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2019 | nr 35 | 18--39
Tytuł artykułu

Identification of Advanced Data Analysis in Marketing: A Systematic Literature Review

Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Aim/purpose - Marketing is an important area of activity for the vast majority of enterprises. Many of them try using marketing data analysis. Both the literature and the practice of many enterprises describe the use of advanced data analysis. However, interpretations of this concept differ. The aim of this paper is to identify the interpretation of advanced data analysis in marketing, in support of decision-making processes applied in the retail trading sector. Design/methodology/approach - The study was conducted using a systematic literature review, suggested by B. Kitchenham (2004), extended by C. Wohlin & R. Prikladniki (2013). This method was modified and expanded through the division of the whole study into two phases. Each phase is intended to facilitate obtaining answers to different important research questions. The first phase constitutes an exploratory study, whose results allow the detailed analysis of the literature in the second phase of the study. Findings - The results of this study of the relevant literature indicate that scholarly publications do not use the phrase 'advanced data analysis', and its context is described with the term 'data analysis'. Another term used broadly within the sphere of data analysis is 'big data'. The concept of 'data analysis' in marketing is focused around the term 'big data analytics' and terms linked to the word 'customer', such as 'customer-centric', 'customer engagement', 'customer experience', 'customer targeting service', and 'customers classification'. The study of the literature undertaken indicates that marketing employs data analysis in such areas as customer needs identification and market segmentation. Research implications/limitations - The study of the literature review was carried out using selected four databases containing publications, i.e. Web of Science, IEEE, Springer and ACM for the period 2008 to 2018. The research described in the article can be continued in two ways. First, by analysing the literature presented in this paper on advanced data analysis in marketing using the method called snowball sampling. Secondly, the results obtained from the first stage of the study can be used to conduct the study with other databases. Originality/value/contribution - The main contribution of this work is the proposal of modifying the systematic literature review method, which was expanded through the introduction of two phases. This division of two stages is important for conducting studies of literature when there are no clear, established definitions for the concepts being employed. The result of the study is also a set of ordered terms and their meanings that clearly define advanced data analysis in marketing.(original abstract)
Rocznik
Numer
Strony
18--39
Opis fizyczny
Twórcy
  • Wrocław University of Economics, Poland
  • Wrocław University of Economics, Poland
  • Wrocław University of Economics, Poland
Bibliografia
  • 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
  • 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/s12525-016-0219-0
  • Bucklin, R. E., & Sismeiro, C. (2009). Click here for Internet insight: Advances in clickstream data analysis in marketing. Journal of Interactive Marketing, 23(1), 35-48.
  • 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.2016.1144326
  • 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.2014.01.015
  • Chen, M., Mao, S., & Liu, Y. (2014). Big data: A survey. Mobile Networks and Applications, 19(2), 171-209. doi: 10.1007/s11036-013-0489-0
  • 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_16
  • 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.2015.02.006
  • Gordon, S. Linoff, M., & Berry, J.A. (2011). Data Mining Techniques: For marketing, sales, and customer relationship. New York: John Wiley & Sons.
  • 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.
  • Han, J., Pei, J., & Kamber, M. (2012). Data Mining: Concepts and techniques. Amsterdam: Elsevier.
  • 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.
  • Jannach, D., Zanker, M., Felfernig, A., & Friedrich, G. (2010). Recommender systems: An introduction. Cambridge: Cambridge University Press.
  • Kitchenham, B. (2004). Procedures for performing systematic reviews. Joint Technical Report, Keele: Keele University TR/SE-0401 and NICTA 0400011T.1, July.
  • Kitchenham, B., & Charters, C. (2007). Guidelines for performing systematic literature reviews in software engineering. Keele University and Durham University Joint Report-EBSE 2007-001.
  • 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.2008.09.009
  • 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.
  • 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/s10257-011-0185-1
  • 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.2013.12.001
  • 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/s13369-015-1586-0
  • 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.2014.079
  • 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: 10.1145/1559845.1559865
  • 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.2017.8324322
  • 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_17
  • 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/ 2017F442
  • 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
  • Ricci, F., Rokach, L., & Shapira, B. (2015). Recommender systems: Introduction and challenges. In Recommender systems handbook (pp. 1-34). Boston, MA: Springer, doi: 10.1007/978-0-387-85820-3
  • 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: 10.1177/088506660101600307
  • 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.2016.58.3.26
  • Setia, S., & Jyoti, D. (2013). Multi-level association rule mining: A review. International Journal of Computer Trends and Technology (IJCTT), 6(3), 166-170.
  • Sharda, R., Delen, D., & Turban, E. (2014). Business intelligence and analytics: Systems for decision support. Harlow: Pearson Educational.
  • 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.2014.2327238
  • 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.2008.09.009
  • 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
  • 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- 0-02071-8
  • 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
  • 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.
  • 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.
  • 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: 10.1080/23270012.2014.968643
Typ dokumentu
Bibliografia
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Identyfikator YADDA
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