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INTEGRATION OF STRUCTURED AND UNSTRUCTURED DATA IN THE ANALYSIS OF E-COMMERCE CUSTOMERS

Snezhana Sulova

First published: 2018-06-20https://doi.org/10.5593/sgem2018/2.1/s07.063View metrics

Abstract

The main reason for the rapid development of e-commerce is the possibility of personalization and individual customer service. Modern e-commerce systems have diverse and well-developed capabilities to analyse and manage customer relationships. In most cases, however, these analytical systems are based on processing and analysis of structured customer data, those that are collected at registration and stored in the database. It is important to note that many of the customers when completing registration forms do not always fill in their data properly because of the lack of sufficient motivation or for security reasons. In addition, it is sometimes difficult to derive sufficiently detailed information about customer behavior from this data alone. What's more - in this case one of the major challenges to the development of e-commerce is the search for and use of additional data sources for customers. That is why we propose for business analyses in the field of electronic commerce the integrated use of the data accumulated in the databases and the data obtained from the processing of unstructured information, deriving mainly from additional surveys and from different Internet sources. Data Mining Methods were used to analyze the resulting integrated data, and the approbation was done with a RapidMiner software product.

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Publication details

Title
INTEGRATION OF STRUCTURED AND UNSTRUCTURED DATA IN THE ANALYSIS OF E-COMMERCE CUSTOMERS
Authors
Snezhana Sulova
Proceedings
SGEM International Multidisciplinary Scientific GeoConference EXPO Proceedings; 18th International Multidisciplinary Scientific GeoConference SGEM2018, Informatics, Geoinformatics and Remote Sensing
Publisher
STEF92 Technology
Year
2018
Pages
499-506
SWS Citekey
Sulova20187499506
ISSN
1314-2704
ISBN
978-619-7408-39-3
Language
en
Publication type
Conference Paper
Keywords
References4
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  2. Snezhana Sulova (2017). USING TEXT MINING TO CLASSIFY RESEARCH PAPERS. DOI: 10.5593/sgem2017/21/s07.083

  3. Ian H. Witten (2008). Data Mining. http://dl.acm.org/citation.cfm?id=1207405

  4. John Van Ryzin; Leo Breiman; Jerome H. Friedman; Richard A. Olshen; Charles J. Stone (1986). Classification and Regression Trees.. DOI: 10.2307/2288003

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Number of times cited according to Crossref: 2

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