Abstract
Huge volume of structured and unstructured data which is called big data, nowadays, provides opportunities for companies especially those that use electronic commerce (e-commerce). The data is collected from customer’s internal processes, vendors, markets and business environment. This paper presents a data mining (DM) process for e-commerce including the three common algorithms: association, clustering and prediction. It also highlights some of the benefits of DM to e-commerce companies in terms of merchandise planning, sale forecasting, basket analysis, customer relationship management and market segmentation which can be achieved with the three data mining algorithms. The main aim of this paper is to review the application of data mining in e-commerce by focusing on structured and unstructured data collected thorough various resources and cloud computing services in order to justify the importance of data mining. Moreover, this study evaluates certain challenges of data mining like spider identification, data transformations and making data model comprehensible to business users. Other challenges which are supporting the slow changing dimensions of data, making the data transformation and model building accessible to business users are also evaluated. A clear guide to e-commerce companies sitting on huge volume of data to easily manipulate the data for business improvement which in return will place them highly competitive among their competitors is also provided in this paper.
Highlights
Data mining in e-commerce is all about integrating statistics, databases and artificial intelligence together with some subjects to form a new idea or a new integrated technology for the purpose of better decision making
The challenge is to design and define extra model types and a strategic way to present them to business users, what regression models can we come up with and how can we present them? (Even linear regression is usually hard for business users to understand.) How can we present nearest-neighbor models, for example? How can we present the results of association rule algorithms without overwhelming users with tens of thousands of rules? [22]
Data mining offers number of benefits to e-commerce companies and allows them to do merchandise planning, analyze customers’ purchasing behaviors and forecast their sales which in turn would place them over other companies and generate more revenue
Summary
Data mining in e-commerce is all about integrating statistics, databases and artificial intelligence together with some subjects to form a new idea or a new integrated technology for the purpose of better decision making. The end product of data mining creates an avenue for decision makers to be able to track their customers’ purchasing patterns, demand trends and locations, making their strategic decision more effective for the betterment of their business This can bring down the cost of inventory together with other expenses and maximizing the overall profit of the company. Cloud computing provides a new breakthrough for enterprises, offering a service model that includes network storage, new information resource sharing, on-demand access to information and processing mechanism. It is possible to provide data mining software via cloud computing which gives e-commerce companies opportunity to centralize their software management and data storage with absolute assurance of reliability, efficiency and protected services to their users which in turn cut their cost and increase their profit [4]. It reviews the process of data mining in e-commerce together with the common types of database and cloud computing in the field of e-commerce
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