%0 Journal Article
%T 零售客户精准分类研究——以唐山市卷烟零售户为例<br>A Study on Precise Classification of Retail Customers: Taking Tangshan Cigarette Retailers as an Example
%A 孙炳华
%A 孙丹丹
%A 宁淑娟
%J Modern Management
%P 1398-1404
%@ 2160-732X
%D 2023
%I Hans Publishing
%R 10.12677/MM.2023.1311174
%X 为提升烟草行业诚信互助小组功能，深度了解社群群体成员的基本情况，以深挖社群运营价值，建立更有效的小组社群化运行模式，本文以唐山市卷烟零售客户为例，根据烟草行业卷烟销售特点，选出零售客户的15项基本属性指标，包括人像指标、店铺属性、经营属性、行为特征四个维度，并K-means聚类分析方法对零售户进行精准分类，从类内误差和离散度两方面验证聚类结果。本文收集了730份有效零售户样本，利用K-means聚类法划分为5类社群小组，分类结果较好，根据各聚类簇的特征，提取零售客户的当前和潜在价值，为烟草公司实施社群化管理服务提供依据。<br />
In order to enhance the function of the integrity mutual assistance group in the tobacco industry, gain a deep understanding of the basic situation of community members, explore the operational value of the community, and establish a more effective group community-based operation mode, this article takes Tangshan cigarette retail customers as an example, selects 15 basic attribute indicators for retail customers based on the sales characteristics of cigarettes in the tobacco industry, including four dimensions: portrait indicators, store attributes, business attributes, and behavioral characteristics, and K-means clustering analysis method is used to accurately classify retail households, verifying the clustering results from two aspects: intra class error and dispersion. This article collected 730 valid retail customer samples and divided them into 5 types of community groups using K-means clustering method. The classification results were good. Based on the characteristics of each cluster, the current and potential value of retail customers were extracted, providing a basis for tobacco companies to implement community-based management services.
%K 客户精准分类，K-means聚类，社群运营<br>Accurate Customer Classification
%K K-Means Clustering
%K Community Operations
%U http://www.hanspub.org/journal/PaperInformation.aspx?PaperID=74765