Exploring Big Data Opportunities for Online Customer Segmentation

Georgia Fotaki, Marco Spruit, Sjaak Brinkkemper and Dion Meijer
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Georgia Fotaki: Department of Information and Computing Sciences, Utrecht University, Utrecht, Netherlands
Marco Spruit: Department of Information and Computing Sciences, Utrecht University, Utrecht, Netherlands
Sjaak Brinkkemper: Department of Information and Computing Sciences, Utrecht University, Utrecht, Netherlands
Dion Meijer: GX Software, Nijmegen, Netherlands

International Journal of Business Intelligence Research (IJBIR), 2014, vol. 5, issue 3, 58-75

Abstract: In today's competitive business environment, more and more organizations move or extent their business online. Thus, there is an increasing need for organizations to build concrete online marketing strategies in order to engage with their customers. One basic step towards achieving the objectives related to online marketing is the segmentation of online customers, based on the customer data gathered online. Since there is an onslaught of customer information collected from online sources, new techniques are required for managing and analyzing the huge amount of data, and this is where the concept of Big Data can play an essential role. This research sheds light on three fields: Online Marketing, Customer Segmentation, and Big Data Analytics. The three domains are integrated into the Online Customer Segmentation (OCS) framework, which attempts to show how online marketing objectives can be supported by techniques and tools applicable to extremely large datasets. For the creation of the OCS framework a set of main online marketing objectives is defined. Moreover, the differences among customer attributes gathered from offline and online channels are discussed and OCS categories are identified. Finally, the concept of Big Data is introduced and relevant techniques and tools suitable for analyzing customer segmentation categories and segmenting customers effectively are described. This work demonstrates the OCS framework by applying it on a hypothetical business scenario using an online customer data set.

Date: 2014
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