利用 SPSS 程序的 K-Means 聚类模型进行二手车客户细分:案例研究 Caroline.Id

Muhammad Farhan, Jerry Heikal
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摘要

本研究讨论了如何使用 K-Means 聚类算法来确定市场细分并制定有针对性的营销策略。这一过程包括根据传输类型、客户满意度、付款方式和分支机构等各种因素对客户数据进行分组。数据分组后,通过提供一个初始号码进行初始化阶段,然后执行聚类过程。对由此产生的聚类进行分析,以确定不同的客户档案和需求。通过深入了解每个细分市场,企业可以针对每个客户群体制定具体的、有针对性的营销策略。此外,本研究还讨论了如何通过确定目标受众、了解他们的需求和愿望、创建人物档案以及编制包含所有重要信息的品牌角色文件来构建品牌角色。定制的品牌角色可用于制定在线价值主张和营销战略。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Used Car Customer Segmentation Using K-Means Clustering Model With SPSS Program: Case Study Caroline.Id
This study discusses the use of K-Means clustering algorithm to determine market segmentation and prepare targeted marketing strategies. The process involves grouping customer data based on various factors such as transmission type, customer satisfaction, payment method, and branches. After grouping the data, the initialization stage is carried out by providing an initial number, and then the clustering process is carried out. The resulting clusters are analyzed to identify different customer profiles and needs. With an in-depth understanding of each segmentation, companies can develop specific and targeted marketing strategies for each customer group. Additionally, this study discusses the construction of a brand persona by identifying the target audience, understanding their needs and wants, creating a character profile, and compiling a brand persona document that includes all the important information. The customized brand persona can then be used in the development of online value proposition and marketing strategies.
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