15,320 research outputs found
How do fashion retail customers search on the Internet?: Exploring the use of data mining tools to enhance CRM
This paper seeks to determine the usefulness of data mining tools to SMEs in developing customer relationship management (CRM) in the fashion retail sector. Kalakota & Robinsonâs (1999, p.114) model of âThe Three Phases of CRMâ acts as a basis to explore the use of data mining software. This paper reviews the nature and type of data that is available for collection and its relevance to CRM; providing an advisory framework for practitioners for them to examine the scope and limitations of using data analysis to improve CRM. The data mining tool examined was Google Analytics (GA); an online freeware tool that enables businesses to understand how people find their site, how they navigate through it, and, ultimately, how they do or donât become customers of it (Google Analytics, 2009). Establishing these relationships should lead to retailer development of enhanced web site aesthetics and functionality to coincide with consumer expectations. The paper finds that the competitive nature and homogeneity of the fashion retail sector requires retailers to improve the âreach, richness and affiliationâ (Hackney et al) of their sites by using technology to explore CRM
Optimizing E-Commerce Product Classification Using Transfer Learning
The global e-commerce market is snowballing at a rate of 23% per year. In 2017, retail e-commerce users were 1.66 billion and sales worldwide amounted to 2.3 trillion US dollars, and e-retail revenues are projected to grow to 4.88 trillion USD in 2021. With the immense popularity that e-commerce has gained over past few years comes the responsibility to deliver relevant results to provide rich user experience. In order to do this, it is essential that the products on the ecommerce website be organized correctly into their respective categories. Misclassification of products leads to irrelevant results for users which not just reflects badly on the website, it could also lead to lost customers. With ecommerce sites nowadays providing their portal as a platform for third party merchants to sell their products as well, maintaining a consistency in product categorization becomes difficult. Therefore, automating this process could be of great utilization. This task of automation done on the basis of text could lead to discrepancies since the website itself, its various merchants, and users, all could use different terminologies for a product and its category. Thus, using images becomes a plausible solution for this problem. Dealing with images can best be done using deep learning in the form of convolutional neural networks. This is a computationally expensive task, and in order to keep the accuracy of a traditional convolutional neural network while reducing the hours it takes for the model to train, this project aims at using a technique called transfer learning. Transfer learning refers to sharing the knowledge gained from one task for another where new model does not need to be trained from scratch in order to reduce the time it takes for training. This project aims at using various product images belonging to five categories from an ecommerce platform and developing an algorithm that can accurately classify products in their respective categories while taking as less time as possible. The goal is to first test the performance of transfer learning against traditional convolutional networks. Then the next step is to apply transfer learning to the downloaded dataset and assess its performance on the accuracy and time taken to classify test data that the model has never seen before
Datamining for Web-Enabled Electronic Business Applications
Web-Enabled Electronic Business is generating massive amount of data on customer purchases, browsing patterns, usage times and preferences at an increasing rate. Data mining techniques can be applied to all the data being collected for obtaining useful information. This chapter attempts to present issues associated with data mining for web-enabled electronic-business
Personality in Computational Advertising: A Benchmark
In the last decade, new ways of shopping online have increased the
possibility of buying products and services more easily and faster
than ever. In this new context, personality is a key determinant
in the decision making of the consumer when shopping. A personâs
buying choices are influenced by psychological factors like
impulsiveness; indeed some consumers may be more susceptible
to making impulse purchases than others. Since affective metadata
are more closely related to the userâs experience than generic
parameters, accurate predictions reveal important aspects of userâs
attitudes, social life, including attitude of others and social identity.
This work proposes a highly innovative research that uses a personality
perspective to determine the unique associations among the
consumerâs buying tendency and advert recommendations. In fact,
the lack of a publicly available benchmark for computational advertising
do not allow both the exploration of this intriguing research
direction and the evaluation of recent algorithms. We present the
ADS Dataset, a publicly available benchmark consisting of 300 real
advertisements (i.e., Rich Media Ads, Image Ads, Text Ads) rated
by 120 unacquainted individuals, enriched with Big-Five usersâ
personality factors and 1,200 personal usersâ pictures
A Special Issue on Statistical Challenges and Opportunities in Electronic Commerce Research
This special issue is a product of the First Interdisciplinary Symposium on
Statistical Challenges and Opportunities in Electronic Commerce Research, which
took place on May 22--23, 2005, at the Robert H. Smith School of Business,
University of Maryland, College Park
(\url{www.smith.umd.edu/dit/statschallenges/}). The symposium brought together,
for the first time, researchers from statistics, information systems, and
related fields, all of whom work or are interested in empirical research
related to electronic commerce. The goal of the symposium was to cross the
borders, discuss joint research opportunities, expose this field and its
statistical challenges, and promote collaboration between the different fields.Comment: Published at http://dx.doi.org/10.1214/088342306000000178 in the
Statistical Science (http://www.imstat.org/sts/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Information Technology Applications in Hospitality and Tourism: A Review of Publications from 2005 to 2007
The tourism and hospitality industries have widely adopted information
technology (IT) to reduce costs, enhance operational efficiency, and most importantly to
improve service quality and customer experience. This article offers a comprehensive review of
articles that were published in 57 tourism and hospitality research journals from 2005 to 2007.
Grouping the findings into the categories of consumers, technologies, and suppliers, the article
sheds light on the evolution of IT applications in the tourism and hospitality industries. The
article demonstrates that IT is increasingly becoming critical for the competitive operations of
the tourism and hospitality organizations as well as for managing the distribution and
marketing of organizations on a global scale
Is adaptation of e-advertising the way forward?
E-advertising is a multi-billion dollar industry that has shown exponential growth in the last few years. However, although the number of users accessing the Internet increases, users donât respond positively to adverts. Adaptive e-advertising may be the key to ensuring effectiveness of the ads reaching their target. Moreover, social networks are good sources of user information and can be used to extract user behaviour and characteristics for presentation of personalized advertising. Here we present a two-sided study based on two questionnaires, one directed to Internet users and the other to businesses. Our study shows that businesses agree that personalized advertising is the best way for the future, to maximize effectiveness and profit. In addition, our results indicate that most Internet users would prefer adaptive advertisements. From this study, we can propose a new design for a system that meets both Internet usersâ and businessesâ requirements
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