湖北省抖音平台厨房清洁剂类客户分级评价数据

分析湖北省抖音平台上经营店铺的厨房清洁剂类产品客户消费行为数据。我们采用根据厨房清洁剂使用特征优化的改进RFMD模型，综合考虑客户最近一次购买时间（R）、90天内购买频次（F）、90天内购买总金额（M）以及90天内购买厨房清洁剂种类数量（D）四个维度，对客户进行价值评级。通过店铺的相关数据，我们能够更精准地识别不同价值层次的客户，制定个性化的运营策略，满足不同客户群的需求，并为抖音平台在湖北省的精准化运营提供数据支持，同时为同行业企业提供有价值的客户洞察。1、数据处理：对采集到的湖北省抖音平台厨房清洁剂类产品客户消费数据进行降噪、清洗、脱敏、聚合和初步分析，确保数据的准确性和安全性。2、数据处理：采用RFMD模型，结合客户的最近一次购买时间（R）、90天内购买频次（F）、90天内购买总金额（M）和90天内购买厨房清洁剂种类数量（D）四个维度进行综合评分。RFMD综合得分计算：RFMD得分 = R得分 * 0.3 + F得分 * 0.2 + M得分 * 0.2 + D得分 * 0.3。 3、数据应用：根据RFMD综合得分，将客户分为以下五个等级： A级客户：得分 ≥ 4.5 B级客户：3.5 ≤ 得分 < 4.5 C级客户：2.5 ≤ 得分 < 3.5 D级客户：1.5 ≤ 得分 < 2.5 E级客户：得分 < 1.5This dataset analyzes customer consumption behavior data for kitchen cleaner products from businesses operating on the Douyin platform in Hubei Province. We employ an improved RFMD model optimized based on the usage characteristics of kitchen cleaner products, and conduct customer value rating by comprehensively considering four dimensions: recency of the customer's last purchase (R), purchase frequency within 90 days (F), total purchase amount within 90 days (M), and the number of kitchen cleaner product types purchased within 90 days (D). With the store-related data, we can accurately identify customer groups at different value tiers, formulate personalized operation strategies to meet the demands of various customer segments, provide data support for the precise operation of the Douyin platform in Hubei Province, and offer valuable customer insights for enterprises in the same industry. 1. Data Processing: Perform denoising, cleaning, anonymization, aggregation and preliminary analysis on the collected customer consumption data of kitchen cleaner products on the Douyin platform in Hubei Province to ensure the accuracy and security of the data. 2. Data Processing: Use the RFMD model to calculate comprehensive scores based on the four dimensions: recency of last purchase (R), purchase frequency within 90 days (F), total purchase amount within 90 days (M) and number of kitchen cleaner product types purchased within 90 days (D). The calculation formula of RFMD comprehensive score is: RFMD Score = R Score * 0.3 + F Score * 0.2 + M Score * 0.2 + D Score * 0.3. 3. Data Application: Divide customers into five tiers based on their RFMD comprehensive scores: - Tier A customers: Score ≥ 4.5 - Tier B customers: 3.5 ≤ Score < 4.5 - Tier C customers: 2.5 ≤ Score < 3.5 - Tier D customers: 1.5 ≤ Score < 2.5 - Tier E customers: Score < 1.5