Mining web content usage patterns of electronic commerce transactions for enhanced customer services

Abstract

A successful business intelligence solution can help organizations improve the quality and speed of their decision‐making processes by analyzing the consolidated information collected from their websites. Using the current Web server log standard, which indicates only the locations of served Web pages, may lead to inaccurate business analysis for data driven and frequently updated static content Web pages. A properly defined Web content usage data warehouse that captures both dynamic and static contents of web pages provides rich data source for discovering interesting business rules among users' activities. This paper demonstrates the simplicity of data extract, transform and load procedures to import raw Web content usage log to various data models for data analysis, reporting and data mining tools. In this paper, we use two data mining techniques (expected maximization and the prefixspan algorithms) for visitor grouping and path analysis to find interesting patterns in Web content usage log data, an important component of e‐commerce Web sites traffic analysis. Visitor grouping uses data clustering to group visitors/sessions with similar selected attributes' value. Path analysis mines common visiting path sequence. The ultimate goal is to enable the online merchant provide enhanced and differentiated marketing services generally to its existing and potential customers and tell what items customers are interested in instead of ambiguous URLs.

The Transition From Ecommerce to M Commerce

“…Web content usage patterns analysis is essential for enhancing customer services in electronic commerce transactions. In this literature review, we will explore existing research on mining web content usage patterns in electronic commerce transactions for enhanced customer services [4]. Mining association rules is a data mining technique that is used to identify patterns in large datasets.…”

“…Year Major Findings/Purpose [1] 2008 Effective Mobile Website Optimization: Making the Switch from eCommerce to mCommerce [2] 2000 E-commerce technology migration [3] 2004 Apps for mobile commerce are introduced in the special edition. [4] 2021 For better customer support, mining web content consumption patterns from electronic commerce transactions [5] 2018 In e-commerce apps, mining association guidelines for admission control and service differentiation [6] 2022 Farmers' professional cooperatives in rural tourism and e-commerce underwent a SWOT analysis in the big data age. [7] 2021 A conceptual paradigm for understanding the variables influencing consumers' attitudes in Lebanon towards the use of mobile commerce [8] 2015 Examining how technology is accepted for mobile shopping: an observational study among smartphone users [9] 2022 Predicting mobile shopping adoption and purpose during Malaysia's COVID-19 lockdown [10] 2017…”

Implementation of Sequential Pattern Neural Classifier in E-Commerce Data Behavioral Characteristic Extraction

“…These studies are relevant to develop models to enhance product recommendations. The existing recommendation systems LiuRec09, ChoiRec12, SuChenRec15, and HPCRec18 (1)(2)(3) employ mining algorithms with certain sequences. The LiuRec09 method groups users with comparable clickstream sequence data into clusters, and then selects Top-N neighbours from the cluster to a target user who belongs to segmentation-based collaborative filtering and association rule mining.…”