{'fraction_non_alphanumeric': 0.019642857142857142, 'fraction_numerical': 0.0175, 'mean_word_length': 5.621749408983452, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Quantitative Models in Support of Direct Marketing in Electronic Channels

Direct marketing activities are an integral part of a company’s interactions with its customers. Through these interactions the company aims to enrich the experience of existing customers and to attract new customers to the commodities on sale. Often, the aim of direct marketing is to classify customers so that personalized advertising and promotional activities can be targeted to specific classes of customers. Such an approach is gaining importance at present. The traditional channels for direct marketing include telephone and catalog mail. Due to the proliferation of the Internet and mobile telecommunication, new channels are catching the attention of direct marketers and they include e-mail, Web, short messaging service (SMS), multimedia messaging service (MMS), and instant messenger services. There is evidence that direct marketing in these electronic channels can be extremely profitable. The fundamental problem of direct marketing that marketers and scholars want to solve is how to target customers who are most likely to respond positively to marketing activities and how to deliver services in the most user-friendly and personalized way to customers without intruding on their privacy. One group of tools used in this area includes quantitative models such as those based on statistics and machine learning. These quantitative models can help marketers do various tasks, such as customer segmentation, customer lifetime value evaluation, and customer targeting. The challenge remains in the integration of these quantitative models with the electronic channels so that marketers can deliver promotional information and personalized services to customers effectively and efficiently. This special issue focuses on advancing research in quantitative models for direct marketing by publishing relevant and rigorous research that stimulates future research on designing, managing, and evaluating direct marketing activities in electronic channels in today’s rapidly changing marketing environment. The special issue consists of four articles addressing various aspects of direct marketing in electronic channels. These articles went though two rounds of rigorous blind reviews by members of the editorial review board before their final selection. In the following paragraphs, the key content and contribution of these articles is briefly summarized. Products that are sold online often come with reviews that influence sales of such products. In the first article, ‘‘Sequential Bias in Online Product Reviews,’’ Gaurav Journal of Organizational Computing and Electronic Commerce, 19: 83–84, 2009 Copyright Taylor & Francis Group, LLC ISSN: 1091-9392 print / 1532-7744 online DOI: 10.1080/10919390902820723