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Identification of advanced data analysis in marketing: A systematic literature review


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Aguinis, H., Forcum, L. E., & Joo, H. (2013). Using market basket analysis in management research. Journal of Management, 39(7), 1799-1824. doi: 10.1177/014920 6312466147 Search in Google Scholar

Akter, S., & Wamba, S. F. (2016). Big data analytics in E-commerce: A systematic review and agenda for future research. Electronic Markets, 26(2), 173-194. doi: 10.1007/s12525-016-0219-010.1007/s12525-016-0219-0 Search in Google Scholar

Bucklin, R. E., & Sismeiro, C. (2009). Click here for Internet insight: Advances in click-stream data analysis in marketing. Journal of Interactive Marketing, 23(1), 35-48.10.1016/j.intmar.2008.10.004 Search in Google Scholar

Calder, B. J., Malthouse, E. C., & Maslowska, E. (2016). Brand marketing, big data and social innovation as future research directions for engagement. Journal of Marketing Management, 32(5-6), 579-585. doi: 10.1080/0267257X.2016.114432610.1080/0267257X.2016.1144326 Search in Google Scholar

Chen, C. P., & Zhang, C. Y. (2014). Data-intensive applications, challenges, techniques and technologies: A survey on Big data. Information Sciences, 275, 314-347. doi: 10.1016/j.ins.2014.01.01510.1016/j.ins.2014.01.015 Search in Google Scholar

Chen, M., Mao, S., & Liu, Y. (2014). Big data: A survey. Mobile Networks and Applications, 19(2), 171-209. doi: 10.1007/s11036-013-0489-010.1007/s11036-013-0489-0 Search in Google Scholar

Elgendy, N., & Elragal, A. (2014). Big data analytics: A literature review paper. Industrial Conference on Data Mining (pp. 214-227). Berlin: Springer-Cham, doi: 10.1007/978-3-319-08976-8_1610.1007/978-3-319-08976-8_16 Search in Google Scholar

Fan, S., Lau, R. Y., & Zhao, J. L. (2015). Demystifying big data analytics for Business Intelligence through the lens of marketing mix. Big Data Research, 2(1), 28-32. doi: 10.1016/j.bdr.2015.02.00610.1016/j.bdr.2015.02.006 Search in Google Scholar

Gordon, S. Linoff, M., & Berry, J.A. (2011). Data Mining Techniques: For marketing, sales, and customer relationship. New York: John Wiley & Sons. Search in Google Scholar

Grandhi, B., Patwa, N., & Saleem, K. (2017). Data-driven marketing for growth and profitability. 10th Annual Conference of the EuroMed Academy of Business, 675-694. Search in Google Scholar

Han, J., Pei, J., & Kamber, M. (2012). Data Mining: Concepts and techniques. Amsterdam: Elsevier. Search in Google Scholar

Hu, H., Wen, Y., Chua, T. S., & Li, X. (2014). Toward scalable systems for big data analytics: A technology tutorial. IEEE Access, 2, 652-687. doi: 10.1109/ACCESS. 2014.2332453. Search in Google Scholar

Jannach, D., Zanker, M., Felfernig, A., & Friedrich, G. (2010). Recommender systems: An introduction. Cambridge: Cambridge University Press.10.1017/CBO9780511763113 Search in Google Scholar

Kitchenham, B. (2004). Procedures for performing systematic reviews. Joint Technical Report, Keele: Keele University TR/SE-0401 and NICTA 0400011T.1, July. Search in Google Scholar

Kitchenham, B., & Charters, C. (2007). Guidelines for performing systematic literature reviews in software engineering. Keele University and Durham University Joint Report–EBSE 2007-001. Search in Google Scholar

Kitchenham, B., Pretorius, R., Budgen, D., Brereton, O. P., Turner, M., Niazi, M., & Linkman, S. (2010). Systematic literature reviews in software engineering – a tertiary study. Journal Information and Software Technology, 52, 792-805. doi: 10.1016/j.infsof.2008.09.00910.1016/j.infsof.2008.09.009 Search in Google Scholar

Konstan, J.A., & Adomavicius, G. (2013). Toward identification and adoption of best practices in algorithmic recommender systems research [in] Proceedings of the International Workshop on Reproducibility and Replication in Recommender Systems Evaluation (pp. 23-28). New York: ACM. Search in Google Scholar

Kridel, D., & Dolk, D. (2013). Automated self-service modelling: Predictive analytics as a service. Information Systems and e-Business Management, 11(1), 119-140. doi: 10.1007/s10257-011-0185-110.1007/s10257-011-0185-1 Search in Google Scholar

Leeflang, P. S., Verhoef, P. C., Dahlström, P., & Freundt, T. (2014). Challenges and solutions for marketing in a digital era. European Management Journal, 32(1), 1-12. doi: 10.1016/j.emj.2013.12.00110.1016/j.emj.2013.12.001 Search in Google Scholar

Niazi, M. (2015). Do systematic literature reviews outperform informal literature reviews in the software engineering domain? An initial case study. Arabian Journal for Science and Engineering, 40(3), 845-855. doi: 10.1007/s13369-015-1586-010.1007/s13369-015-1586-0 Search in Google Scholar

Pabedinskaitė, A., Davidavičienė, V., & Milišauskas, P. (2014). Big data driven e-commerce marketing. In Proceedings of 8th International Scientific Conference Business and Management-Spausdinta (pp. 645-654). Vilnus, Lietuva. doi: 10. 3846/bm.2014.07910.3846/bm.2014.079 Search in Google Scholar

Pavlo, A., Paulson, E., Rasin, A., Abadi, D. J., DeWitt, D. J., Madden, S., & Stonebraker, M. (2009). A comparison of approaches to large-scale data analysis. In Proceedings of the 2009 ACM SIGMOD International Conference on Management of data (pp. 165-178). Providence, RI: ACM. doi: 10.1145/1559845.155986510.1145/1559845.1559865 Search in Google Scholar

Pawełoszek, I., & Korczak, J. (2017). From data exploration to semantic model of customer. In Intelligent Systems Conference (IntelliSys) (pp. 382-388). London, 7-8 September. doi: 10.1109/IntelliSys.2017.832432210.1109/IntelliSys.2017.8324322 Search in Google Scholar

Pierański, B., & Strykowski, S. (2017). Towards a personalized virtual customer experience. In Asian Conference on Intelligent Information and Database Systems (pp. 185-195). Berlin: Springer. doi: 10.1007/978-3-319-56660-3_1710.1007/978-3-319-56660-3_17 Search in Google Scholar

Pondel, M., & Korczak, J. (2017). A view on the methodology of analysis and exploration of marketing data. In Proceedings of the 2017 Federated Conference on Computer Science and Information Systems. Annals of Computer Science and Information Systems (Vol. 9, pp. 1135-1143). Prague, September 3-6. doi: 10.15439/2017F44210.15439/2017F442 Search in Google Scholar

Quiñones, D., & Rusu, C. (2017). How to develop usability heuristics: A systematic literature review. Computer Standards & Interfaces, 53, 89-122. doi: 10.1016/j.csi. 2017.03.009 Search in Google Scholar

Ricci, F., Rokach, L., & Shapira, B. (2015). Recommender systems: Introduction and challenges. In Recommender systems handbook (pp. 1-34). Boston, MA: Springer, doi: 10.1007/978-0-387-85820-310.1007/978-0-387-85820-3 Search in Google Scholar

Sackett, D. L., Straus, S. E., Richardson, W. S., Rosenberg, W., & Haynes, R. B. (2000). Evidence-based medicine: How to practice and teach EBM. 2nd ed., Edinburgh: Churchill Livingstone. doi: 10.1177/08850666010160030710.1177/088506660101600307 Search in Google Scholar

Sanders, N. R. (2016). How to use big data to drive your supply chain. California Management Review, 58(3), 26-48.doi: 10.1525/cmr.2016.58.3.2610.1525/cmr.2016.58.3.26 Search in Google Scholar

Setia, S., & Jyoti, D. (2013). Multi-level association rule mining: A review. International Journal of Computer Trends and Technology (IJCTT), 6(3), 166-170. Search in Google Scholar

Sharda, R., Delen, D., & Turban, E. (2014). Business intelligence and analytics: Systems for decision support. Harlow: Pearson Educational. Search in Google Scholar

Slavakis, K., Giannakis, G. B., & Mateos, G. (2014). Modelling and optimization for big data analytics: (statistical) learning tools for our era of data deluge. IEEE Signal Processing Magazine, 31(5), 18-31. doi: 10.1109/MSP.2014.232723810.1109/MSP.2014.2327238 Search in Google Scholar

Vahid, G., & Mäntyläc, M. V. (2016). A systematic literature review of literature reviews in software testing. Journal Information and Software Technology, 80, 195-216. doi: 10.1016/j.infsof.2008.09.00910.1016/j.infsof.2008.09.009 Search in Google Scholar

Vera-Baquero, A., Colomo-Palacios, R., & Molloy, O. (2013). Business process analytics using a big data approach. IT Professional, 15(6), 29-35. doi: 10.1109/MITP. 2013.60 Search in Google Scholar

Witten, I., Frank, E., Hall, M., & Pal, C. (2017). Data mining: Practical machine learning tools and techniques. Burlington, MA: Morgan Kaufman. doi: 10.1016/C2015-0-02071-810.1016/C2015-0-02071-8 Search in Google Scholar

Wohlin, C., & Prikladniki, R. (2013). Systematic literature reviews in software engineering. Information and Software Technology, 55(6), 919-920. doi: 10.1016/j.infsof. 2008.09.009 Search in Google Scholar

Yeung, R., & Yee, W. (2015). Application of cluster analysis and discriminant analysis in market segmentation and prediction. In J. Mendy & S. G. Geringer (Eds.), Leading Issues in Business Research Methods (Vol. 2, pp. 63-79). Sonning Common, RG: ASPI. Search in Google Scholar

Zhao, D. (2013). Frontiers of big data business analytics: Patterns and cases in online marketing. In J. Liebowitz (Ed.), Big data and business analytics (pp. 43-67). Boston, MA: Auerbach Publications. Search in Google Scholar

Zhao, J. L., Fan, S., & Hu, D. (2014). Business challenges and research directions of management analytics in the big data era. Journal of Management Analytics, 1(3), 169-174. doi: 10.1080/23270012.2014.96864310.1080/23270012.2014.968643 Search in Google Scholar