Demystifying Big Data Analytics for Business Intelligence Through the Lens of Marketing Mix

Fan, Shaokun; Lau, Raymond Y. K.; Zhao, Jinxi

doi:10.1016/j.bdr.2015.02.006

Cited by 265 publications

(179 citation statements)

References 27 publications

(24 reference statements)

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“…; velocity refers to the speed to collect and process data. Big data research focuses on the techniques, technologies, systems, practices, methodologies, and applications that convert big data to useful, relevant, timely information, helping an enterprise to better understand its business and market conditions and to make appropriate decisions (Chen et al 2012;Fan et al 2015).…”

Section: Big Data For Credit Risk Management In P2p Lendingmentioning

confidence: 99%

How signaling and search costs affect information asymmetry in P2P lending: the economics of big data

Yan

Zhao

2015

Financial Innovation

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In the past decade, online Peer-to-Peer (P2P) lending platforms have transformed the lending industry, which has been historically dominated by commercial banks. Information technology breakthroughs such as big data-based financial technologies (Fintech) have been identified as important disruptive driving forces for this paradigm shift. In this paper, we take an information economics perspective to investigate how big data affects the transformation of the lending industry. By identifying how signaling and search costs are reduced by big data analytics for credit risk management of P2P lending, we discuss how information asymmetry is reduced in the big data era. Rooted in the lending business, we propose a theory on the economics of big data and outline a number of research opportunities and challenging issues.

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Section: Big Data For Credit Risk Management In P2p Lendingmentioning

confidence: 99%

How signaling and search costs affect information asymmetry in P2P lending: the economics of big data

Yan

Zhao

2015

Financial Innovation

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“…Thus, one can define data mining, with respect to customer profiling, as being the technology that allows building customer models (profiles), where each model describes the specific habits, needs and behaviour of a customer group (segment). Therefore, developing customer profiles is an important step for targeted marketing campaigns, for it does not only classify new customers, but also provide information of current customers [4]. This paper first explores traditional segmentation and profiling methods, and subsequently reviews data mining tools used for these processes.…”

Section: Introductionmentioning

confidence: 99%

Customer Super-Profiling Demonstrator to Enable Efficient Targeting in Marketing Campaigns

Walters

Bekker

2017

SAJIE

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Difficulties lie with identifying the right customers to engage in successful marketing campaigns. Thus far, segmentation has been a popular marketing method for selecting customer groups for targeted campaigns. However, each segment can be further exploited by performing customer profiling. In this paper, we explain the on-going development of a proposed simulator and demonstration tool that incorporates big data analytics to uncover hidden patterns within the customer dataset, thereby generating a customer super-profile. A developed toy problem and a large, realistic problem demonstrate segmentation, via clustering, and create customer profiles to enable marketers to identify appropriate marketing strategies. The proposed framework serves as the basis for enhancing customer relationship management by providing improved customer profiles for marketing campaigns. OPSOMMINGDaar is uitdagings by die identifisering van die regte kliënte vir suksesvolle bemarkingsveldtogte. Tot dusver is segmentasie 'n gewilde bemarkingmetode om te gebruik om kliëntgroepe te kies vir teikenveldtogte. Elke segment kan egter nog verder ontgin word deur kliënteprofilering uit te voer. In die artikel beskryf ons die ontwikkeling van 'n voorgestelde simulator en demonstrasiewerktuig wat groot data-analise inkorporeer om verborge patrone binne die kliëntedatastel te ontbloot, en daardeur word 'n kliëntsuperprofiel geskep. 'n Eenvoudige asook 'n groot, realistiese probleem demonstreer segmentasie deur groepering, en ontwikkel kliëntprofiele om bemarkers in staat te stel om geskikte bemarkingstrategieë te identifiseer. Die voorgestelde raamwerk dien as 'n basis om kliënte-verhoudingbestuur te versterk, deur verbeterde kliëntprofiele vir bemarkingsveldtogte beskikbaar te stel.

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“…Several approaches were experimented in several domains like: mobile communications (Laurila, et al, 2012), biology ), economics (World Economic Forum, 2012, (Letouzé, 2012), marketing (Fan, et al, 2015), decision making (Probst, et al, 2013), etc. In Big Data mining, it is usual to deal with 3V problems: Volume, Variety, and Velocity.…”

Section: Introductionmentioning

confidence: 99%

An Evaluation of the Challenges of Multilingualism in Data Warehouse Development

Dediź

Stanier

2016

Proceedings of the 18th International Conference on Enterprise Information Systems

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ICEIS 2016 received 257 paper submissions from 42 countries in all continents, which makes it one of the largest conferences in the World in the area of Information Systems, thus demonstrating the success and global dimension of this conference. From these, 42 papers were selected for publication and presentation at the Conference as full papers. These numbers, leading to a full-paper acceptance ratio of 16%, show the intention of preserving a high quality forum for this conference, a quality that we intend to maintain in the future, for the next editions of this conference.The high number and high quality of the received papers imposed difficult choices in the selection process. To evaluate each submission, a double blind paper review was performed by the Program Committee, whose members are highly qualified researchers in ICEIS topic areas.All presented papers will be available at the SCITEPRESS Digital Library and will be submitted for indexation by Thomson Reuters Conference Proceedings Citation Index (ISI), INSPEC, DBLP, EI (Elsevier Index) and Scopus.Additionally, a short list of presented papers will be selected to be expanded into a forthcoming book of ICEIS 2016 Selected Papers to be published by Springer in the LNBIP Series.The technical program of the conference included a panel and 4 invited talks delivered by internationally distinguished speakers, namely: Claudia Loebbecke (University of Cologne, Germany), Sergio Gusmeroli (TXT e-solutions SPA, Italy), Wil Van Der Aalst (Technische Universiteit Eindhoven, Netherlands) and Jan Vom Brocke (University of Liechtenstein, Liechtenstein). Their participation positively contributes to reinforce the overall quality of the Conference and to provide a deeper understanding of the fields addressed by the conference.Moreover, ICEIS 2016 had a Doctoral Consortium on Enterprise Information Systems and 1 tutorial. We are thankful to the Conference Co-chairs (Olivier Camp and José Cordeiro) and Program Co-chairs (Slimane Hammoudi, Leszek Maciaszek and Michele M. Missikoff) for their dedication and hard work in organizing these events.We sincerely thank all the authors for their submissions and participation in ICEIS 2016. Furthermore, we would like to thank all the members of the program committee and reviewers, who helped us with their expertise, dedication and time. We would also like to thank the invited speakers for their excellent contribution in sharing their knowledge and vision and the workshop/special session chairs whose collaboration with ICEIS 2015 was much appreciated. Finally, we gratefully acknowledge the professional support of the ICEIS 2016 team for all organizational processes. XIIIWe hope that all colleagues find this a fruitful and inspiring conference. We hope to contribute to the development of the Enterprise Information Systems community and look forward to having additional research results presented at the next edition of ICEIS, details of which are available at http://www.iceis.org. Slimane Hammoudi University of Cologne, GermanyAbstract:...

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Demystifying Big Data Analytics for Business Intelligence Through the Lens of Marketing Mix

Cited by 265 publications

References 27 publications

How signaling and search costs affect information asymmetry in P2P lending: the economics of big data

How signaling and search costs affect information asymmetry in P2P lending: the economics of big data

Customer Super-Profiling Demonstrator to Enable Efficient Targeting in Marketing Campaigns

An Evaluation of the Challenges of Multilingualism in Data Warehouse Development

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