Exploring Big Data Opportunities for Online Customer Segmentation

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.

[1]  Liu Yijun,et al.  Customer Segmentation for Telecom with the k-means Clustering Method , 2013 .

[2]  John Wang,et al.  Encyclopedia of Business Analytics and Optimization , 2018 .

[3]  Cliff Wymbs.  Digital Marketing: The Time for a New “Academic Major” Has Arrived , 2011 .

[4]  Marco R. Spruit,et al.  Linguistic Engineering and its Applicability to Business Intelligence - Towards an Integrated Framework , 2011, KDIR.

[5]  Su-Yeon Kim,et al.  Customer segmentation and strategy development based on customer lifetime value: A case study , 2006, Expert Syst. Appl..

[6]  A. Baranov.  BUILDING ONLINE CUSTOMER RELATIONSHIP , 2012 .

[7]  SpruitMarco,et al.  Exploring Big Data Opportunities for Online Customer Segmentation , 2014 .

[8]  Chu-Chai Henry Chan,et al.  Intelligent value-based customer segmentation method for campaign management: A case study of automobile retailer , 2008, Expert Syst. Appl..

[9]  Li Xiu,et al.  Application of data mining techniques in customer relationship management: A literature review and classification , 2009, Expert Syst. Appl..

[10]  Vijay Srinivas Agneeswaran.  Big-Data - Theoretical, Engineering and Analytics Perspective , 2012, BDA.

[11]  S. Jansen,et al.  Customer Segmentation and Customer Profiling for a Mobile Telecommunications Company Based on Usage Behavior , 2007 .

[12]  Katherine N. Lemon,et al.  Customer Engagement Behavior: Theoretical Foundations and Research Directions , 2010 .

[13]  Srinivasan H. Sengamedu.  Scalable Analytics - Algorithms and Systems , 2012, BDA.

[14]  Cheng-Seen Ho,et al.  Toward a hybrid data mining model for customer retention , 2007, Knowl. Based Syst..

[15]  Roung-Shiunn Wu,et al.  Customer segmentation of multiple category data in e-commerce using a soft-clustering approach , 2011, Electron. Commer. Res. Appl..

[16]  Guozheng Zhang.  Customer Segmentation Based on Survival Character , 2007 .

[17]  Alan R. Hevner,et al.  Design Research in Information Systems , 2010 .

[18]  Arkady B. Zaslavsky,et al.  Sensing as a Service and Big Data , 2013, ArXiv.

[19]  Chi-Hong Leung,et al.  An Inductive Learning Approach to Market Segmentation based on Customer Profile Attributes , 2009 .

[20]  Xiuli He,et al.  Advance Information Sharing in Supply Chains , 2014 .

[21]  Özgür Çakır,et al.  A Recommendation Engine by Using Association Rules , 2012 .

[22]  Konstantinos Tsiptsis,et al.  Data Mining Techniques in CRM: Inside Customer Segmentation , 2010 .

[23]  Inge van de Weerd,et al.  Understanding users' behavior with software operation data mining , 2014, Comput. Hum. Behav..

[24]  Chu-Chai Henry Chan,et al.  Online Auction Customer Segmentation Using a Neural Network Model , 2005 .

[25]  Marco R. Spruit,et al.  Historical Data Analysis through Data Mining From an Outsourcing Perspective: The Three-Phases Model , 2010, Int. J. Bus. Intell. Res..

[26]  Veda C. Storey,et al.  Business Intelligence and Analytics: From Big Data to Big Impact , 2012, MIS Q..

[27]  Padhraic Smyth,et al.  From Data Mining to Knowledge Discovery in Databases , 1996, AI Mag..

[28]  Nicole Tache.  Big Data Now, 2015 Edition , 2016 .

[29]  Sanjay Ghemawat,et al.  MapReduce: Simplified Data Processing on Large Clusters , 2004, OSDI.

[30]  Sang-Chan Park,et al.  Visualization method for customer targeting using customer map , 2005, Expert Syst. Appl..

[31]  Lerzan Aksoy,et al.  Undervalued or Overvalued Customers: Capturing Total Customer Engagement Value , 2010 .

[32]  R. Winer.  A Framework for Customer Relationship Management , 2001 .

[33]  Gary Palmer.  Customer Intelligence , 2006 .

[34]  Aboul Ella Hassanien,et al.  Computational Social Network Analysis - Trends, Tools and Research Advances , 2010, Computational Social Network Analysis.

[35]  Konstantinos Tsiptsis,et al.  Data Mining Techniques in CRM , 2010 .

[36]  Sankar K. Pal,et al.  Data mining in soft computing framework: a survey , 2002, IEEE Trans. Neural Networks.

[37]  Mukesh K. Mohania,et al.  Cloud Computing and Big Data Analytics: What Is New from Databases Perspective? , 2012, BDA.

[38]  D. Chaffey,et al.  Internet Marketing: Strategy, Implementation and Practice , 2000 .

[39]  H. Wilson,et al.  Engagement, telepresence and interactivity in online consumer experience: Reconciling scholastic and managerial perspectives , 2010 .

[40]  Navneet Kaur,et al.  Opinion mining and sentiment analysis , 2016, 2016 3rd International Conference on Computing for Sustainable Global Development (INDIACom).

[41]  M. F.,et al.  Bibliography , 1985, Experimental Gerontology.

[42]  Melnned M. Kantardzic.  Big Data Analytics , 2013, Lecture Notes in Computer Science.

[43]  You-Shyang Chen,et al.  Classifying the segmentation of customer value via RFM model and RS theory , 2009, Expert Syst. Appl..

[44]  Inge van de Weerd,et al.  Meta-Modeling for Situational Analysis and Design Methods , 2009 .

[45]  Eunmi Kim,et al.  Segmenting customers in online stores from factors that affect the customer's intention to purchase , 2010, International Conference on Information Society.

[46]  Samir Chatterjee,et al.  A Design Science Research Methodology for Information Systems Research , 2008 .

[47]  Arvind Sathi,et al.  Big Data Analytics: Disruptive Technologies for Changing the Game , 2012 .

[48]  D. Coddington,et al.  The big deal about big data. , 2013, Healthcare financial management : journal of the Healthcare Financial Management Association.

[49]  O. P. Vyas,et al.  CSHURI - Modified HURI algorithm for Customer Segmentation and Transaction Profitability , 2012, ArXiv.

[50]  Sang-Chan Park,et al.  Intelligent profitable customers segmentation system based on business intelligence tools , 2005, Expert Syst. Appl..

[51]  Mohammad Jafar Tarokh,et al.  Customer Segmentation Using CLV Elements , 2011 .

[52]  Pr Smith,et al.  Emarketing Excellence: Planning and Optimizing your Digital Marketing , 2012 .

[53]  Dennis M. Wilkinson,et al.  Large-Scale Parallel Collaborative Filtering for the Netflix Prize , 2008, AAIM.

[54]  J. Manyika.  Big data: The next frontier for innovation, competition, and productivity , 2011 .

[55]  João Falcão e Cunha,et al.  Customer data mining for lifestyle segmentation , 2012, Expert Syst. Appl..

[56]  Khalid Raza,et al.  A Comprehensive Study of CRM through Data Mining Techniques , 2012, ArXiv.

[57]  Rick L. Edgeman,et al.  Six Sigma Innovation and Design , 2014 .