مدل رفتار الکترونیکی مشتری با استخراج تحلیلی آنلاین برای برنامه ریزی بازاریابی اینترنتی

In the digital market, attracting sufficient online traffic in a business to customer Web site is vital to an online business's success. The changing patterns of Internet surfer access to e-commerce sites pose challenges for the Internet marketing teams of online companies. For e-business to grow, a system must be devised to provide customers' preferred traversal patterns from product awareness and exploration to purchase commitment. Such knowledge can be discovered by synthesizing a large volume of Web access data through information compression to produce a view of the frequent access patterns of e-customers. This paper develops constructs for measuring the online movement of e-customers, and uses a mental cognitive model to identify the four important dimensions of e-customer behavior, abstract their behavioral changes by developing a three-phase e-customer behavioral graph, and tests the instrument via a prototype that uses an online analytical mining (OLAM) methodology. The knowledge discovered is expected to foster the development of a marketing plan for B2C Web sites. A prototype with an empirical Web server log file is used to verify the feasibility of the methodology.

In e-commerce, the current challenge is determining how to design responsive Web site infrastructure that provides a sustainable competitive advantage through a better understanding of target customers. The quality of an e-commerce site depends on interrelated factors such as site architecture, network capacity, Web services, and the unpredictability of e-customer behavior. These characteristics imply the need to measure the behavior of the Web-based system and its users. Knowledge management is the key to business learning. The technologies that support knowledge management in e-business are data warehousing, data mining, the Internet, and document management systems [21], [25] and [26]. Online marketing aims to produce online revenue by understanding customer needs. Meeting this objective requires knowledge of how e-customers' online movements change from awareness of products to the exploration of options and further to purchase commitment. An online analytical mining (OLAM) system using an underlying cognitive model and e-customer behavioral graph can be used to articulate the online activities of e-customers on a particular Web site. This can provide the framework of an e-customer behavior (eCB) model that can be used to discover e-customer profiles which identify the significant dimensions of online behavior and identify Web pages that trigger behavior changes. The knowledge thereby obtained will foster informed Internet marketing decision making, and allow Web content and infrastructure refinement to support Internet marketing.

This paper proposes an eCB model that uses an OLAM methodology to discover e-customer behavioral changes on a Web site to support Internet marketing. We undertake an empirical study using a prototype built upon the eCB model to verify its feasibility. Our prototype identifies different customer segments according to their successful path frequencies, counting sessions that result in purchases, the referring Web pages that determine targeted e-customer behavioral changes, and, based on the correlation semantic discovered between clicks in sessions from e-customers' click histories, the most popular paths taken by target e-customers. Moreover, typical trigger URLs that lead to positive progression toward purchase commitment are recovered. These patterns represent e-customer behavioral changes over the three phases of our e-customers behavior graph. In summary, we have proposed an instrument to measure the four dimensions of e-customer behavior: average path length (PL), log on path frequency (PF), page revisit frequency (PR) and page duration (PD). This knowledge is valuable in fostering informed decision making for effective Internet marketing plans and understanding the e-customers. Despite the promising result, our research findings could be further refined. More dimensions could be defined to measure the online movement of e-customers. The OLAM instrument could be enhanced to include the analysis of changes of access patterns over different durations, which will allow the analysis of e-customer behavioral evolution.