Research trends on Big Data in Marketing: a text mining and topic modeling based literature analysis

• Alexandra Amado ^[1] ; Paulo Cortez ^[2] ; Paulo Rita ^[1] ; Sérgio Moro ^[1]
  1. [1] Instituto Universitário de Lisboa

     Instituto Universitário de Lisboa

     Socorro, Portugal

     
  2. [2] Universidade do Minho

     Universidade do Minho

     Braga (São José de São Lázaro), Portugal

     
• Localización: European Research on Management and Business Economics, ISSN 2444-8834, Vol. 24, Nº. 1, 2018, págs. 1-7
• Idioma: inglés
• DOI: 10.1016/j.iedeen.2017.06.002
• Enlaces
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• Dialnet Métricas: 8 Citas
• Resumen

  • Given the research interest on Big Data in Marketing, we present a research literature analysis based on a text mining semi-automated approach with the goal of identifying the main trends in this domain. In particular, the analysis focuses on relevant terms and topics related with five dimensions: Big Data, Marketing, Geographic location of authors’ affiliation (countries and continents), Products, and Sectors. A total of 1560 articles published from 2010 to 2015 were scrutinized. The findings revealed that research is bipartite between technological and research domains, with Big Data publications not clearly aligning cutting edge techniques toward Marketing benefits. Also, few inter-continental co-authored publications were found. Moreover, findings show that research in Big Data applications to Marketing is still in an embryonic stage, thus making it essential to develop more direct efforts toward business for Big Data to thrive in the Marketing arena.

• Referencias bibliográficas
  • Akter and Wamba, 2016 S. Akter, S.F. Wamba. Big data analytics in E-commerce: A systematic review and agenda for future research. Electronic...
  • Bendle and Wang, 2016 N.T. Bendle, X.S. Wang. Uncovering the message from the mess of big data. Business Horizons, 59 (1) (2016), pp. 115-124
  • Blei, 2012 D.M. Blei. Probabilistic topic models. Communications of the ACM, 55 (4) (2012), pp. 77-84
  • Bloomberg, 2015 Bloomberg. Stock markets and sectors in the US. Bloomberg (2015) Retrieved from: http://www.bloomberg.com/research/sectorandindustry/overview/sectorlanding.asp
  • Calheiros et al., 2017 A.C. Calheiros, S. Moro, P. Rita. Sentiment classification of consumer generated online reviews using topic modeling.Journal...
  • Chen and Zhang, 2014 C.P. Chen, C.Y. Zhang. Data-intensive applications, challenges, techniques and technologies: A survey on Big Data Information...
  • Chen et al., 2012 H. Chen, R.H. Chiang, V.C. Storey. Business intelligence and analytics: From big data to big impact MIS Quarterly, 36 (4)...
  • Delen and Crossland, 2008 D. Delen, M.D. Crossland. Seeding the survey and analysis of research literature with text mining Expert Systems...
  • Erevelles et al., 2016 S. Erevelles, N. Fukawa, L. Swayne. Big Data consumer analytics and the transformation of marketing Journal of Business...
  • Fan and Bifet, 2013 W. Fan, A. Bifet. Mining big data: Current status, and forecast to the future ACM sIGKDD Explorations Newsletter, 14...
  • Feinleib, 2012 D. Feinleib. Why this big data company may be the next omniture Forbes/Entrepreneurs (2012). Retrieved from: http://www.forbes.com/sites/davefeinleib/2012/10/31/why-this-company-may-be-the-next-omniture/
  • Google-Finance, 2015 Google-Finance. Google Finance website Google (2015). Retrieved from: https://www.google.com/finance
  • Grigsby, 2015 M. Grigsby. Marketing analytics: A practical guide to real marketing science Kogan Page Publishers (2015)
  • Grönroos, 1989 C. Grönroos. Defining marketing: A market-oriented approach European Journal of Marketing, 23 (1) (1989), pp. 52-60
  • Hazen et al., 2014 B.T. Hazen, C.A. Boone, J.D. Ezell, L.A. Jones-FarmerData quality for data science, predictive analytics, and big data...
  • Hornik and Grün, 2011 K. Hornik, B. Grün. topicmodels: An R package for fitting topic models. Journal of Statistical Software, 40 (13) (2011),...
  • Kao and Poteet, 2007 A. Kao, S.R. Poteet (Eds.), Natural language processing and text mining, Springer Science & Business Media (2007)
  • Lacoste, 2016 S. Lacoste. Perspectives on social media ant its use by key account managers. Industrial Marketing Management, 54 (2016), pp....
  • Laney, 2001 D. Laney. 3D data management: Controlling data volume, velocity and variety. META Group Research Note, 6 (2001), pp. 70-73
  • Lansley and Longley, 2016 G. Lansley, P. Longley. Deriving age and gender from forenames for consumer analytics.Journal of Retailing and...
  • Laurent, 2013 G. Laurent. EMAC distinguished marketing scholar 2012: Respect the data!. International Journal of Research in Marketing, 30...
  • Malthouse et al., 2013 E.C. Malthouse, M. Haenlein, B. Skiera, E. Wege, M. Zhang. Managing customer relationships in the social media era:...
  • Moro et al., 2014 S. Moro, P. Cortez, P. Rita. A data-driven approach to predict the success of bank telemarketing. Decision Support Systems,...
  • Moro et al., 2015a S. Moro, P. Cortez, P. Rita. Business intelligence in banking: A literature analysis from 2002 to 2013 using text mining...
  • Moro et al., 2015b S. Moro, P. Cortez, P. Rita. Using customer lifetime value and neural networks to improve the prediction of bank deposit...
  • Moro et al., 2016 S. Moro, P. Rita, B. Vala. Predicting social media performance metrics and evaluation of the impact on brand building: A...
  • Mysore et al., 2013 D. Mysore, S. Khupat, S. Jain. Big data architecture and patterns. Part 3: Understanding the architectural layers of a...
  • Ozkose et al., 2015 H. Ozkose, E.S. Ari, C. Gencer. Yesterday, today and tomorrow of big data. Procedia – Social and Behavioral Sciences,...
  • Robson and McCartan, 2016 C. Robson, K. McCartan. Real world research. John Wiley & Sons (2016)
  • Seret et al., 2014 A. Seret, S.K. vanden Broucke, B. Baesens, J. Vanthienen. A dynamic understanding of customer behavior processes based...
  • Sharda et al., 2015 R. Sharda, D. Delen, E. Turban. Business intelligence and analytics: Systems for decision support (10th ed.), Pearson...