total consumption amount. 1. Data Source: Sales data is collected from the company's online platform, followed by data cleaning and invalid data removal operations. 2. Data Processing: The RFM data model is employed. We aggregate consumption frequency (F), total consumption amount (M), and the number of days since the most recent purchase (R) by clustering customer IDs, and classify customers using these three dimensions. 3. Data Calculation: The R score = (30 - R)/30 * 10, with a score of 0 assigned when R exceeds 30 days; The M score = M / maximum total consumption amount * 10, where the maximum total consumption amount refers to the highest total order amount of customers within the data collection period; The F score = F / maximum consumption frequency * 10, where the maximum consumption frequency refers to the highest consumption frequency of customers within the data collection period; The comprehensive RFM score = a*R score + b*F score + c*M score, where the weight coefficients a, b, and c are set to 0.3, 0.3, and 0.4 respectively. Customers are categorized into three classes based on their comprehensive RFM scores: Class A for scores ≥7, Class B for scores ≥4, and Class C for scores <4. This process formulates customer tags, identifies their consumption tiers, and provides the necessary customer classification data for precision marketing, as well as data support for developing targeted advertising and marketing strategies for customers at different tiers.

搜集汇总

数据集介绍

特点

该数据集包含1114条脏衣篮类客户的消费记录，通过RFM模型对客户进行分类，支持精准营销策略的制定。数据来源于企业销售平台，适用于批发和零售业的统计分析。