Applying K Means Clustering Techniques on Retail Shop

Chandra Mohini C.P, V. Raghavendran
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Abstract

Implement a customer segmentation system using k-mean clustering for a Retail business. Collect Data and pre-processing to clear missing data and inconsistence data and then visualization to analysis(EDA), apply K-Means to divide customer into distinct groups based on purchasing behaviour. Analyse and create customer profiles for each segment to optimize marketing strategies and enhance customer Engagement. Main goal of in this project on price optimization, Increase revenue, brand awareness and increase relationship between customer and enhance marketing Strategies and increasing sales.
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在零售店中应用 K 均值聚类技术
使用 K-均值聚类为零售企业实施客户细分系统。收集数据并进行预处理,清除缺失数据和不一致数据,然后进行可视化分析(EDA),应用 K-均值法根据购买行为将客户划分为不同的群体。为每个细分市场分析并创建客户档案,以优化营销策略并提高客户参与度。本项目的主要目标是优化价格、增加收入、提高品牌知名度、加强客户关系、强化营销策略和提高销售额。
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