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Experts cite the lack of a sound business plan and a diminished regard for basic marketing and management practices as major reasons for the failure rate of Web‐based retailers. The dot‐com platform alone was often viewed as a sufficient basis for business success. This article provides a guide in applying marketing management principles to Internet‐based retailers. The format for this guide uses marketing management plans, providers, access, distribution, markets, products, prices, and promotions. The practices and examples provided in this guide are useful for gaining competitive advantage in the retail, dot‐com marketplace.

In this paper we present a corporate information system for untrained users to search gigabytes of unformatted data using quasi‐natural language and relevance feedback queries. The data can reside on distributed servers anywhere on a wide area network, giving the users access to personal, corporate, and published information from a single interface. Effective queries can be turned into profiles, allowing the system to automatically alert the user when new data are available. The system was tested by twenty executive users located in six cities. Our primary goal in building the system was to determine if the technology and infrastructure existed to make end‐user searching of unstructured information profitable. We found that effective search and user interface technologies for end‐users are available, but network technologies are still a limiting cost factor. As a result of the experiment, we are continuing the development of the system. This article will describe the overall system architecture, the implemented subset, and the lessons learned.

This study aims to investigate the factors that influence South African customers to potentially switch from one bank to another. Instead of using established models and survey techniques, the research measured social media sentiment to measure threats to switch.

The research involved a 12-month analysis of social media sentiment, specifically customer threats to switch banks (churn). These threats were then analysed for co-occurring themes to provide data on the reasons customers were making these threats. The study used over 1.7 million social media posts and focused on all five major South African retail banks (essentially the entire sector).

This study concluded that seven factors are most significant in understanding the underlying causes of churn. These are turnaround time, accusations of unethical behaviour, billing or payments, telephonic interactions, branches or stores, fraud or scams and unresponsiveness.

This study is unique in its measurement of unsolicited social media sentiment as opposed to most churn-related research that uses survey- or customer-data-based methods. In addition, this study observed the sentiment of customers from all major retail banks across 12 months. To date, no studies on retail bank churn theory have provided such an extensive perspective. The findings contribute to Susan Keaveney’s churn theory and provide a new measurement of switching threat through social media sentiment analysis.

This paper aims to present a method based on hidden Markov models (HMM) for extracting information from web news.

The samples under study are derived from the contents of PROC “People's Daily Online,” a web‐based news publication containing non‐structured archives. This study focuses on developing HMM‐based tools for news filtering in order to retrieve terms of interest, such as “Geo‐location,” “System,” and “Personas.” The experiments are performed in two stages. In the first stage, each HMM being built is exclusively serving for extracting unique target term in order to evaluate the fundamental information extraction (IE) capability. In the second stage, the experiment is then extended to resolve a more complex, multi‐term extraction issue.

The results reveal that, by using HMMs as a basis, the accuracies (F‐measure) for unique IE tasks can achieve more than 70 per cent on average, while no fewer than 66 per cent accuracies are obtained for multi‐term extraction.

The study reveals the promising of using HMM for developing automatic tool in filtering free‐structured data.

Recently, with a high volume of users and user’s content in Community Question Answering (CQA) sites, the quality of answers provided by users has raised a big concern. Finding the expert users can be a method to address this problem, which aims to find the suitable users (answerers) who can provide high-quality relevant answers. The purpose of this paper is to find the expert users for the newly posted questions of the CQA sites.

In this paper, a new algorithm, RANKuser, is proposed for identifying the expert users of CQA sites. The proposed RANKuser algorithm consists of three major stages. In the first stage, folksonomy relation between users, tags, and queries is established. User profile attributes, namely, reputation, tags, and badges, are also considered in folksonomy. In the second stage, expertise scores of the user are calculated based on reputation, badges, and tags. Finally, in the third stage, the expert users are identified by extracting top N users based on expertise score.

In this work, with the help of proposed ranking algorithm, expert users are identified for newly posted questions. In this paper, comparison of proposed user ranking algorithm (RANKuser) is also performed with other existing ranking algorithms, namely, ML-KNN, rankSVM, LDA, STM CQARank, and EV-based model using performance parameters such as hamming loss, accuracy, average precision, one error, F-measure, and normalized discounted cumulative gain. The proposed ranking method is also compared to the original ranking of CQA sites using the paired t-test. The experimental results demonstrate the effectiveness of the proposed RANKuser algorithm in comparison with the existing ranking algorithms.

This paper proposes and implements a new algorithm for expert user identification in CQA sites. By utilizing the folksonomy in CQA sites and information of user profile, this algorithm identifies the experts.