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Data Gathering Architecture for Clickstream Data Mining on Online Shop

机译:在线商店中Clickstream数据挖掘的数据收集架构

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Online buying and selling activities in Indonesia continue to develop every year. In 2018 there were approximately 106.96 million users of online-based transactions and the number is predicted to continue to grow. This is caused by a shift from conventional shopping method to online shopping method, as online shopping method is more practical. At present, online shops are starting to analyze the data of online sales and purchases, generated from the websites, for the purpose of compiling a sales strategy. One type of data that can be analyzed is customer behavior data. This data can be recorded based on customer browsing activities when they are doing online shopping, resulting in clickstream data. In this study, a data collection architecture for online shops will be proposed, which will provide a module for recording clickstream data for online shops’ users. This module will be designed on web and mobile applications, considering that currently customers can also access online shops through mobile commerce applications on smartphones that are used. The clickstream data that has been collected, will be processed further for the purpose of making decisions related to the sales strategy, such as to identify potential customers and determine recommendations for customers.
机译:印尼的网上买卖活动每年都在发展。 2018年,基于在线交易的用户约有10996万,预计这一数字将继续增长。这是由于从传统的购物方式向在线购物方式的转变引起的,因为在线购物方式更加实用。目前,在线商店开始分析从网站生成的在线销售和购买数据,以编制销售策略。可以分析的一种数据类型是客户行为数据。可以根据客户在进行在线购物时的浏览活动来记录此数据,从而产生点击流数据。在这项研究中,将提出一种用于在线商店的数据收集体系结构,该体系结构将提供一个模块,用于记录在线商店用户的点击流数据。考虑到当前客户还可以通过使用的智能手机上的移动商务应用程序访问在线商店,因此将在Web和移动应用程序上设计该模块。已收集的点击流数据将被进一步处理,以做出与销售策略相关的决策,例如识别潜在客户并确定对客户的建议。

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