IDENTIFYING CUSTOMER BUYING PATTERNS USING MARKET BASKET ANALYSIS

K. Rakhmanaliyeva
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引用次数: 1

Abstract

Market Basket Analysis (MBA) is an approach that finds the strength of association between pairs of products that customers buy and can determine patterns of co-occurrence. The main aim of MBA is to determine customer buying behavior and predict next purchase. It can help companies to increase cross-selling.To generate association rules, the Apriori algorithm employs frequently purchased item-sets. It is based on the idea that a frequently purchased item’s subset is also a frequently purchased item. If the support value of a frequently purchased item-set exceeds a minimum threshold, the item-set is chosen. This paper observes the advantages of implementing MBA, algorithms that applies in this technique and ways to identify customer buying patterns.
使用购物篮分析识别客户购买模式
市场购物篮分析(Market Basket Analysis, MBA)是一种发现客户购买的产品对之间关联强度的方法,可以确定共现模式。MBA的主要目的是确定客户的购买行为并预测下一次购买。它可以帮助公司增加交叉销售。为了生成关联规则,Apriori算法使用频繁购买的商品集。它基于一个经常购买的物品的子集也是一个经常购买的物品的想法。如果经常购买的项集的支持值超过最小阈值,则选择该项集。本文观察了实现MBA的优点、应用于该技术的算法以及识别客户购买模式的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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