安徽地区定制鞋垫消费者分析数据

本数据可用于鞋类企业在安徽地区开展定制鞋垫业务过程中制定针对安徽地区客户的特色营销策略时，能够针对不同登记的定制客户推行不同的营销策略，以提升销售转化率和实现精准化营销。精准化营销的关键在于深入了解不同客户群体的需求、购买行为及偏好。通过对安徽地区定制鞋垫客户的价值管理，满足不同价值客户的个性化需求，并为行业内准备在安徽地区开展鞋垫定制业务的鞋企提供参考。安徽地区消费者分类算法规则采用RFM数据模型排序、聚类的方法，通过对客户最近一次活动R（天数）和活动频率F（次数）的聚类，以及对消费金额M（总额）的排序，实现对安徽地区客户按等级进行分类。 1.数据来源：采集自本企业在安徽地区的定制鞋垫销售订单数据。 2.数据处理：对采集到的数据进行降噪、清洗、脱敏、聚集、分析。 3.模型选择：运用RFM模型 提取出客户最近一次活动R（天数）、活动频率F（次数）、消费金额M（总额）进行聚类，并按最近一次活动（R）进行排序。 数据加工：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。 根据客户活动频率（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。 消费金额（M），前20%的客户在消费金额的分数为5，以此类推。RFM综合评分=（R）得分*0.3+（F）得分*0.3+（M）得分*0.4，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 supports footwear enterprises conducting custom insole businesses in Anhui Province to formulate targeted marketing strategies for local customers, enabling differentiated marketing for different registered custom clients to enhance sales conversion rates and achieve precise marketing. The core of precise marketing lies in deeply understanding the needs, purchasing behaviors and preferences of different customer groups. Through value management of custom insole customers in Anhui Province, this dataset meets the personalized demands of customers with different value tiers, and provides references for footwear enterprises intending to launch custom insole businesses in Anhui Province.

The customer classification algorithm for Anhui region adopts the RFM data model-based ranking and clustering method. Specifically, it clusters the recency of a customer's last activity (R, measured in days) and activity frequency (F, number of occurrences), and ranks the consumption amount (M, total amount), so as to classify customers in Anhui Province into different tiers.

1. Data Source: Collected from the custom insole sales order data of the enterprise in Anhui Province.
2. Data Processing: Noise reduction, data cleaning, anonymization, aggregation and analysis are performed on the collected dataset.
3. Model Selection: The RFM model is utilized to extract three metrics: recency of customer's last activity (R, days), activity frequency (F, number of times) and total consumption amount (M), for clustering, with ranking conducted based on the recency metric (R).

Data Scoring: Customers are scored on a scale of 1 to 5. The top 20% of customers receive 5 points, the subsequent 20% get 4 points, the next 20% are assigned 3 points, the following 20% get 2 points, and the last 20% receive 1 point. For the activity frequency (F) metric, customers are sorted in descending order, with the top 20% obtaining 5 points, and the rest assigned scores following the same rule. For the total consumption amount (M) metric, the top 20% of customers get 5 points, with the remaining customers scored in the same pattern. The comprehensive RFM score is calculated as: RFM Score = (R Score) * 0.3 + (F Score) * 0.3 + (M Score) * 0.4. Based on the comprehensive score, customers are categorized as follows: Tier A customers with a score ≥4, Tier B customers with 3 ≤ Score <4, Tier C customers with 2 ≤ Score <3, and Tier D customers with Score <2.

搜集汇总

数据集介绍

特点

该数据集为安徽地区定制鞋垫消费者分析数据，包含2249条记录，采用RFM模型对消费者进行分类，适用于鞋类企业在安徽地区的精准营销策略制定。