Evaluation of e-Word-of-Mouth through Business Intelligence processes in banking domain

Social networks and Internet discussions are valuable sources for a company’s marketing research and public relations management. The Internet is full of public communication in an unstructured form and reflects recent movements of contributors' perception of the company, brand, products, competitors or whole market. As one of the approaches to achieve a better view we propose to design metrics which should be followed in order to get valuable insight where the company stands in terms of its customers. This paper focuses on obtaining an e-Word-of-Mouth in the banking sector using publicly available data. The main goal is to design metrics and dashboards evaluating customers’ perception of a bank’s services based on the analysis of public Facebook sites and web discussions related to several banks in the Czech Republic. We studied several approaches to unstructured data analysis. Thus we present complementary findings in classification of the unstructured data analysis presentation as a set of summarised metadata, top peaks of primary qualitative data and results of automated semantic analysis of the unstructured data. Based on the result we discuss the possible value of an unstructured data analysis and related systems. We find out that the value could be in the identification of opportunities and threats in the market by unexpected movements in public opinion of the Internet crowd, which we suggest to explore in future research. The benefit of this report is to describe the processing of data that can be obtained with emphasis on their content, their further enrichment, and their users.

[1]  Yuh-Min Chen,et al.  Electronic word of mouth analysis for service experience , 2013, Expert Syst. Appl..

[2]  Rong Zheng,et al.  The impact of word-of-mouth on book sales: review, blog or tweet? , 2012, ICEC '12.

[3]  Jun Bai,et al.  Feasibility analysis of big log data real time search based on Hbase and ElasticSearch , 2013, 2013 Ninth International Conference on Natural Computation (ICNC).

[4]  Szymon Adamala,et al.  Key Success Factors in Business Intelligence , 2011 .

[5]  Mohammed M. Almossawi The Impact of Word of Mouth (WOM) on the Bank Selection Decision of the Youth: A Case of Bahrain , 2015 .

[6]  Jan Pour,et al.  Business intelligence v podnikové praxi , 2012 .

[7]  Thomas Johnson Indexing Linked Bibliographic Data with JSON-LD, BibJSON and Elasticsearch , 2013 .

[8]  Thomas Reinartz,et al.  CRISP-DM 1.0: Step-by-step data mining guide , 2000 .

[9]  J. Coyle,et al.  Electronic word of mouth: The effects of incentives on e‐referrals by senders and receivers , 2013 .

[10]  S. Sénécal,et al.  The influence of online product recommendations on consumers' online choices , 2004 .

[11]  Paul A. Pavlou,et al.  Can online reviews reveal a product's true quality?: empirical findings and analytical modeling of Online word-of-mouth communication , 2006, EC '06.

[12]  Judy E. Scott,et al.  Electronic Word of Mouth and Knowledge Sharing on Social Network Sites: A Social Capital Perspective , 2013, J. Theor. Appl. Electron. Commer. Res..

[13]  Bettina Lis,et al.  Electronic Word of Mouth , 2013, Business & Information Systems Engineering.

[14]  Andrew J. Czaplewski,et al.  eWOM: The impact of customer-to-customer online know-how exchange on customer value and loyalty , 2006 .

[15]  Victor R. Prybutok,et al.  Quantitative quality control from qualitative data: control charts with latent semantic analysis , 2015 .

[16]  Alex S. L. Tsang,et al.  Is a “star” worth a thousand words?: The interplay between product‐review texts and rating valences , 2009 .

[17]  Hans-Georg Kemper,et al.  Business Intelligence — Grundlagen und praktische Anwendungen , 2004 .

[18]  Hans-Georg Kemper,et al.  Management Support with Structured and Unstructured Data—An Integrated Business Intelligence Framework , 2008, Inf. Syst. Manag..

[19]  Hsinchun Chen Business and Market Intelligence 2.0 , 2010 .

[20]  Watson,et al.  Integrating Structured and Unstructured Data Using Text Tagging and Annotation 8 , 2006 .

[21]  Jakob Grue Simonsen,et al.  Extracting usability and user experience information from online user reviews , 2013, CHI.

[22]  Thomas Serries,et al.  Informationsportale für das Management: Integration von Data-Warehouse- und Content-Management-Systemen , 2002 .

[23]  C. J. McGrath,et al.  Effect of exchange rate return on volatility spill-over across trading regions , 2012 .

[24]  P. Aurier,et al.  Perceived justice and consumption experience evaluations , 2007 .

[25]  Chrysanthos Dellarocas,et al.  Exploring the value of online product reviews in forecasting sales: The case of motion pictures , 2007 .

[26]  Owen Kaser,et al.  Analyzing Large Collections of Electronic Text Using OLAP , 2006, ArXiv.

[27]  Ralph Kimball,et al.  The Kimball Group Reader: Relentlessly Practical Tools for Data Warehousing and Business Intelligence , 2010 .

[28]  Yi-Cheng Zhang,et al.  Leaders in Social Networks, the Delicious Case , 2011, PloS one.

[29]  U. Yavas,et al.  Relationships between service quality and behavioral outcomes , 2004 .

[30]  R. East Researching Word of Mouth , 2007 .

[31]  Line Ricard,et al.  e-WOM Scale: Word-of-Mouth Measurement Scale for e-Services Context* , 2010 .

[32]  C. Lymperopoulos,et al.  Price satisfaction and personnel efficiency as antecedents of overall satisfaction from consumer credit products and positive word of mouth , 2008 .

[33]  Lucie Sperková,et al.  Analýza nestrukturovaných dat z bankovních stránek na sociální síti Facebook , 2014, Acta Informatica Pragensia.